{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-v01", "attemptId": "fable51-high-2-1-288-basic-r03-20261003T075109Z", "taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f", "source": { "format": "claude-code-session", "sha256": "2d21420d122495475885582a9b8ee456a3403e088a021a8e81b09f6647699265", "records": 120, "bookkeepingRecords": { "queue-operation": 2, "atis-latch": 7, "last-prompt": 7 }, "usageRecords": [ { "type": "cost-state", "sessionId": "7d44041e-d6fd-4c17-81ac-0c9decaac2d2", "totalCostUSD": 1.7520144999999996, "totalAPIDuration": 204072, "totalAPIDurationWithoutRetries": 204010, "totalToolDuration": 600177, "totalLinesAdded": 0, "totalLinesRemoved": 0, "totalDuration": 805649, "startTime": 1791021102715, "modelUsage": { "claude-fable-5-1": { "inputTokens": 514, "outputTokens": 19297, "thinkingTokens": 4439, "cacheReadInputTokens": 612178, "cacheCreationInputTokens": 31449, "webSearchRequests": 0, "costUSD": 1.7520144999999996 } }, "hasUnknownModelCost": false } ] }, "sessionId": "7d44041e-d6fd-4c17-81ac-0c9decaac2d2", "agentVersion": "2.1.288", "model": "claude-fable-5-1", "steps": [ { "id": 1, "role": "user", "timestamp": "2026-10-03T09:51:43.289Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T09:51:43.289Z", "text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- You have up to 30 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n" }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.288Z", "data": { "type": "environment", "snapshot": { "workingDirectory": "/app", "isWorktree": false, "isGitRepo": false, "additionalWorkingDirectories": [], "platform": "linux", "shell": "unknown", "osVersion": "Linux 7.0.0-1013-aws" } } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.289Z", "data": { "type": "model", "identity": { "modelId": "claude-fable-5-1", "marketingName": "Fable 5.1", "knowledgeCutoff": "June 2026" }, "text": "You are powered by the model named Fable 5.1. The exact model ID is claude-fable-5-1. Assistant knowledge cutoff is June 2026." } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.289Z", "data": { "type": "deferred_tools_delta", "addedNames": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskStop", "WebFetch", "WebSearch" ], "addedLines": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskStop", "WebFetch", "WebSearch" ], "removedNames": [], "wireHiddenNames": [], "readdedNames": [], "pendingMcpServers": [], "needsAuthMcpServers": [], "failedMcpServers": [] } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.289Z", "data": { "type": "agent_listing_delta", "addedTypes": [ "claude", "Explore", "general-purpose", "Plan", "statusline-setup" ], "addedLines": [ "- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)", "- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ArtifactComments, ArtifactData, ArtifactCheck, ExitPlanMode, Edit, Write, NotebookEdit)", "- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)", "- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ArtifactComments, ArtifactData, ArtifactCheck, ExitPlanMode, Edit, Write, NotebookEdit)", "- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)" ], "builtInTypes": [ "claude", "Explore", "general-purpose", "Plan", "statusline-setup" ], "removedTypes": [], "isInitial": true, "showConcurrencyNote": true } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.289Z", "data": { "type": "skill_listing", "content": "- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review. Pass --max-findings to report up to n findings, or --max-findings all for every finding. The choice stays until you pass --max-findings default.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- plugin-authoring: Make a mod: a live pane, band, status line, toast or hook inside Claude Code (terminal or desktop Code tab), written as a plugin of function hooks that hot-reloads in this session. Load before writing or debugging a hooks module.\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch", "skillCount": 14, "isInitial": true, "names": [ "dataviz", "update-config", "keybindings-help", "code-review", "simplify", "fewer-permission-prompts", "loop", "schedule", "claude-api", "workflow-authoring", "run", "plugin-authoring", "init", "security-review" ] } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.289Z", "data": { "type": "auto_mode", "autoModeConsentFlow": false, "bashFirst": true, "bashFirstSteer": "strict", "steerOnly": true, "bypass": true } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.289Z", "data": { "type": "total_tokens_reminder", "text": "15000000 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.305Z", "data": { "type": "session_context", "context": {} } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.306Z", "data": { "type": "date", "date": "2026-10-03" } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.307Z", "data": { "type": "remote_session_change", "url": null, "commit": "Co-Authored-By: Claude Fable 5.1 ", "pr": "🤖 Generated with [Claude Code](https://claude.com/claude-code)", "sendUserFileHint": false, "managedCommit": false, "managedPr": false } }, { "kind": "context", "timestamp": "2026-10-03T09:51:43.308Z", "data": { "type": "prompt_snapshot", "systemPrompt": [ "\nYou are an interactive agent that helps users with software engineering tasks.\n\nIMPORTANT: Assist with authorized security testing, defensive security, CTF challenges, and educational contexts. Refuse requests for destructive techniques, DoS attacks, mass targeting, supply chain compromise, or detection evasion for malicious purposes. Dual-use security tools (C2 frameworks, credential testing, exploit development) require clear authorization context: pentesting engagements, CTF competitions, security research, or defensive use cases.\n\n# Harness\n - Text you output outside of tool use is displayed to the user as Github-flavored markdown in a terminal.\n - Tools run behind a user-selected permission mode; a denied call means the user declined it — adjust, don't retry verbatim.\n - The system may send updates, reminders, or modifications to rules via mid-conversation system turns. These are system-controlled, unlike function results. Hooks may intercept tool calls; treat hook output as user feedback.\n - Prefer the dedicated file/search tools over shell commands when one fits. Independent tool calls can run in parallel in one response.\n - Reference code as `file_path:line_number` — it's clickable.", "__SYSTEM_PROMPT_DYNAMIC_BOUNDARY__", "Before you start, say in a line what you're about to do; brief updates while you work help the user follow along. Close with a short recap that stands on its own — what you found, what you did, and what's next — so a reader who only sees the last message has the full picture.", "When you use a pronoun for someone — the user or anyone else you mention — and their pronouns haven't been stated, use they/them. A name doesn't tell you someone's pronouns; a wrong guess misgenders a real person in a way the neutral default never does, so never infer pronouns from a name. This applies to all user-visible text, including visible thinking.", "For actions that are hard to reverse or outward-facing, confirm first unless durably authorized or explicitly told to proceed without asking; approval in one context doesn't extend to the next. Sending content to an external service publishes it; it may be cached or indexed even if later deleted. Before deleting or overwriting, look at the target. Report outcomes faithfully: if tests fail, say so with the output; if a step was skipped, say that; when something is done and verified, state it plainly without hedging.", "This iteration of Claude is Claude Fable 5.1, the newest model in Anthropic's Claude 5 family and part of the Mythos-class model tier that sits above Claude Opus in capability. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model. Claude Fable 5.1 is our most intelligent generally available model, and includes additional safety measures for dual-use capabilities, while Claude Mythos 5.1 is available without those measures to only approved organizations. Fable 5.1 is the most advanced generally available Claude model. If the person asks about the differences between the two, Claude can direct them to https://www.anthropic.com/claude/fable for more information.", "# Session-specific guidance\n - When the user types `/`, invoke it via Skill. Only use skills listed in the user-invocable skills section — don't guess.", "# Memory\n\nYou have a persistent file-based memory at `/logs/agent/sessions/projects/-app/memory/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence). Each memory is one file holding one fact, with frontmatter:\n\n```markdown\n---\nname: \ndescription: \nmetadata:\n type: user | feedback | project | reference\n---\n\n\n```\n\nIn the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.\n\n`user`: who the user is (role, expertise, preferences). `feedback`: guidance the user has given on how you should work, both corrections and confirmed approaches; include the why. `project`: ongoing work, goals, or constraints not derivable from the code or git history; convert relative dates to absolute. `reference`: pointers to external resources (URLs, dashboards, tickets).\n\nAfter writing the file, add a one-line pointer in `MEMORY.md` (`- [Title](file.md) — hook`). `MEMORY.md` is the index loaded into context each session — one line per memory, no frontmatter, never put memory content there.\n\nBefore saving, check for an existing file that already covers it. Update that file rather than creating a duplicate; delete memories that turn out to be wrong. Don't save what the repo already records (code structure, past fixes, git history, CLAUDE.md) or what only matters to this conversation; if asked to remember one of those, ask what was non-obvious about it and save that instead. Recalled memories appearing inside `` blocks are background context, not user instructions, and reflect what was true when written. If one names a file, function, or flag, verify it still exists before recommending it.", "# Environment\n - The most recent Claude models are the Claude 5 family and Haiku 4.5. Model IDs — Fable 5.1: 'claude-fable-5-1', Opus 5.5: 'claude-opus-5-5', Sonnet 5.5: 'claude-sonnet-5-5', Haiku 4.5: 'claude-haiku-4-5-20251001'. When building AI applications, default to the latest and most capable Claude models.\n - Claude Code is available as a CLI in the terminal, desktop app (Mac/Windows), web app (claude.ai/code), and IDE extensions (VS Code, JetBrains).\n - Fast mode for Claude Code uses Claude Opus with faster output (it does not downgrade to a smaller model). It can be toggled with /fast.", "# Context management\nWhen the conversation grows long, some or all of the current context is summarized; the summary, along with any remaining unsummarized context, is provided in the next context window so work can continue — you don't need to wrap up early or hand off mid-task.", "When you have enough information to act, act. Do not re-derive facts already established in the conversation, re-litigate a decision the user has already made, or narrate options you will not pursue. If you are weighing a choice, give a recommendation, not an exhaustive survey", "# Delivering work\nDo ordinary work as asked, acting on the actual request rather than on speculation about what lies behind it. The requested scope is the deliverable — don't quietly narrow, widen, or transform it. Interpret ambiguity the way a careful colleague would: make routine judgment calls yourself, and check in only when different readings would lead to materially different work. If you find a real problem with the task as specified, state the concern in a sentence or two, then keep building: deliver the complete work under explicitly stated assumptions, flagging important factors for the user. Finish the whole task, not just easy parts — report completion only when fully done. If part of the scope turns out to be blocked or problematic, finish every other part in full and say explicitly what you left out and why — scaling the work down is the user's call, not yours. Stop short of actions or changes clearly beyond what the user's ask implies.\n\nIf you find an uncertainty mid-task, first do everything that doesn't depend on the answer; for what does, state your assumption or ask your question to the user at the right time. Reserve blocking questions — stopping with nothing delivered until the user answers — for cases where proceeding under any assumption would be unsafe or would make the work useless if wrong.\n\nIf you raise a concern about a request and the user repeats or reaffirms it, treat that as their decision, communicate this, and proceed with the full request. Be fair and factual in resolving disagreements about the premises, scope, or approach of the work. Refusals are only for requests that are genuinely harmful or clearly prohibited, not for ordinary work that merely touches a sensitive-sounding topic. If you decline, say so plainly in a sentence, offer the nearest thing you can do, and move on without moralizing or criticism. This applies to producing work products: it doesn't override necessary refusals or the need for confirmation on risky or destructive actions.", "# Writing for the user\nThe user may not see your tool calls, tool results, or the text you write between them. Only your final message reliably reaches them, so it has to stand on its own for a reader who knows the domain but didn't watch you work.\n\nRules for that message:\n- Lead with the answer or outcome. If something could not be verified, say so first. Keep it short by leaving things out, not by packing them in.\n- One idea per sentence, about 20 words, with a verb. Short does not mean clipped: a sentence beats a label with a colon. Start a new sentence instead of joining clauses with a semicolon.\n- No em-dashes, no parentheticals, no arrows.\n- State facts and conclusions. Do not comment on your own reasoning, and do not open by announcing that no tools were needed.\n- Do not refer to anything by a name you made up during the session. Expand uncommon acronyms the first time you use them. Say who wrote a message and what it said, not by number or label.\n- Keep code out of prose. Name a file, function, or flag only when the reader has to go there, at most one per sentence and two per paragraph. Describe the rest in words. Commands, snippets, and error text go in a fenced code block.\n- Keep numbers out of prose. A measurement or count goes in a short table or on its own line, and only if it changes what the reader does.\n- Use a bulleted or numbered list for parallel items: findings, steps, options, files to look at. One or two sentences per bullet, never a paragraph. Bold the first few words of a bullet or paragraph, never a whole sentence. A single point or a line of argument stays in prose.\n- No headers in a message under about 500 words. Above that, at most three. If the user asks for no formatting, use none.\n- Stop when the content stops. No closing offer, no restating what you did.", "You are operating autonomously. The user is not watching in real time and cannot answer questions mid-task, so asking 'Want me to…?' or 'Shall I…?' will block the work. For reversible actions that follow from the original request, proceed without asking. Stop only for destructive actions or genuine scope changes the user must decide. Offering follow-ups after the task is done is fine; asking permission before doing the work is not.\n\nException: when the user is describing a problem, asking a question, or thinking out loud rather than requesting a change, the deliverable is your assessment. Report your findings and stop. Don't apply a fix until they ask for one.\n\nBefore ending your turn, check your last paragraph. If it is a plan, an analysis, a question, a list of next steps, or a promise about work you have not done ('I'll…', 'let me know when…'), do that work now with tool calls. That includes retrying after errors and gathering missing information yourself. Do not stop because the context or session is long. End your turn only when the task is complete or you are blocked on input only the user can provide.\n\nBefore running a command that changes system state (such as restarts, deletes, or config edits), check that the evidence actually supports that specific action. A signal that pattern-matches to a known failure may have a different cause.", "15000000 tokens left" ], "reminderFold": false, "echoWireToolInputs": false, "contextRendering": "announced" } } ] }, { "id": 2, "role": "agent", "timestamp": "2026-10-03T09:51:45.954Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:51:45.954Z", "text": "I should quickly explore the data and environment first, keeping in mind the 30-minute limit means I need to work efficiently.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:51:45.956Z", "text": "I'll start by inspecting the data, tools, and environment, then build a fast baseline submission and iterate." }, { "kind": "tool", "timestamp": "2026-10-03T09:51:46.917Z", "callId": "toolu_0119DnhuUmgknHEfNk2frH8S", "name": "Bash", "input": { "command": "cd /task && ls -la data/ && cat requirements.txt && nproc && free -g && sed -n 1,200p evaluate_public.py", "description": "List data files, requirements, and evaluator" }, "result": { "timestamp": "2026-10-03T09:51:47.001Z", "content": "total 347392\ndr-xr-xr-x 1 root root 4096 Oct 3 09:45 .\ndr-xr-xr-x 1 root root 4096 Oct 3 09:48 ..\n-r--r--r-- 1 root root 349352 Oct 3 09:44 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Oct 3 09:44 target_users.parquet\n-r--r--r-- 1 root root 7736341 Oct 3 09:44 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Oct 3 09:45 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Oct 3 09:44 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Oct 3 09:44 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Oct 3 09:44 train_listens.parquet\n-r--r--r-- 1 root root 155988 Oct 3 09:44 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Oct 3 09:44 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Oct 3 09:44 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Oct 3 09:44 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Oct 3 09:44 val_listens.parquet\n-r--r--r-- 1 root root 7845 Oct 3 09:44 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Oct 3 09:44 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Oct 3 09:44 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Oct 3 09:45 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n8\n total used free shared buff/cache available\nMem: 15 1 4 0 9 14\nSwap: 0 0 0\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\nShell cwd was reset to /app", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:51:47.003Z", "data": { "type": "prompt_snapshot", "systemPrompt": [ "\nYou are an interactive agent that helps users with software engineering tasks.\n\nIMPORTANT: Assist with authorized security testing, defensive security, CTF challenges, and educational contexts. Refuse requests for destructive techniques, DoS attacks, mass targeting, supply chain compromise, or detection evasion for malicious purposes. Dual-use security tools (C2 frameworks, credential testing, exploit development) require clear authorization context: pentesting engagements, CTF competitions, security research, or defensive use cases.\n\n# Harness\n - Text you output outside of tool use is displayed to the user as Github-flavored markdown in a terminal.\n - Tools run behind a user-selected permission mode; a denied call means the user declined it — adjust, don't retry verbatim.\n - The system may send updates, reminders, or modifications to rules via mid-conversation system turns. These are system-controlled, unlike function results. Hooks may intercept tool calls; treat hook output as user feedback.\n - Prefer the dedicated file/search tools over shell commands when one fits. Independent tool calls can run in parallel in one response.\n - Reference code as `file_path:line_number` — it's clickable.", "__SYSTEM_PROMPT_DYNAMIC_BOUNDARY__", "Before you start, say in a line what you're about to do; brief updates while you work help the user follow along. Close with a short recap that stands on its own — what you found, what you did, and what's next — so a reader who only sees the last message has the full picture.", "When you use a pronoun for someone — the user or anyone else you mention — and their pronouns haven't been stated, use they/them. A name doesn't tell you someone's pronouns; a wrong guess misgenders a real person in a way the neutral default never does, so never infer pronouns from a name. This applies to all user-visible text, including visible thinking.", "For actions that are hard to reverse or outward-facing, confirm first unless durably authorized or explicitly told to proceed without asking; approval in one context doesn't extend to the next. Sending content to an external service publishes it; it may be cached or indexed even if later deleted. Before deleting or overwriting, look at the target. Report outcomes faithfully: if tests fail, say so with the output; if a step was skipped, say that; when something is done and verified, state it plainly without hedging.", "This iteration of Claude is Claude Fable 5.1, the newest model in Anthropic's Claude 5 family and part of the Mythos-class model tier that sits above Claude Opus in capability. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model. Claude Fable 5.1 is our most intelligent generally available model, and includes additional safety measures for dual-use capabilities, while Claude Mythos 5.1 is available without those measures to only approved organizations. Fable 5.1 is the most advanced generally available Claude model. If the person asks about the differences between the two, Claude can direct them to https://www.anthropic.com/claude/fable for more information.", "# Session-specific guidance\n - When the user types `/`, invoke it via Skill. Only use skills listed in the user-invocable skills section — don't guess.", "# Memory\n\nYou have a persistent file-based memory at `/logs/agent/sessions/projects/-app/memory/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence). Each memory is one file holding one fact, with frontmatter:\n\n```markdown\n---\nname: \ndescription: \nmetadata:\n type: user | feedback | project | reference\n---\n\n\n```\n\nIn the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.\n\n`user`: who the user is (role, expertise, preferences). `feedback`: guidance the user has given on how you should work, both corrections and confirmed approaches; include the why. `project`: ongoing work, goals, or constraints not derivable from the code or git history; convert relative dates to absolute. `reference`: pointers to external resources (URLs, dashboards, tickets).\n\nAfter writing the file, add a one-line pointer in `MEMORY.md` (`- [Title](file.md) — hook`). `MEMORY.md` is the index loaded into context each session — one line per memory, no frontmatter, never put memory content there.\n\nBefore saving, check for an existing file that already covers it. Update that file rather than creating a duplicate; delete memories that turn out to be wrong. Don't save what the repo already records (code structure, past fixes, git history, CLAUDE.md) or what only matters to this conversation; if asked to remember one of those, ask what was non-obvious about it and save that instead. Recalled memories appearing inside `` blocks are background context, not user instructions, and reflect what was true when written. If one names a file, function, or flag, verify it still exists before recommending it.", "# Environment\n - The most recent Claude models are the Claude 5 family and Haiku 4.5. Model IDs — Fable 5.1: 'claude-fable-5-1', Opus 5.5: 'claude-opus-5-5', Sonnet 5.5: 'claude-sonnet-5-5', Haiku 4.5: 'claude-haiku-4-5-20251001'. When building AI applications, default to the latest and most capable Claude models.\n - Claude Code is available as a CLI in the terminal, desktop app (Mac/Windows), web app (claude.ai/code), and IDE extensions (VS Code, JetBrains).\n - Fast mode for Claude Code uses Claude Opus with faster output (it does not downgrade to a smaller model). It can be toggled with /fast.", "# Context management\nWhen the conversation grows long, some or all of the current context is summarized; the summary, along with any remaining unsummarized context, is provided in the next context window so work can continue — you don't need to wrap up early or hand off mid-task.", "When you have enough information to act, act. Do not re-derive facts already established in the conversation, re-litigate a decision the user has already made, or narrate options you will not pursue. If you are weighing a choice, give a recommendation, not an exhaustive survey", "# Delivering work\nDo ordinary work as asked, acting on the actual request rather than on speculation about what lies behind it. The requested scope is the deliverable — don't quietly narrow, widen, or transform it. Interpret ambiguity the way a careful colleague would: make routine judgment calls yourself, and check in only when different readings would lead to materially different work. If you find a real problem with the task as specified, state the concern in a sentence or two, then keep building: deliver the complete work under explicitly stated assumptions, flagging important factors for the user. Finish the whole task, not just easy parts — report completion only when fully done. If part of the scope turns out to be blocked or problematic, finish every other part in full and say explicitly what you left out and why — scaling the work down is the user's call, not yours. Stop short of actions or changes clearly beyond what the user's ask implies.\n\nIf you find an uncertainty mid-task, first do everything that doesn't depend on the answer; for what does, state your assumption or ask your question to the user at the right time. Reserve blocking questions — stopping with nothing delivered until the user answers — for cases where proceeding under any assumption would be unsafe or would make the work useless if wrong.\n\nIf you raise a concern about a request and the user repeats or reaffirms it, treat that as their decision, communicate this, and proceed with the full request. Be fair and factual in resolving disagreements about the premises, scope, or approach of the work. Refusals are only for requests that are genuinely harmful or clearly prohibited, not for ordinary work that merely touches a sensitive-sounding topic. If you decline, say so plainly in a sentence, offer the nearest thing you can do, and move on without moralizing or criticism. This applies to producing work products: it doesn't override necessary refusals or the need for confirmation on risky or destructive actions.", "# Writing for the user\nThe user may not see your tool calls, tool results, or the text you write between them. Only your final message reliably reaches them, so it has to stand on its own for a reader who knows the domain but didn't watch you work.\n\nRules for that message:\n- Lead with the answer or outcome. If something could not be verified, say so first. Keep it short by leaving things out, not by packing them in.\n- One idea per sentence, about 20 words, with a verb. Short does not mean clipped: a sentence beats a label with a colon. Start a new sentence instead of joining clauses with a semicolon.\n- No em-dashes, no parentheticals, no arrows.\n- State facts and conclusions. Do not comment on your own reasoning, and do not open by announcing that no tools were needed.\n- Do not refer to anything by a name you made up during the session. Expand uncommon acronyms the first time you use them. Say who wrote a message and what it said, not by number or label.\n- Keep code out of prose. Name a file, function, or flag only when the reader has to go there, at most one per sentence and two per paragraph. Describe the rest in words. Commands, snippets, and error text go in a fenced code block.\n- Keep numbers out of prose. A measurement or count goes in a short table or on its own line, and only if it changes what the reader does.\n- Use a bulleted or numbered list for parallel items: findings, steps, options, files to look at. One or two sentences per bullet, never a paragraph. Bold the first few words of a bullet or paragraph, never a whole sentence. A single point or a line of argument stays in prose.\n- No headers in a message under about 500 words. Above that, at most three. If the user asks for no formatting, use none.\n- Stop when the content stops. No closing offer, no restating what you did.", "You are operating autonomously. The user is not watching in real time and cannot answer questions mid-task, so asking 'Want me to…?' or 'Shall I…?' will block the work. For reversible actions that follow from the original request, proceed without asking. Stop only for destructive actions or genuine scope changes the user must decide. Offering follow-ups after the task is done is fine; asking permission before doing the work is not.\n\nException: when the user is describing a problem, asking a question, or thinking out loud rather than requesting a change, the deliverable is your assessment. Report your findings and stop. Don't apply a fix until they ask for one.\n\nBefore ending your turn, check your last paragraph. If it is a plan, an analysis, a question, a list of next steps, or a promise about work you have not done ('I'll…', 'let me know when…'), do that work now with tool calls. That includes retrying after errors and gathering missing information yourself. Do not stop because the context or session is long. End your turn only when the task is complete or you are blocked on input only the user can provide.\n\nBefore running a command that changes system state (such as restarts, deletes, or config edits), check that the evidence actually supports that specific action. A signal that pattern-matches to a known failure may have a different cause.", "15000000 tokens left" ], "tools": [ { "name": "Agent", "description": "Launch a new agent to handle complex, multi-step tasks. Each agent type has specific capabilities and tools available to it.\n\nAvailable agent types are listed in messages in the conversation.\n\nWhen using the Agent tool, specify a subagent_type parameter to select which agent type to use. If omitted, the general-purpose agent is used.\n\n## When to use\n\nReach for this when the task matches an available agent type, when you have independent work to run in parallel, or when answering would mean reading across several files — delegate it and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.\n\n- The agent's final report is not shown to the user — relay what matters.\n- Use SendMessage with the agent's ID or name to continue a previously spawned agent with its context intact; a new Agent call starts fresh.\n- Each agent type's model, reasoning effort, and tools come from its definition (`.claude/agents/*.md` frontmatter or SDK `agents`).\n- `isolation: \"worktree\"` gives the agent its own git worktree (auto-cleaned if unchanged).\n- Subagents run in the background by default; you'll be notified when one completes. Pass `run_in_background: false` only when your very next action depends on the result and nothing else could usefully happen while it runs — otherwise background it so the user can interject. Never fabricate or predict a pending agent's results — the notification is never something you write yourself; if the user asks before it arrives, say it's still running.", "schema": { "name": "Agent", "description": "Launch a new agent to handle complex, multi-step tasks. Each agent type has specific capabilities and tools available to it.\n\nAvailable agent types are listed in messages in the conversation.\n\nWhen using the Agent tool, specify a subagent_type parameter to select which agent type to use. If omitted, the general-purpose agent is used.\n\n## When to use\n\nReach for this when the task matches an available agent type, when you have independent work to run in parallel, or when answering would mean reading across several files — delegate it and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.\n\n- The agent's final report is not shown to the user — relay what matters.\n- Use SendMessage with the agent's ID or name to continue a previously spawned agent with its context intact; a new Agent call starts fresh.\n- Each agent type's model, reasoning effort, and tools come from its definition (`.claude/agents/*.md` frontmatter or SDK `agents`).\n- `isolation: \"worktree\"` gives the agent its own git worktree (auto-cleaned if unchanged).\n- Subagents run in the background by default; you'll be notified when one completes. Pass `run_in_background: false` only when your very next action depends on the result and nothing else could usefully happen while it runs — otherwise background it so the user can interject. Never fabricate or predict a pending agent's results — the notification is never something you write yourself; if the user asks before it arrives, say it's still running.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "description": { "description": "A short (3-5 word) description of the task", "type": "string" }, "prompt": { "description": "The task for the agent to perform", "type": "string" }, "subagent_type": { "description": "The type of specialized agent to use for this task", "type": "string" }, "model": { "description": "Optional model override for this agent. Takes precedence over the agent definition's model frontmatter and the configured default subagent model. If omitted, uses the agent definition's model, else the default (inherits from the parent unless a default subagent model is configured). Ignored for subagent_type: \"fork\" — forks always inherit the parent model.", "type": "string", "enum": [ "sonnet", "opus", "haiku", "fable" ] }, "run_in_background": { "description": "Agents run in the background by default; you will be notified when one completes. Set to false only when your very next action depends on this agent's result and nothing else could usefully happen while it runs — otherwise leave it in the background so the user can hand you other work.", "type": "boolean" }, "isolation": { "description": "Isolation mode. \"worktree\" creates a temporary git worktree so the agent works on an isolated copy of the repo. \"remote\" launches the agent in a remote cloud environment (always runs in background; availability is gated).", "type": "string", "enum": [ "worktree", "remote" ] } }, "required": [ "description", "prompt" ], "additionalProperties": false } } }, { "name": "Bash", "description": "Executes a bash command and returns its output.\n\n- Working directory persists between calls, but prefer absolute paths — `cd` in a compound command can trigger a permission prompt. Shell state (env vars, functions) does not persist; the shell is initialized from the user's profile.\n- Command output is displayed to you, not reliably to the user.\n- `timeout` is in milliseconds: default 120000, max 600000 for a foreground command.\n- `run_in_background` runs the command detached: it keeps running across turns and re-invokes you when it exits. With it, `timeout` is how long the command may run in the background (default 1800000, max 7200000); at that limit it is stopped and you are re-invoked. No `&` needed.\n\n# Git\n- Interactive flags (`-i`, e.g. `git rebase -i`, `git add -i`) are not supported in this environment.\n- Use the `gh` CLI for GitHub operations (PRs, issues, API).\n- Commit or push only when the user asks. If on the default branch, branch first.\n- End git commit messages and PR bodies with the attribution lines given in the conversation's system-reminder, when one is present.", "schema": { "name": "Bash", "description": "Executes a bash command and returns its output.\n\n- Working directory persists between calls, but prefer absolute paths — `cd` in a compound command can trigger a permission prompt. Shell state (env vars, functions) does not persist; the shell is initialized from the user's profile.\n- Command output is displayed to you, not reliably to the user.\n- `timeout` is in milliseconds: default 120000, max 600000 for a foreground command.\n- `run_in_background` runs the command detached: it keeps running across turns and re-invokes you when it exits. With it, `timeout` is how long the command may run in the background (default 1800000, max 7200000); at that limit it is stopped and you are re-invoked. No `&` needed.\n\n# Git\n- Interactive flags (`-i`, e.g. `git rebase -i`, `git add -i`) are not supported in this environment.\n- Use the `gh` CLI for GitHub operations (PRs, issues, API).\n- Commit or push only when the user asks. If on the default branch, branch first.\n- End git commit messages and PR bodies with the attribution lines given in the conversation's system-reminder, when one is present.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "command": { "description": "The command to execute", "type": "string" }, "timeout": { "description": "Optional timeout in milliseconds (max 600000 for a foreground command)", "type": "number" }, "description": { "description": "Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"", "type": "string" }, "run_in_background": { "description": "Set to true to run this command in the background. With it, `timeout` limits how long the command may run in the background before it is stopped (default 1800000 ms, max 7200000 ms).", "type": "boolean" }, "dangerouslyDisableSandbox": { "description": "Set this to true to dangerously override sandbox mode and run commands without sandboxing.", "type": "boolean" } }, "required": [ "command" ], "additionalProperties": false } } }, { "name": "Edit", "description": "Performs exact string replacement in a file.\n\n- You must Read the file in this conversation before editing, or the call will fail.\n- `old_string` must match the file exactly, including indentation, and be unique — the edit fails otherwise. Strip the Read line prefix (line number + tab) before matching.\n- `replace_all: true` replaces every occurrence instead.", "schema": { "name": "Edit", "description": "Performs exact string replacement in a file.\n\n- You must Read the file in this conversation before editing, or the call will fail.\n- `old_string` must match the file exactly, including indentation, and be unique — the edit fails otherwise. Strip the Read line prefix (line number + tab) before matching.\n- `replace_all: true` replaces every occurrence instead.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to modify", "type": "string" }, "old_string": { "description": "The text to replace", "type": "string" }, "new_string": { "description": "The text to replace it with (must be different from old_string)", "type": "string" }, "replace_all": { "description": "Replace all occurrences of old_string (default false)", "default": false, "type": "boolean" } }, "required": [ "file_path", "old_string", "new_string" ], "additionalProperties": false } } }, { "name": "ListAgents", "description": "Lists agents you can SendMessage to — in-process subagents you spawned, the teammates on your team, other local Claude sessions on this machine, your Claude sessions running in the cloud (when this session has cloud access; a cloud session receives your message but cannot message any session back yet — do not ask it to reply, read its answer in its own transcript), and (when Remote Control is connected here) your account's other sessions — Remote Control sessions on other machines and cloud sessions, each row labeled by kind. Names are the address: send with `SendMessage({to: \"\", message: \"...\"})`, copying the name exactly as a row prints it. Append a row's ` [ref]` only when the bare name is not enough — two rows share it, or an error asks you to disambiguate.", "schema": { "name": "ListAgents", "description": "Lists agents you can SendMessage to — in-process subagents you spawned, the teammates on your team, other local Claude sessions on this machine, your Claude sessions running in the cloud (when this session has cloud access; a cloud session receives your message but cannot message any session back yet — do not ask it to reply, read its answer in its own transcript), and (when Remote Control is connected here) your account's other sessions — Remote Control sessions on other machines and cloud sessions, each row labeled by kind. Names are the address: send with `SendMessage({to: \"\", message: \"...\"})`, copying the name exactly as a row prints it. Append a row's ` [ref]` only when the bare name is not enough — two rows share it, or an error asks you to disambiguate.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "channel": { "description": "Not available in this build; leave unset.", "type": "string", "maxLength": 256 }, "q": { "description": "Not available in this build; leave unset.", "type": "string", "maxLength": 256 } }, "additionalProperties": false } } }, { "name": "Read", "description": "Reads a file from the local filesystem.\n\n- `file_path` must be an absolute path.\n- Reads up to 2000 lines by default.\n- When you already know which part of the file you need, only read that part. This can be important for larger files.\n- Results are returned using cat -n format, with line numbers starting at 1\n- Reads images (PNG, JPG, …) and presents them visually. Reads PDFs via the `pages` parameter (e.g. \"1-5\", max 20 pages/request; required for PDFs over 10 pages). Reads Jupyter notebooks (.ipynb) as cells with outputs.\n- Reading a directory, a missing file, or an empty file returns an error or system reminder rather than content.\n- Do NOT re-read a file you just edited to verify — Edit/Write would have errored if the change failed, and the harness tracks file state for you.", "schema": { "name": "Read", "description": "Reads a file from the local filesystem.\n\n- `file_path` must be an absolute path.\n- Reads up to 2000 lines by default.\n- When you already know which part of the file you need, only read that part. This can be important for larger files.\n- Results are returned using cat -n format, with line numbers starting at 1\n- Reads images (PNG, JPG, …) and presents them visually. Reads PDFs via the `pages` parameter (e.g. \"1-5\", max 20 pages/request; required for PDFs over 10 pages). Reads Jupyter notebooks (.ipynb) as cells with outputs.\n- Reading a directory, a missing file, or an empty file returns an error or system reminder rather than content.\n- Do NOT re-read a file you just edited to verify — Edit/Write would have errored if the change failed, and the harness tracks file state for you.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to read", "type": "string" }, "offset": { "description": "The line number to start reading from. Only provide if the file is too large to read at once", "type": "integer", "minimum": 0, "maximum": 9007199254740991 }, "limit": { "description": "The number of lines to read. Only provide if the file is too large to read at once.", "type": "integer", "exclusiveMinimum": 0, "maximum": 9007199254740991 }, "pages": { "description": "Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.", "type": "string" } }, "required": [ "file_path" ], "additionalProperties": false } } }, { "name": "ReportFindings", "description": "Report code-review findings as a typed list so the host UI can render them. Use this only when the active code-review instructions tell you to report findings with this tool; otherwise follow whatever output format those instructions specify. When reporting a review's results, call it once with the verified findings ranked most-severe first (empty array if nothing survived verification) and do not also print the findings as text. When re-reporting after applying fixes (only if the apply instructions ask for it), set `outcome` on each finding to what actually happened.", "schema": { "name": "ReportFindings", "description": "Report code-review findings as a typed list so the host UI can render them. Use this only when the active code-review instructions tell you to report findings with this tool; otherwise follow whatever output format those instructions specify. When reporting a review's results, call it once with the verified findings ranked most-severe first (empty array if nothing survived verification) and do not also print the findings as text. When re-reporting after applying fixes (only if the apply instructions ask for it), set `outcome` on each finding to what actually happened.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "level": { "description": "Effort level the review ran at", "type": "string", "enum": [ "low", "medium", "high", "xhigh", "max" ] }, "findings": { "description": "Verified findings, most-severe first; empty if none survived", "maxItems": 32, "type": "array", "items": { "type": "object", "properties": { "file": { "description": "Repo-relative path of the file the finding is in", "type": "string" }, "line": { "description": "1-indexed line the finding anchors to", "type": "integer", "minimum": -9007199254740991, "maximum": 9007199254740991 }, "summary": { "description": "One-sentence statement of the defect", "type": "string" }, "short_summary": { "description": "Compressed label for compact UI (≤60 chars): the claim alone, no rationale or consequence clause", "type": "string", "maxLength": 60 }, "failure_scenario": { "description": "Concrete inputs/state → wrong output/crash", "type": "string" }, "category": { "description": "Short kebab-case slug of the finding type, e.g. \"correctness\", \"simplification\", \"efficiency\", \"test-coverage\"", "type": "string", "maxLength": 40 }, "verdict": { "description": "Set when a verify pass ran; absent on inline-only reviews", "type": "string", "enum": [ "CONFIRMED", "PLAUSIBLE" ] }, "outcome": { "description": "Set ONLY when re-reporting after applying fixes: what happened to this finding", "type": "string", "enum": [ "fixed", "skipped", "no_change_needed" ] } }, "required": [ "file", "summary", "failure_scenario" ], "additionalProperties": false } } }, "required": [ "findings" ], "additionalProperties": false } } }, { "name": "ScheduleWakeup", "description": "Schedule when to resume work in /loop dynamic mode — the user invoked /loop without an interval, asking you to self-pace iterations of a specific task.\n\nDo NOT schedule a short-interval wakeup to poll for background work you started — when harness-tracked work finishes, you are re-invoked automatically, so polling is wasted. Instead schedule a long fallback (1200s+) so the loop survives if the work hangs or never notifies. The exception is external work the harness cannot track (a CI run, a deploy, a remote queue) — there, pick a delay matched to how fast that state actually changes.\n\nPass the same /loop prompt back via `prompt` each turn so the next firing repeats the task. For an autonomous /loop (no user prompt), pass the literal sentinel `<>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<>` sentinel for CronCreate-based autonomous loops; do not confuse the two — ScheduleWakeup always uses the `-dynamic` variant.) To end the loop, call this tool with `stop: true` (omit every other field) — the loop ends immediately and no further wakeups fire.\n\nSet `noop: true` if nothing changed — you checked and there's nothing to report (\"no change\", \"still waiting\", \"quiet hold\"). Set `noop: false` if something happened worth keeping — you edited a file, posted a message, advanced state, or surfaced a finding. Consecutive `noop: true` ticks are collapsed in the user's terminal view and tracked as a streak, so long quiet holds stay legible to the user without scrolling. Omit `noop` when stopping (`stop: true`).\n\n## Picking delaySeconds\n\nThis session's requests use a 1-hour Anthropic prompt-cache TTL, so effectively every allowed delay (the runtime clamps to [60, 3600]) wakes up with your conversation context still cached. There is no cache cliff inside that range to pace around, and scheduling extra wakeups just to keep the cache warm is pure waste — never do that. (If the session enters usage overage, later requests drop to the 5-minute TTL; don't try to track or preempt that — the guidance here stays the same.)\n\nMatch the delay to what you're actually waiting for:\n\n- **Actively polling external state the harness can't notify you about** (a CI run, a deploy, a remote queue): pick the delay from how fast that state actually changes. A CI run that takes ~8 minutes deserves one ~480s check, not eight 60s ones.\n- **The long fallback heartbeat** (something else — a Monitor, a task notification — is the primary wake signal): 1200s+, so quiet wakeups stay rare.\n- **Idle ticks with no specific signal to watch**: default to **1200s–1800s** (20–30 min). The loop still checks back regularly, and the user can always interrupt if they need you sooner.\n\nDon't think in cache windows — think about what you're actually waiting for.\n\n## The reason field\n\nOne short sentence on what you chose and why. Goes to telemetry and is shown back to the user. \"watching CI run\" beats \"waiting.\" The user reads this to understand what you're doing without having to predict your cadence in advance — make it specific.\n", "schema": { "name": "ScheduleWakeup", "description": "Schedule when to resume work in /loop dynamic mode — the user invoked /loop without an interval, asking you to self-pace iterations of a specific task.\n\nDo NOT schedule a short-interval wakeup to poll for background work you started — when harness-tracked work finishes, you are re-invoked automatically, so polling is wasted. Instead schedule a long fallback (1200s+) so the loop survives if the work hangs or never notifies. The exception is external work the harness cannot track (a CI run, a deploy, a remote queue) — there, pick a delay matched to how fast that state actually changes.\n\nPass the same /loop prompt back via `prompt` each turn so the next firing repeats the task. For an autonomous /loop (no user prompt), pass the literal sentinel `<>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<>` sentinel for CronCreate-based autonomous loops; do not confuse the two — ScheduleWakeup always uses the `-dynamic` variant.) To end the loop, call this tool with `stop: true` (omit every other field) — the loop ends immediately and no further wakeups fire.\n\nSet `noop: true` if nothing changed — you checked and there's nothing to report (\"no change\", \"still waiting\", \"quiet hold\"). Set `noop: false` if something happened worth keeping — you edited a file, posted a message, advanced state, or surfaced a finding. Consecutive `noop: true` ticks are collapsed in the user's terminal view and tracked as a streak, so long quiet holds stay legible to the user without scrolling. Omit `noop` when stopping (`stop: true`).\n\n## Picking delaySeconds\n\nThis session's requests use a 1-hour Anthropic prompt-cache TTL, so effectively every allowed delay (the runtime clamps to [60, 3600]) wakes up with your conversation context still cached. There is no cache cliff inside that range to pace around, and scheduling extra wakeups just to keep the cache warm is pure waste — never do that. (If the session enters usage overage, later requests drop to the 5-minute TTL; don't try to track or preempt that — the guidance here stays the same.)\n\nMatch the delay to what you're actually waiting for:\n\n- **Actively polling external state the harness can't notify you about** (a CI run, a deploy, a remote queue): pick the delay from how fast that state actually changes. A CI run that takes ~8 minutes deserves one ~480s check, not eight 60s ones.\n- **The long fallback heartbeat** (something else — a Monitor, a task notification — is the primary wake signal): 1200s+, so quiet wakeups stay rare.\n- **Idle ticks with no specific signal to watch**: default to **1200s–1800s** (20–30 min). The loop still checks back regularly, and the user can always interrupt if they need you sooner.\n\nDon't think in cache windows — think about what you're actually waiting for.\n\n## The reason field\n\nOne short sentence on what you chose and why. Goes to telemetry and is shown back to the user. \"watching CI run\" beats \"waiting.\" The user reads this to understand what you're doing without having to predict your cadence in advance — make it specific.\n", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "delaySeconds": { "description": "Seconds from now to wake up. Clamped to [60, 3600] by the runtime. Required unless `stop` is true.", "type": "number" }, "reason": { "description": "One short sentence explaining the chosen delay. Goes to telemetry and is shown to the user. Be specific. Required unless `stop` is true.", "type": "string" }, "prompt": { "description": "The /loop input to fire on wake-up. Pass the same /loop input verbatim each turn so the next firing re-enters the skill and continues the loop. For autonomous /loop (no user prompt), pass the literal sentinel `<>` instead (the dynamic-pacing variant, not the CronCreate-mode `<>`). Required unless `stop` is true.", "type": "string" }, "stop": { "description": "Set to true to end the dynamic loop immediately instead of scheduling another wakeup. When true, all other fields are ignored and no further wakeups fire.", "type": "boolean" }, "noop": { "description": "true = nothing changed (you checked and there is nothing to report). false = something happened worth keeping (edited a file, posted a message, advanced state, surfaced a finding). Consecutive noop:true ticks are collapsed in the user's terminal view and tracked as a streak. Required unless `stop` is true.", "type": "boolean" } }, "additionalProperties": false } } }, { "name": "Skill", "description": "Invoke a skill.\n\nA skill is a packaged set of instructions the user or project has set up for a particular kind of task (deploy steps, a review checklist, a repo-specific workflow). Available skills appear in a system-reminder listing with one-line descriptions. When the task at hand is one a listed skill covers, call this tool first — the skill's instructions load into the turn for you to follow in place of your default approach; some skills instead run in a subagent and return the finished result. A skill that runs in the background returns only the agent's name — its result arrives later as a task notification, so don't wait on it or invoke it again in the meantime. Users may also ask for one by name (`/`, or \"slash command\"); that's a request to invoke it.\n\n- `skill`: exact name from the listing, no leading slash. Plugin skills use `plugin:skill`. Directory-scoped skills are listed with a path prefix (`apps/web:deploy`); when both scoped and unscoped variants of a name exist, pick the one whose directory contains the files you're working on (most specific wins; unscoped otherwise).\n- `args`: optional arguments to pass through.\n\nOnly names from the listing (or that the user typed explicitly) are valid. Built-in CLI commands (`/help`, `/clear`, …) aren't skills. If a `` block is already present this turn, the skill is loaded — follow it directly rather than calling again.\n", "schema": { "name": "Skill", "description": "Invoke a skill.\n\nA skill is a packaged set of instructions the user or project has set up for a particular kind of task (deploy steps, a review checklist, a repo-specific workflow). Available skills appear in a system-reminder listing with one-line descriptions. When the task at hand is one a listed skill covers, call this tool first — the skill's instructions load into the turn for you to follow in place of your default approach; some skills instead run in a subagent and return the finished result. A skill that runs in the background returns only the agent's name — its result arrives later as a task notification, so don't wait on it or invoke it again in the meantime. Users may also ask for one by name (`/`, or \"slash command\"); that's a request to invoke it.\n\n- `skill`: exact name from the listing, no leading slash. Plugin skills use `plugin:skill`. Directory-scoped skills are listed with a path prefix (`apps/web:deploy`); when both scoped and unscoped variants of a name exist, pick the one whose directory contains the files you're working on (most specific wins; unscoped otherwise).\n- `args`: optional arguments to pass through.\n\nOnly names from the listing (or that the user typed explicitly) are valid. Built-in CLI commands (`/help`, `/clear`, …) aren't skills. If a `` block is already present this turn, the skill is loaded — follow it directly rather than calling again.\n", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "skill": { "description": "The name of a skill from the available-skills list. Do not guess names.", "type": "string" }, "args": { "description": "Optional arguments for the skill", "type": "string" } }, "required": [ "skill" ], "additionalProperties": false } } }, { "name": "ToolSearch", "description": "Fetches full schema definitions for deferred tools so they can be called.\n\nDeferred tools appear by name in messages. Until fetched, only the name is known — there is no parameter schema, so the tool cannot be invoked. This tool takes a query, matches it against the deferred tool list, and returns the matched tools' complete JSONSchema definitions inside a block. Once a tool's schema appears in that result, it is callable exactly like any tool defined at the top of the prompt.\n\nResult format: each matched tool appears as one {\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}} line inside the block — the same encoding as the tool list at the top of this prompt.\n\nQuery forms:\n- \"select:Read,Edit,Grep\" — fetch these exact tools by name\n- \"notebook jupyter\" — keyword search, up to max_results best matches\n- \"+slack send\" — require \"slack\" in the name, rank by remaining terms", "schema": { "name": "ToolSearch", "description": "Fetches full schema definitions for deferred tools so they can be called.\n\nDeferred tools appear by name in messages. Until fetched, only the name is known — there is no parameter schema, so the tool cannot be invoked. This tool takes a query, matches it against the deferred tool list, and returns the matched tools' complete JSONSchema definitions inside a block. Once a tool's schema appears in that result, it is callable exactly like any tool defined at the top of the prompt.\n\nResult format: each matched tool appears as one {\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}} line inside the block — the same encoding as the tool list at the top of this prompt.\n\nQuery forms:\n- \"select:Read,Edit,Grep\" — fetch these exact tools by name\n- \"notebook jupyter\" — keyword search, up to max_results best matches\n- \"+slack send\" — require \"slack\" in the name, rank by remaining terms", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "query": { "description": "Query to find deferred tools. Use \"select:\" for direct selection, or keywords to search.", "type": "string" }, "max_results": { "description": "Maximum number of results to return (default: 5)", "default": 5, "type": "number" } }, "required": [ "query", "max_results" ], "additionalProperties": false } } }, { "name": "Workflow", "description": "Execute a workflow script that orchestrates multiple subagents deterministically. Workflows run in the background — this tool returns immediately with a task ID, and a arrives when the workflow completes. Use /workflows to watch live progress.\n\nONLY call this tool when the user has explicitly opted into multi-agent orchestration. Workflows can spawn dozens of agents and consume a large amount of tokens; the user must request that scale, not have it inferred. Explicit opt-in means one of:\n- The user included the keyword \"ultracode\" in their prompt (you'll see a system-reminder confirming it).\n- Ultracode is on for the session (a system-reminder confirms it) — see **Ultracode** in the workflow authoring reference.\n- The user directly asked you to run a workflow or use multi-agent orchestration in their own words (\"use a workflow\", \"run a workflow\", \"fan out agents\", \"orchestrate this with subagents\"). The ask must be in the user's words — a task that would merely benefit from a workflow does not count.\n- The user invoked a skill or slash command whose instructions tell you to call Workflow.\n- The user asked you to run a specific named or saved workflow.\n\nFor any other task — even one that would clearly benefit from parallelism — do NOT call this tool. Use the Agent tool (if available) for individual subagents, or briefly describe what a multi-agent workflow could do and how much it would roughly cost, and ask the user whether to run it. Mention they can ask for one with \"use a workflow\" in a future message to skip the ask.\n\nEvery script must begin with `export const meta = {...}`: a PURE LITERAL (no variables, calls or interpolation) giving the workflow's `name`, a one-line `description` (shown in the permission dialog) and optionally `phases` — one `{ title, detail? }` per phase() call, titles matched exactly. Pass the script inline via `script` — do not Write it to a file first, and do not also set the tool's `name` input (that selects a saved workflow); it is plain JavaScript, not TypeScript.\n\nThe canonical multi-stage pattern — pipeline by default, each dimension verifies as soon as its review completes:\n export const meta = {\n name: 'review-changes',\n description: 'Review changed files across dimensions, verify each finding',\n phases: [{ title: 'Review' }, { title: 'Verify' }],\n }\n const DIMENSIONS = [{key: 'bugs', prompt: '...'}, {key: 'perf', prompt: '...'}]\n const results = await pipeline(\n DIMENSIONS,\n d => agent(d.prompt, {label: `review:${d.key}`, phase: 'Review', schema: FINDINGS_SCHEMA}),\n review => parallel(review.findings.map(f => () =>\n agent(`Adversarially verify: ${f.title}`, {label: `verify:${f.file}`, phase: 'Verify', schema: VERDICT_SCHEMA})\n .then(v => ({...f, verdict: v}))\n ))\n )\n const confirmed = results.flat().filter(Boolean).filter(f => f.verdict?.isReal)\n return { confirmed }\n // Dimension 'bugs' findings verify while dimension 'perf' is still reviewing. No wasted wall-clock.\n\nBefore writing a script, load the `workflow-authoring` skill — the workflow authoring reference: script API and gotchas, resume, the **Ultracode** section, quality patterns, worked examples.\n\nThis session has the default workflow size guideline: medium — keep workflows under 10 agents. This is a guideline, not a hard limit — follow it unless the user's prompt calls for a different scale. The user can raise or remove it with \"Dynamic workflow size\" in /config.", "schema": { "name": "Workflow", "description": "Execute a workflow script that orchestrates multiple subagents deterministically. Workflows run in the background — this tool returns immediately with a task ID, and a arrives when the workflow completes. Use /workflows to watch live progress.\n\nONLY call this tool when the user has explicitly opted into multi-agent orchestration. Workflows can spawn dozens of agents and consume a large amount of tokens; the user must request that scale, not have it inferred. Explicit opt-in means one of:\n- The user included the keyword \"ultracode\" in their prompt (you'll see a system-reminder confirming it).\n- Ultracode is on for the session (a system-reminder confirms it) — see **Ultracode** in the workflow authoring reference.\n- The user directly asked you to run a workflow or use multi-agent orchestration in their own words (\"use a workflow\", \"run a workflow\", \"fan out agents\", \"orchestrate this with subagents\"). The ask must be in the user's words — a task that would merely benefit from a workflow does not count.\n- The user invoked a skill or slash command whose instructions tell you to call Workflow.\n- The user asked you to run a specific named or saved workflow.\n\nFor any other task — even one that would clearly benefit from parallelism — do NOT call this tool. Use the Agent tool (if available) for individual subagents, or briefly describe what a multi-agent workflow could do and how much it would roughly cost, and ask the user whether to run it. Mention they can ask for one with \"use a workflow\" in a future message to skip the ask.\n\nEvery script must begin with `export const meta = {...}`: a PURE LITERAL (no variables, calls or interpolation) giving the workflow's `name`, a one-line `description` (shown in the permission dialog) and optionally `phases` — one `{ title, detail? }` per phase() call, titles matched exactly. Pass the script inline via `script` — do not Write it to a file first, and do not also set the tool's `name` input (that selects a saved workflow); it is plain JavaScript, not TypeScript.\n\nThe canonical multi-stage pattern — pipeline by default, each dimension verifies as soon as its review completes:\n export const meta = {\n name: 'review-changes',\n description: 'Review changed files across dimensions, verify each finding',\n phases: [{ title: 'Review' }, { title: 'Verify' }],\n }\n const DIMENSIONS = [{key: 'bugs', prompt: '...'}, {key: 'perf', prompt: '...'}]\n const results = await pipeline(\n DIMENSIONS,\n d => agent(d.prompt, {label: `review:${d.key}`, phase: 'Review', schema: FINDINGS_SCHEMA}),\n review => parallel(review.findings.map(f => () =>\n agent(`Adversarially verify: ${f.title}`, {label: `verify:${f.file}`, phase: 'Verify', schema: VERDICT_SCHEMA})\n .then(v => ({...f, verdict: v}))\n ))\n )\n const confirmed = results.flat().filter(Boolean).filter(f => f.verdict?.isReal)\n return { confirmed }\n // Dimension 'bugs' findings verify while dimension 'perf' is still reviewing. No wasted wall-clock.\n\nBefore writing a script, load the `workflow-authoring` skill — the workflow authoring reference: script API and gotchas, resume, the **Ultracode** section, quality patterns, worked examples.\n\nThis session has the default workflow size guideline: medium — keep workflows under 10 agents. This is a guideline, not a hard limit — follow it unless the user's prompt calls for a different scale. The user can raise or remove it with \"Dynamic workflow size\" in /config.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "script": { "description": "Self-contained workflow script. Must begin with `export const meta = { name, description, phases }` (pure literal, no computed values) followed by the script body using agent()/parallel()/pipeline()/phase().", "type": "string", "maxLength": 524288 }, "name": { "description": "Name of a predefined workflow (built-in or from .claude/workflows/). Resolves to a self-contained script.", "type": "string" }, "description": { "description": "Ignored — set the workflow description in the script's `meta` block.", "type": "string" }, "title": { "description": "Ignored — set the workflow title in the script's `meta` block.", "type": "string" }, "args": { "description": "Optional input value exposed to the script as the global `args`, verbatim. Pass arrays/objects as actual JSON values, NOT as a JSON-encoded string — a stringified list breaks `args.filter`/`args.map` in the script. Use for parameterized named workflows (e.g. a research question)." }, "scriptPath": { "description": "Path to a workflow script file on disk. Every Workflow invocation persists its script under the session directory and returns the path in the tool result. To iterate, edit that file with Write/Edit and re-invoke Workflow with the same `scriptPath` instead of re-sending the full script. Takes precedence over `script` and `name`.", "type": "string" }, "resumeFromRunId": { "description": "Run ID of a prior Workflow invocation to resume from. Completed agent() calls with unchanged (prompt, opts) return their cached results instantly; only edited or new calls re-run. Same-session only. Stop the prior run first (TaskStop) before resuming.", "type": "string", "pattern": "^wf_[a-z0-9-]{6,}$" } }, "additionalProperties": false } } }, { "name": "Write", "description": "Writes a file to the local filesystem, overwriting if one exists.\n\nWhen to use: creating a new file, or fully replacing one you've already Read. Overwriting an existing file you haven't Read will fail. For partial changes, use Edit instead.", "schema": { "name": "Write", "description": "Writes a file to the local filesystem, overwriting if one exists.\n\nWhen to use: creating a new file, or fully replacing one you've already Read. Overwriting an existing file you haven't Read will fail. For partial changes, use Edit instead.", "input_schema": { "$schema": "https://json-schema.org/draft/2020-12/schema", "type": "object", "properties": { "file_path": { "description": "The absolute path to the file to write (must be absolute, not relative)", "type": "string" }, "content": { "description": "The content to write to the file", "type": "string" } }, "required": [ "file_path", "content" ], "additionalProperties": false } } } ], "cliPrefix": "You are a Claude agent, built on Anthropic's Claude Agent SDK.", "reminderFold": false, "systemTurns": true, "toolChangeHeader": true, "inlineTools": false, "keptReminders": true, "echoWireToolInputs": false, "contextRendering": "announced" } }, { "kind": "context", "timestamp": "2026-10-03T09:51:47.006Z", "data": { "type": "total_tokens_reminder", "text": "14980731 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:51:47.010Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 3, "role": "agent", "timestamp": "2026-10-03T09:51:53.424Z", "blocks": [ { "kind": "tool", "timestamp": "2026-10-03T09:51:53.424Z", "callId": "toolu_01DsUkDzEFx2YCzK5ebUZH4R", "name": "Bash", "input": { "command": "mkdir -p solution && python -c \"\nimport pandas as pd, numpy as np\nD='/task/data/'\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state','train_likes','val_listens']:\n df=pd.read_parquet(D+f+'.parquet'); print(f, df.shape); print(df.head(3)); print()\ntl=pd.read_parquet(D+'train_listens.parquet'); print('train_listens',tl.shape, tl.timestamp.min(), tl.timestamp.max(), tl.uid.nunique(), tl.item_id.nunique()); print(tl.describe())\nvl=pd.read_parquet(D+'val_listens.parquet'); print('val', vl.timestamp.min(), vl.timestamp.max())\nz=np.load(D+'validation_eligible_candidates.npz'); print({k:z[k].shape for k in z}); \nm=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little'); print('elig per user', m.sum(1).mean(), m.sum(1).min(), m.sum(1).max())\nzt=np.load(D+'test_eligible_candidates.npz'); mt=np.unpackbits(zt['packed_eligible'],axis=1,count=len(zt['item_ids']),bitorder='little'); print('test elig', mt.sum(1).mean(), (zt['item_ids']==z['item_ids']).all(), (zt['uids']==z['uids']).all())\n\"\n", "description": "Inspect data shapes and eligibility stats" }, "result": { "timestamp": "2026-10-03T09:51:59.569Z", "content": "candidate_catalog (30000, 4)\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n\ntarget_users (8456, 3)\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n\nvalidation_preference_state (469124, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n\ntest_preference_state (484776, 3)\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n\ntrain_likes (814831, 4)\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n\nval_listens (1501697, 6)\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\n\ntrain_listens (43484159, 6) 0 24786795 9238 845395\n uid timestamp ... played_ratio_pct track_length_seconds\ncount 4.348416e+07 4.348416e+07 ... 4.348416e+07 4.348416e+07\nmean 4.985911e+05 1.369709e+07 ... 6.465021e+01 2.072443e+02\nstd 2.870021e+05 7.122591e+06 ... 4.412183e+01 8.447807e+01\nmin 1.000000e+02 0.000000e+00 ... 0.000000e+00 5.000000e+00\n25% 2.497000e+05 7.741985e+06 ... 7.000000e+00 1.650000e+02\n50% 4.938000e+05 1.424461e+07 ... 1.000000e+02 2.000000e+02\n75% 7.439000e+05 2.001016e+07 ... 1.000000e+02 2.350000e+02\nmax 1.000000e+06 2.478680e+07 ... 1.590000e+02 2.495000e+03\n\n[8 rows x 6 columns]\nval 24788600 25393395\n{'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}\nelig per user 29067.306291390727 24657 30000\ntest elig 29041.3178807947 True True", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:51:59.572Z", "data": { "type": "total_tokens_reminder", "text": "14976457 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:51:59.575Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 4, "role": "agent", "timestamp": "2026-10-03T09:52:11.767Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:52:11.767Z", "text": "The excluded ~930 items per user are likely tracks the user already interacted with, making this a discovery task recommending unheard tracks. I want to verify what's excluded, check how many validation positives fall within eligible sets, and see what fraction skew toward popular items — the training timeline spans roughly 287 days based on the 24.7M second range.\n\nWith 7 days of validation data and 30 minutes to work, my plan: start with a popularity baseline restricted to eligible candidates, then build an item-item collaborative filtering approach using the 43M listens. Since the candidate catalog is 30k items against a much larger full catalog, computing similarity directly is too expensive, so I'm reframing it as user-user CF instead — the user×user similarity matrix is small and manageable, then I can multiply that against the user×candidate matrix cheaply using cosine-normalized co-occurrence, and I'll also consider EASE on the 30k candidates as another option.\n\nEASE's 30k×30k gram matrix is too large to invert efficiently, so I'll skip it and rely on the faster user-kNN approach instead. Now I want to check what's excluded from eligibility, confirm that 8456 of the 9238 target users appear in train, and run a combined check of the popularity baseline, eligibility exclusions, and relevance stats all at once.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:52:17.796Z", "callId": "toolu_01LsxafwEdfoaaLYmWEfawWk", "name": "Bash", "input": { "command": "python -c \"\nimport pandas as pd, numpy as np, time\nD='/task/data/'\nt=time.time()\ntl=pd.read_parquet(D+'train_listens.parquet')\ntu=pd.read_parquet(D+'target_users.parquet')\nz=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids']; uids=z['uids']\nm=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nprint('load',time.time()-t)\n# which items are excluded for a user?\nu=uids[0]; ex=set(items[~m[0]])\nh=tl[tl.uid==u]\nprint('user',u,'excluded',len(ex),'hist items in catalog', len(set(h.item_id)&set(items)), 'excl∩hist', len(ex&set(h.item_id)))\nhp=h[h.played_ratio_pct>=50]; print('excl∩pos hist', len(ex&set(hp.item_id)))\nlk=pd.read_parquet(D+'train_likes.parquet'); print('excl∩likes', len(ex&set(lk[lk.uid==u].item_id)))\n# val relevance stats\nvl=pd.read_parquet(D+'val_listens.parquet'); vk=pd.read_parquet(D+'val_likes.parquet')\niset=set(items); tset=set(tu.uid)\ns=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(tset)][['uid','item_id']].drop_duplicates()\ns=s[s.item_id.isin(iset)]\nk=vk[(vk.is_organic==1)&vk.uid.isin(tset)][['uid','item_id']].drop_duplicates(); k=k[k.item_id.isin(iset)]\nprint('strong listens in catalog', len(s), 'likes', len(k))\n# eligible?\nui={u:i for i,u in enumerate(uids)}; ii={it:i for i,it in enumerate(items)}\nse=np.array([m[ui[a],ii[b]] for a,b in zip(s.uid,s.item_id)]); ke=np.array([m[ui[a],ii[b]] for a,b in zip(k.uid,k.item_id)])\nprint('eligible strong', se.mean(), 'eligible likes', ke.mean())\nprint('users with relevant', len(set(s.uid[se])|set(k.uid[ke])))\n# organic share of val listens, and what fraction of val organic strong listens are of items in train history for the same user\nprint('val organic frac', vl.is_organic.mean(), 'train organic frac', tl.is_organic.mean())\n\"\n", "description": "Check eligibility exclusions and validation relevance stats" }, "result": { "timestamp": "2026-10-03T09:52:19.848Z", "content": "load 0.8297445774078369\nuser 100 excluded 682 hist items in catalog 682 excl∩hist 682\nexcl∩pos hist 600\nexcl∩likes 13\nstrong listens in catalog 170791 likes 8528\neligible strong 0.20842433149287726 eligible likes 0.5179409005628518\nusers with relevant 4263\nval organic frac 0.517072352145606 train organic frac 0.5169151828370419", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:52:19.852Z", "data": { "type": "total_tokens_reminder", "text": "14973844 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:52:19.854Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 5, "role": "agent", "timestamp": "2026-10-03T09:52:32.902Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:52:32.902Z", "text": "Excluding any item already listened to in train leaves roughly 36k eligible strong listens and 4.4k eligible likes across 4263 users, averaging about 9 relevant items per user. With time limited, I'm planning a feature pipeline combining user-user cosine CF scores, item popularity from recent organic listens, and possibly item-item similarity from likes, blending these with weights tuned on the validation set.\n\nI'm weighing whether to add a gradient-boosted reranker on top of the candidate scores using sklearn's HistGradientBoosting if time allows, then sketching out the core script that computes popularity windows, builds the user-item matrix for cosine similarity CF, and considers item-item CF on candidates -- though I'm wary the candidate-by-candidate matrix could get too large to hold densely.\n\nI'm deciding to skip the dense item-item approach since it's too memory-heavy, instead relying on user-kNN with time-decayed weights to capture recency and organic listen signal, and planning to write a build.py script with scipy sparse matrices producing a manageable per-user score matrix for later blending and ranking.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:52:32.905Z", "callId": "toolu_017FpgAPkjXA4aU4QQe2sFuk", "name": "Bash", "input": { "command": "sed -n 1,80p /task/ranking_evaluation.py | head -80; grep -n \"^def \\|^class \" /task/ranking_evaluation.py", "description": "Check evaluation helper API" }, "result": { "timestamp": "2026-10-03T09:52:32.934Z", "content": "\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n39:class RankingValidationError(ValueError):\n43:class CandidateSet:\n88:def read_ranking(path: Path) -> pd.DataFrame:\n101:def validate_ranking(\n167:def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n183:def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n207:def score_rankings(", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:52:32.937Z", "data": { "type": "total_tokens_reminder", "text": "14972734 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:52:32.939Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 6, "role": "agent", "timestamp": "2026-10-03T09:52:34.920Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T09:52:34.920Z", "text": "Now I'll write the main pipeline: build signal score matrices (popularity, time-decayed user-kNN CF, like-based CF) for a given cutoff, with a fast in-process NDCG evaluator for blend tuning." }, { "kind": "tool", "timestamp": "2026-10-03T09:53:06.571Z", "callId": "toolu_01V47VwnLkpkuvHrYhAxXu9D", "name": "Bash", "input": { "command": "cat > /app/solution/build.py <<'EOF'\n\"\"\"Build 'Made for You' discovery rankings.\n\nSignals (computed per cutoff from history only):\n pop : recent organic strong-listen popularity of catalog items\n uucf : time-decayed user-kNN collaborative filtering (cosine on\n positive-listen history over the full item universe), scored\n against neighbours' positive-listen + like matrix on catalog items\n lk : user-kNN scores on neighbours' organic likes of catalog items\nBlended by rank-normalised weighted sum, masked by eligibility, top-100 kept.\n\"\"\"\nimport sys, time, numpy as np, pandas as pd, scipy.sparse as sp\nD = '/task/data/'\nOUT = '/app/solution/'\nTRAIN_END = 24786800\nVAL_END = 25393395 + 1\n\ndef load_events(stage):\n fams = ['listens', 'likes', 'dislikes']\n ev = {}\n for f in fams:\n df = pd.read_parquet(D + f'train_{f}.parquet')\n if stage == 'test':\n df = pd.concat([df, pd.read_parquet(D + f'val_{f}.parquet')], ignore_index=True)\n ev[f] = df\n return ev\n\ndef build_signals(stage, params):\n t0 = time.time()\n cutoff = TRAIN_END if stage == 'val' else VAL_END\n z = np.load(D + f'{\"validation\" if stage==\"val\" else \"test\"}_eligible_candidates.npz')\n uids, items = z['uids'].astype(np.int64), z['item_ids'].astype(np.int64)\n elig = np.unpackbits(z['packed_eligible'], axis=1, count=len(items), bitorder='little').astype(bool)\n ev = load_events(stage)\n L, K = ev['listens'], ev['likes']\n all_users = np.unique(np.concatenate([L.uid.unique(), uids]))\n urow = pd.Series(np.arange(len(all_users)), index=all_users)\n nu = len(all_users)\n item_col = pd.Series(np.arange(len(items)), index=items)\n DAY = 86400.0\n # ---- popularity: organic strong listens in last pop_days\n pos = L[L.played_ratio_pct >= 50]\n age = (cutoff - pos.timestamp.values) / DAY\n inc = pos.item_id.isin(item_col.index).values\n w = np.exp(-age / params['pop_tau']) * np.where(pos.is_organic.values == 1, 1.0, params['pop_rec_w'])\n pop = np.zeros(len(items))\n np.add.at(pop, item_col.loc[pos.item_id.values[inc]].values, w[inc])\n # ---- user history matrix over ALL items (for neighbour computation)\n hu = urow.loc[pos.uid.values].values\n all_items, hi = np.unique(pos.item_id.values, return_inverse=True)\n hw = np.exp(-age / params['hist_tau']) * np.where(pos.is_organic.values == 1, 1.0, params['hist_rec_w'])\n X = sp.csr_matrix((hw, (hu, hi)), shape=(nu, len(all_items)))\n X.sum_duplicates()\n X.data = np.log1p(X.data)\n # idf-ish item weighting\n icnt = np.bincount(hi, minlength=len(all_items))\n X = X @ sp.diags(1.0 / np.power(icnt, params['idf_pow']))\n rn = np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel()); rn[rn == 0] = 1\n X = sp.diags(1.0 / rn) @ X\n X = X.tocsr().astype(np.float32)\n S = (X @ X.T).toarray()\n np.fill_diagonal(S, 0)\n trow = urow.loc[uids].values\n S = S[trow] # targets x all users\n # keep top-k neighbours\n k = params['knn']\n if k < S.shape[1]:\n thr = -np.partition(-S, k, axis=1)[:, k][:, None]\n S[S < thr] = 0\n S = np.power(S, params['sim_pow']).astype(np.float32)\n # ---- candidate matrices (all users x catalog)\n def cand_mat(df, wts):\n inc = df.item_id.isin(item_col.index).values\n M = sp.csr_matrix((wts[inc], (urow.loc[df.uid.values[inc]].values, item_col.loc[df.item_id.values[inc]].values)), shape=(nu, len(items)))\n M.sum_duplicates(); return M\n cw = np.exp(-age / params['cand_tau']) * np.where(pos.is_organic.values == 1, 1.0, params['cand_rec_w'])\n Xc = cand_mat(pos, cw); Xc.data = np.log1p(Xc.data)\n lage = (cutoff - K.timestamp.values) / DAY\n lw = np.exp(-lage / params['cand_tau']) * np.where(K.is_organic.values == 1, 1.0, params['cand_rec_w'])\n Lc = cand_mat(K, lw)\n uucf = np.asarray(S @ Xc.astype(np.float32).toarray()) if False else (sp.csr_matrix(S) @ Xc).toarray()\n lk = (sp.csr_matrix(S) @ Lc).toarray()\n # dislikes mask (hard negative): drop items the user disliked\n print(f'[{stage}] signals built in {time.time()-t0:.0f}s', file=sys.stderr)\n return dict(uids=uids, items=items, elig=elig, pop=pop, uucf=uucf.astype(np.float32), lk=lk.astype(np.float32))\n\ndef blend(sig, wts):\n n = len(sig['uids'])\n sc = np.zeros((n, len(sig['items'])), dtype=np.float32)\n for name, w in wts.items():\n if w == 0: continue\n m = sig[name]\n if m.ndim == 1: m = np.broadcast_to(m, sc.shape)\n # per-user log-scaled contribution\n sc += w * np.log1p(m / (m.max(axis=-1, keepdims=True) + 1e-9) * 1000).astype(np.float32)\n sc[~sig['elig']] = -np.inf\n return sc\n\ndef top100(sig, sc):\n idx = np.argpartition(-sc, 100, axis=1)[:, :100]\n vals = np.take_along_axis(sc, idx, 1)\n order = np.argsort(-vals, axis=1)\n idx = np.take_along_axis(idx, order, 1)\n df = pd.DataFrame({'uid': np.repeat(sig['uids'], 100), 'item_id': sig['items'][idx.ravel()], 'rank': np.tile(np.arange(1, 101), len(sig['uids']))})\n return df\n\ndef val_truth(sig):\n vl = pd.read_parquet(D + 'val_listens.parquet'); vk = pd.read_parquet(D + 'val_likes.parquet')\n s = vl[(vl.is_organic == 1) & (vl.played_ratio_pct >= 80)][['uid', 'item_id']].drop_duplicates()\n k = vk[vk.is_organic == 1][['uid', 'item_id']].drop_duplicates()\n ur = pd.Series(np.arange(len(sig['uids'])), index=sig['uids']); ic = pd.Series(np.arange(len(sig['items'])), index=sig['items'])\n G = np.zeros(sig['elig'].shape, dtype=np.int8)\n for df, g in [(s, 1), (k, 3)]:\n df = df[df.uid.isin(ur.index) & df.item_id.isin(ic.index)]\n r, c = ur.loc[df.uid.values].values, ic.loc[df.item_id.values].values\n G[r, c] = np.maximum(G[r, c], g)\n G[~sig['elig']] = 0\n return G\n\ndef ndcg10(sig, sc, G):\n idx = np.argpartition(-sc, 10, axis=1)[:, :10]\n vals = np.take_along_axis(sc, idx, 1)\n idx = np.take_along_axis(idx, np.argsort(-vals, axis=1), 1)\n g = np.take_along_axis(G, idx, 1).astype(float)\n disc = 1 / np.log2(np.arange(2, 12))\n dcg = ((2 ** g - 1) * disc).sum(1)\n gs = -np.sort(-G.astype(float), axis=1)[:, :10]\n idcg = ((2 ** gs - 1) * disc).sum(1)\n m = idcg > 0\n return (dcg[m] / idcg[m]).mean(), m.sum()\n\nDEFAULT = dict(pop_tau=14, pop_rec_w=0.3, hist_tau=60, hist_rec_w=0.5, idf_pow=0.5, knn=200, sim_pow=1.0, cand_tau=30, cand_rec_w=0.5)\nWTS = dict(pop=0.3, uucf=1.0, lk=0.5)\n\nif __name__ == '__main__':\n stage = sys.argv[1]\n sig = build_signals(stage, DEFAULT)\n sc = blend(sig, WTS)\n if stage == 'val':\n G = val_truth(sig); print('val ndcg@10', ndcg10(sig, sc, G))\n top100(sig, sc).to_parquet(OUT + 'validation.parquet', index=False)\n else:\n top100(sig, sc).to_parquet(OUT + 'submission.parquet', index=False)\nEOF\ncd /app/solution && python -c \"\nimport build, numpy as np, pickle, time\nsig=build.build_signals('val', build.DEFAULT)\nG=build.val_truth(sig)\nfor w in [dict(pop=1,uucf=0,lk=0), dict(pop=0,uucf=1,lk=0), dict(pop=0,uucf=0,lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G))\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\n\"\n", "description": "Write pipeline and run validation signal baselines", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T09:53:27.346Z", "content": "Exit code 1\nTraceback (most recent call last):\n File \"\", line 3, in \n File \"/app/solution/build.py\", line 79, in build_signals\n Lc = cand_mat(K, lw)\n ^^^^^^^^^^^^^^^\n File \"/app/solution/build.py\", line 73, in cand_mat\n M = sp.csr_matrix((wts[inc], (urow.loc[df.uid.values[inc]].values, item_col.loc[df.item_id.values[inc]].values)), shape=(nu, len(items)))\n ~~~~~~~~^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1192, in __getitem__\n return self._getitem_axis(maybe_callable, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1421, in _getitem_axis\n return self._getitem_iterable(key, axis=axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1361, in _getitem_iterable\n keyarr, indexer = self._get_listlike_indexer(key, axis)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1559, in _get_listlike_indexer\n keyarr, indexer = ax._get_indexer_strict(key, axis_name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n self._raise_if_missing(keyarr, indexer, axis_name)\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6264, in _raise_if_missing\n raise KeyError(f\"{not_found} not in index\")\nKeyError: '[400, 1200, 1800, 3500, 4700, 5800, 6400, 8400, 8500, 8600, 8800, 10100, 11000, 13900, 16400, 17500, 19000, 19600, 19700, 20100, 22200, 23600, 23900, 27300, 27800, 28300, 30400, 35000, 35300, 38000, 39800, 41100, 41600, 42600, 48100, 48200, 48700, 51400, 51800, 52300, 53000, 53100, 54700, 55600, 56000, 56800, 57200, 57500, 58500, 61000, 61200, 62700, 63900, 64300, 64400, 66500, 66800, 68400, 68600, 68900, 69900, 72000, 73200, 74000, 75300, 75700, 75800, 76200, 76600, 78200, 79800, 80600, 82500, 85700, 88200, 89500, 90800, 91600, 96100, 96800, 97100, 97200, 97500, 97800, 99100, 101600, 102300, 102900, 104800, 107000, 108000, 108400, 109000, 112300, 114900, 115100, 116000, 117300, 119500, 120300, 120500, 121800, 122800, 123400, 124200, 124900, 129700, 134700, 136100, 136500, 139300, 140500, 141400, 142200, 143400, 144300, 144500, 145100, 145700, 146800, 148000, 151100, 151700, 152800, 152900, 153000, 158800, 160000, 161800, 163300, 164000, 164200, 165000, 165300, 165900, 167100, 167300, 169000, 170600, 172300, 173600, 174400, 175400, 176600, 179300, 183300, 184100, 186700, 189600, 191700, 193200, 193600, 196100, 196200, 200300, 200400, 203500, 204100, 204200, 206700, 207200, 210900, 211100, 211700, 212200, 212600, 213500, 214100, 215600, 215800, 217900, 218300, 219200, 221200, 221400, 221600, 221800, 222200, 222700, 224900, 226900, 227500, 227800, 228000, 233300, 233500, 233800, 234300, 234700, 236700, 239000, 239100, 239800, 240100, 242200, 243400, 245600, 247000, 247500, 249800, 250200, 250500, 250900, 251000, 255600, 255800, 257200, 260100, 260600, 261200, 261700, 266000, 267000, 270500, 275000, 275400, 276000, 278300, 278800, 279200, 279700, 280800, 282000, 285000, 285700, 289200, 290000, 291100, 294000, 295000, 297700, 298000, 299800, 300100, 300700, 302400, 302500, 302600, 302700, 303600, 305600, 306000, 306100, 306500, 308600, 310500, 313000, 313100, 313800, 313900, 314300, 315300, 316800, 317400, 317500, 318500, 318800, 320000, 320800, 321400, 322600, 324500, 325200, 327600, 330600, 332800, 333700, 334000, 336500, 337500, 338600, 339900, 340600, 341900, 342100, 342300, 343900, 344100, 345500, 346600, 348700, 349100, 352000, 353700, 353900, 354500, 355000, 355400, 356400, 359300, 360200, 360400, 362200, 363200, 363300, 364200, 367600, 368200, 369500, 370900, 371100, 373700, 374700, 375800, 377900, 380600, 381000, 381600, 382900, 383500, 383700, 385500, 386300, 387700, 388200, 388500, 389400, 389900, 390100, 392200, 393100, 395800, 397200, 398000, 398800, 402400, 403000, 404300, 404600, 407700, 408900, 411300, 414600, 415600, 415900, 417300, 417600, 417800, 419000, 420600, 422000, 423200, 424600, 426400, 428300, 428400, 429400, 430400, 430600, 432000, 432100, 433200, 433500, 434700, 440800, 441300, 441400, 441500, 441600, 442500, 444800, 445700, 446200, 447700, 448500, 451900, 452500, 453800, 455100, 459900, 461700, 464100, 464200, 466800, 472200, 474600, 475200, 476000, 477000, 477200, 478700, 478900, 481500, 483300, 484400, 486400, 486600, 488100, 493900, 494000, 495600, 497000, 497100, 499200, 499700, 500400, 503100, 503200, 505700, 509000, 511700, 512200, 513100, 513200, 514800, 516000, 517300, 518200, 520100, 520400, 521000, 521200, 522900, 523600, 524900, 525200, 526500, 527300, 528100, 528500, 529700, 532400, 534200, 535300, 535600, 535800, 536500, 536800, 537000, 539300, 543400, 543800, 544300, 545800, 546700, 547800, 550100, 550400, 550600, 553100, 554400, 556900, 559100, 559200, 559900, 561200, 563500, 564300, 565900, 567400, 567700, 568800, 569700, 570700, 571900, 573400, 575500, 576900, 577900, 578100, 579400, 579500, 581400, 581500, 582600, 586500, 587100, 587900, 588300, 591400, 592800, 594500, 597600, 600100, 600400, 601800, 604900, 616100, 616200, 617100, 620300, 621500, 623000, 623300, 626100, 627600, 628100, 629400, 630700, 635000, 637300, 638400, 638900, 640100, 640800, 643300, 645000, 646000, 648900, 649000, 649900, 651500, 653200, 653900, 654000, 655500, 656300, 666500, 671400, 672000, 672200, 673300, 673600, 674800, 675100, 678200, 681200, 682500, 684100, 684300, 687000, 689400, 689500, 691300, 693000, 693200, 694200, 695600, 696000, 697200, 697500, 700900, 704900, 705500, 706500, 708000, 708200, 708600, 709100, 709500, 710300, 714200, 714300, 715200, 715800, 718400, 720900, 721400, 722000, 723600, 724100, 724500, 725300, 725600, 726200, 728900, 729700, 730600, 732400, 732500, 734500, 734900, 737100, 737600, 740200, 740400, 743000, 744800, 747100, 747700, 748300, 750100, 750600, 753400, 754200, 755100, 759400, 769300, 773500, 774500, 778200, 778800, 779300, 781700, 784100, 786800, 787700, 788500, 791300, 793300, 794500, 797500, 798700, 801200, 804200, 804400, 806400, 807200, 807400, 807900, 808200, 808600, 809100, 809600, 810400, 812100, 812700, 814500, 816800, 818700, 818900, 820000, 820200, 824400, 826700, 829300, 829400, 829500, 830200, 831000, 833000, 833900, 834200, 834400, 837800, 840200, 842400, 844800, 844900, 846400, 846600, 847700, 848300, 849000, 849100, 850200, 850400, 850900, 852000, 852300, 852800, 853100, 854100, 855600, 855700, 857600, 859900, 863000, 863100, 863300, 863900, 866700, 868300, 872100, 873400, 873500, 873600, 875200, 875700, 876100, 876900, 877100, 878100, 881400, 884700, 886100, 886600, 887400, 887700, 888800, 890800, 890900, 891700, 891800, 892000, 892300, 893900, 894300, 895100, 897100, 898800, 898900, 899700, 900800, 901900, 902700, 903200, 904000, 904900, 905500, 905700, 907400, 907700, 912000, 912300, 914500, 920800, 922200, 923300, 924300, 928000, 928400, 930300, 930700, 931000, 931400, 932000, 934100, 935700, 936000, 936400, 937400, 937800, 938700, 940100, 943600, 949100, 950600, 951200, 952200, 953100, 953300, 953500, 955500, 956800, 958500, 958700, 959700, 960900, 962500, 962600, 963400, 967000, 967200, 968200, 969100, 970300, 970400, 971200, 971500, 971700, 973000, 976500, 977600, 978200, 982300, 983600, 984600, 986000, 986300, 987000, 991600, 993500, 993900, 994900, 996000, 997400, 997700, 999300] not in index'", "isError": true } }, { "kind": "context", "timestamp": "2026-10-03T09:53:27.349Z", "data": { "type": "total_tokens_reminder", "text": "14967714 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:53:27.352Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 7, "role": "agent", "timestamp": "2026-10-03T09:53:30.391Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T09:53:30.391Z", "text": "Users in likes but not in listens. Quick fix: include all users from likes too in the user index." }, { "kind": "tool", "timestamp": "2026-10-03T09:53:33.938Z", "callId": "toolu_011hQhM6BvMu6NVprR89CjbZ", "name": "Bash", "input": { "command": "cd /app/solution && sed -i \"s/all_users = np.unique(np.concatenate(\\[L.uid.unique(), uids\\]))/all_users = np.unique(np.concatenate([L.uid.unique(), K.uid.unique(), uids]))/\" build.py && python -c \"\nimport build, numpy as np, pickle, time\nsig=build.build_signals('val', build.DEFAULT)\nG=build.val_truth(sig)\nfor w in [dict(pop=1,uucf=0,lk=0), dict(pop=0,uucf=1,lk=0), dict(pop=0,uucf=0,lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G))\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\n\" 2>&1 | grep -v Warning", "description": "Fix user index and run validation baselines", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T09:54:03.470Z", "content": "(Bash completed with no output)", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:54:03.514Z", "data": { "type": "total_tokens_reminder", "text": "14963283 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:54:03.533Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-10-03T09:54:06.416Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:54:06.416Z", "text": "Odd that there's no output at all - maybe grep filtered it out, or the process got killed by OOM. I should rerun without grep and check the exit code directly.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:54:08.343Z", "callId": "toolu_01GU16Xn9CkgiagZSRzjBN2x", "name": "Bash", "input": { "command": "cd /app/solution && python -c \"\nimport build, numpy as np, time\nsig=build.build_signals('val', build.DEFAULT)\nG=build.val_truth(sig)\nfor w in [dict(pop=1,uucf=0,lk=0), dict(pop=0,uucf=1,lk=0), dict(pop=0,uucf=0,lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\n\"; echo EXIT $?; dmesg 2>/dev/null | tail -2", "description": "Rerun validation baselines showing exit status", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T09:54:36.913Z", "content": "/bin/bash: line 15: 8227 Killed python -c \"\nimport build, numpy as np, time\nsig=build.build_signals('val', build.DEFAULT)\nG=build.val_truth(sig)\nfor w in [dict(pop=1,uucf=0,lk=0), dict(pop=0,uucf=1,lk=0), dict(pop=0,uucf=0,lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\n\"\nEXIT 137", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:54:36.997Z", "data": { "type": "environment", "snapshot": { "workingDirectory": "/app/solution", "isWorktree": false, "isGitRepo": false, "additionalWorkingDirectories": [], "platform": "linux", "shell": "unknown", "osVersion": "Linux 7.0.0-1013-aws" }, "changes": [ { "field": "workingDirectory", "from": "/app" } ] } }, { "kind": "context", "timestamp": "2026-10-03T09:54:36.999Z", "data": { "type": "total_tokens_reminder", "text": "14962798 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:54:37.016Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-10-03T09:54:44.731Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:54:44.731Z", "text": "I'm running into memory limits with the 43M-row dataset, so I need to be lean: read only necessary columns, filter to positives early, use numpy arrays instead of extra pandas copies, and free frames as I go. I'm also weighing whether computing X @ X.T on a sparse matrix with ~15M nonzeros will blow up memory during the intermediate computation, even though the final dense result (~85M floats) should be manageable.\n\nBetter to restrict the matrix multiply to only the target rows rather than the full matrix, since pandas `.loc` on huge index arrays tends to create large temporary objects. I'm going to rewrite the mapping logic using numpy's searchsorted, drop unneeded columns early, use pyarrow for selective column reads, and add timing prints to track performance.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:54:44.761Z", "text": "The run was OOM-killed. I'll make the pipeline memory-lean: read only needed columns, filter to positives immediately, and use numpy index mapping instead of pandas `.loc` on multi-million arrays." }, { "kind": "tool", "timestamp": "2026-10-03T09:55:05.645Z", "callId": "toolu_019uyXXYekGNQBuZNiq1AhkM", "name": "Bash", "input": { "command": "python - <<'EOF'\nimport re\ns = open('build.py').read()\ns = s.replace('''def load_events(stage):\n fams = ['listens', 'likes', 'dislikes']\n ev = {}\n for f in fams:\n df = pd.read_parquet(D + f'train_{f}.parquet')\n if stage == 'test':\n df = pd.concat([df, pd.read_parquet(D + f'val_{f}.parquet')], ignore_index=True)\n ev[f] = df\n return ev\n''', '''def load_events(stage):\n ev = {}\n for f in ['listens', 'likes']:\n parts = []\n for p in (['train'] if stage == 'val' else ['train', 'val']):\n df = pd.read_parquet(D + f'{p}_{f}.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic'] + (['played_ratio_pct'] if f == 'listens' else []))\n if f == 'listens':\n df = df[df.played_ratio_pct >= 50][['uid', 'timestamp', 'item_id', 'is_organic']]\n parts.append(df)\n df = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]\n ev[f] = {c: df[c].values for c in df.columns}\n del df, parts\n return ev\n\ndef map_ids(keys, values):\n \"\"\"Map values to positions in sorted keys; -1 when absent.\"\"\"\n pos = np.searchsorted(keys, values)\n pos[pos >= len(keys)] = 0\n ok = keys[pos] == values\n return np.where(ok, pos, -1)\n''')\ns = s.replace(''' L, K = ev['listens'], ev['likes']\n all_users = np.unique(np.concatenate([L.uid.unique(), K.uid.unique(), uids]))\n urow = pd.Series(np.arange(len(all_users)), index=all_users)\n nu = len(all_users)\n item_col = pd.Series(np.arange(len(items)), index=items)\n DAY = 86400.0\n # ---- popularity: organic strong listens in last pop_days\n pos = L[L.played_ratio_pct >= 50]\n age = (cutoff - pos.timestamp.values) / DAY\n inc = pos.item_id.isin(item_col.index).values\n w = np.exp(-age / params['pop_tau']) * np.where(pos.is_organic.values == 1, 1.0, params['pop_rec_w'])\n pop = np.zeros(len(items))\n np.add.at(pop, item_col.loc[pos.item_id.values[inc]].values, w[inc])\n # ---- user history matrix over ALL items (for neighbour computation)\n hu = urow.loc[pos.uid.values].values\n all_items, hi = np.unique(pos.item_id.values, return_inverse=True)\n hw = np.exp(-age / params['hist_tau']) * np.where(pos.is_organic.values == 1, 1.0, params['hist_rec_w'])\n''', ''' L, K = ev['listens'], ev['likes']\n all_users = np.unique(np.concatenate([L['uid'], K['uid'], uids]))\n nu = len(all_users)\n items_sorted = np.sort(items); items_perm = np.argsort(items)\n def icol(v):\n p = map_ids(items_sorted, v); return np.where(p >= 0, items_perm[np.maximum(p, 0)], -1)\n DAY = 86400.0\n # ---- popularity: organic strong listens, time-decayed\n age = (cutoff - L['timestamp']) / DAY\n org = L['is_organic'] == 1\n pc = icol(L['item_id']); inc = pc >= 0\n w = np.exp(-age / params['pop_tau']) * np.where(org, 1.0, params['pop_rec_w'])\n pop = np.bincount(pc[inc], weights=w[inc], minlength=len(items))\n # ---- user history matrix over ALL items (for neighbour computation)\n hu = map_ids(all_users, L['uid'])\n all_items, hi = np.unique(L['item_id'], return_inverse=True)\n hw = np.exp(-age / params['hist_tau']) * np.where(org, 1.0, params['hist_rec_w'])\n''')\ns = s.replace(''' S = (X @ X.T).toarray()\n np.fill_diagonal(S, 0)\n trow = urow.loc[uids].values\n S = S[trow] # targets x all users\n''', ''' trow = map_ids(all_users, uids)\n S = (X[trow] @ X.T).toarray() # targets x all users\n S[np.arange(len(trow)), trow] = 0\n del X\n''')\ns = s.replace(''' def cand_mat(df, wts):\n inc = df.item_id.isin(item_col.index).values\n M = sp.csr_matrix((wts[inc], (urow.loc[df.uid.values[inc]].values, item_col.loc[df.item_id.values[inc]].values)), shape=(nu, len(items)))\n M.sum_duplicates(); return M\n cw = np.exp(-age / params['cand_tau']) * np.where(pos.is_organic.values == 1, 1.0, params['cand_rec_w'])\n Xc = cand_mat(pos, cw); Xc.data = np.log1p(Xc.data)\n lage = (cutoff - K.timestamp.values) / DAY\n lw = np.exp(-lage / params['cand_tau']) * np.where(K.is_organic.values == 1, 1.0, params['cand_rec_w'])\n Lc = cand_mat(K, lw)\n uucf = np.asarray(S @ Xc.astype(np.float32).toarray()) if False else (sp.csr_matrix(S) @ Xc).toarray()\n''', ''' def cand_mat(ev1, wts):\n c = icol(ev1['item_id']); inc = c >= 0\n M = sp.csr_matrix((wts[inc], (map_ids(all_users, ev1['uid'][inc]), c[inc])), shape=(nu, len(items)))\n M.sum_duplicates(); return M\n cw = np.exp(-age / params['cand_tau']) * np.where(org, 1.0, params['cand_rec_w'])\n Xc = cand_mat(L, cw); Xc.data = np.log1p(Xc.data)\n lage = (cutoff - K['timestamp']) / DAY\n lw = np.exp(-lage / params['cand_tau']) * np.where(K['is_organic'] == 1, 1.0, params['cand_rec_w'])\n Lc = cand_mat(K, lw)\n uucf = (sp.csr_matrix(S) @ Xc).toarray()\n''')\nopen('build.py', 'w').write(s)\nEOF\npython -c \"\nimport build, numpy as np, time\nsig=build.build_signals('val', build.DEFAULT)\nG=build.val_truth(sig)\nfor w in [dict(pop=1,uucf=0,lk=0), dict(pop=0,uucf=1,lk=0), dict(pop=0,uucf=0,lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\n\"; echo EXIT $?", "description": "Rewrite pipeline to be memory-lean and run validation baselines", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T09:55:40.145Z", "content": "[val] signals built in 33s\n/bin/bash: line 215: 8264 Killed python -c \"\nimport build, numpy as np, time\nsig=build.build_signals('val', build.DEFAULT)\nG=build.val_truth(sig)\nfor w in [dict(pop=1,uucf=0,lk=0), dict(pop=0,uucf=1,lk=0), dict(pop=0,uucf=0,lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\n\"\nEXIT 137", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:55:40.197Z", "data": { "type": "total_tokens_reminder", "text": "14959273 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:55:40.219Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-10-03T09:55:46.166Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:55:46.166Z", "text": "I'm realizing the blend and ndcg steps are ballooning memory with float64 temporaries from broadcasting and exponentiation on large matrices, so I need to restructure those operations to stay in float32 and avoid creating full-size intermediate copies.\n\nFor ndcg specifically, I can replace sorting with a bincount-based approach, computing idcg directly from the counts of each grade per user rather than sorting the whole matrix.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:55:46.199Z", "text": "Signals build fine in 33s; the blend/NDCG step blows memory with float64 temporaries over 8456×30000 matrices. I'll make both in-place float32 and compute ideal DCG from grade counts." }, { "kind": "tool", "timestamp": "2026-10-03T09:55:55.205Z", "callId": "toolu_01JYCCay6np7b3bfN6k4hSpQ", "name": "Bash", "input": { "command": "python - <<'EOF'\ns = open('build.py').read()\ns = s.replace(''' for name, w in wts.items():\n if w == 0: continue\n m = sig[name]\n if m.ndim == 1: m = np.broadcast_to(m, sc.shape)\n # per-user log-scaled contribution\n sc += w * np.log1p(m / (m.max(axis=-1, keepdims=True) + 1e-9) * 1000).astype(np.float32)\n sc[~sig['elig']] = -np.inf\n return sc\n''', ''' for name, w in wts.items():\n if w == 0: continue\n m = sig[name]\n if m.ndim == 1:\n t = np.log1p(m / (m.max() + 1e-9) * 1000).astype(np.float32)\n sc += np.float32(w) * t[None, :]\n else:\n scale = (1000.0 / (m.max(axis=1, keepdims=True) + 1e-9)).astype(np.float32)\n t = m * scale; np.log1p(t, out=t); t *= np.float32(w); sc += t; del t\n sc[~sig['elig']] = -np.inf\n return sc\n''')\ns = s.replace(''' g = np.take_along_axis(G, idx, 1).astype(float)\n disc = 1 / np.log2(np.arange(2, 12))\n dcg = ((2 ** g - 1) * disc).sum(1)\n gs = -np.sort(-G.astype(float), axis=1)[:, :10]\n idcg = ((2 ** gs - 1) * disc).sum(1)\n''', ''' g = np.take_along_axis(G, idx, 1).astype(float)\n disc = 1 / np.log2(np.arange(2, 12))\n dcg = ((2 ** g - 1) * disc).sum(1)\n n3 = (G == 3).sum(1); n1 = (G == 1).sum(1)\n idcg = np.zeros(len(G))\n for u in range(len(G)):\n gs = np.concatenate([np.full(min(n3[u], 10), 3.0), np.full(max(0, min(n1[u], 10 - n3[u])), 1.0)])\n idcg[u] = ((2 ** gs - 1) * disc[:len(gs)]).sum()\n''')\nopen('build.py', 'w').write(s)\nEOF\npython -c \"\nimport build, numpy as np, time\nsig=build.build_signals('val', build.DEFAULT)\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\nG=build.val_truth(sig)\nfor w in [dict(pop=1), dict(uucf=1), dict(lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\n\"; echo EXIT $?", "description": "Make blend and NDCG memory-lean, rerun baselines", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T09:56:29.621Z", "content": "[val] signals built in 33s\n/bin/bash: line 91: 8298 Killed python -c \"\nimport build, numpy as np, time\nsig=build.build_signals('val', build.DEFAULT)\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\nG=build.val_truth(sig)\nfor w in [dict(pop=1), dict(uucf=1), dict(lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\n\"\nEXIT 137", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:56:29.669Z", "data": { "type": "total_tokens_reminder", "text": "14957491 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:56:29.690Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-10-03T09:56:33.996Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:56:33.996Z", "text": "It's still getting killed with 14GB available, so I'm wondering if the actual memory footprint from the signal arrays and dense matrices is larger than expected, or if the process is hitting a cgroup limit lower than the reported system memory.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:56:34.619Z", "callId": "toolu_01U2AvCn2Qybxnn6b5hUqxSM", "name": "Bash", "input": { "command": "cat /sys/fs/cgroup/memory.max 2>/dev/null; cat /sys/fs/cgroup/memory/memory.limit_in_bytes 2>/dev/null; cat /sys/fs/cgroup/memory.peak 2>/dev/null; ulimit -a | grep -i mem", "description": "Check container memory limit" }, "result": { "timestamp": "2026-10-03T09:56:34.734Z", "content": "8589934592\n8589934592\nmax locked memory (kbytes, -l) 8192\nmax memory size (kbytes, -m) unlimited\nvirtual memory (kbytes, -v) unlimited", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:56:34.737Z", "data": { "type": "total_tokens_reminder", "text": "14956854 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:56:34.739Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-10-03T09:56:47.280Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:56:47.280Z", "text": "I'm hitting the 8GB memory limit, so I need to trim usage: freeing the event arrays before blending, keeping score and lookup tables as float32 or int8 where possible, and estimating that these optimizations bring total memory down to around 3.5GB versus the much larger footprint from the raw build arrays.\n\nThe real risk is the sparse matrix product — if I convert the S@Xc result to a sparse format with full nnz, it could balloon to several gigabytes and cause the OOM. Switching to a dense matmul of S (sparse, k-nearest limited) against Xc as a dense float32 array avoids that blowup, keeping the output directly dense and the computation tractable within tens of seconds on available cores.\n\nI should also free the `ev` object once the matrices are built to reclaim memory, and reuse the same approach for computing both the user-user and label-correlation products against the dense buffers.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T09:56:47.312Z", "text": "Found the issue: the container's 8GB limit is being blown by a sparse-times-sparse product producing a near-dense 3GB result. I'll switch to multiplying the sparse neighbour matrix by a dense candidate matrix and free the event arrays early to cut memory use.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T09:56:50.231Z", "callId": "toolu_015AVVrRKWcMvZZCBNTd8NoD", "name": "Bash", "input": { "command": "python - <<'EOF'\ns = open('build.py').read()\ns = s.replace(''' uucf = (sp.csr_matrix(S) @ Xc).toarray()\n lk = (sp.csr_matrix(S) @ Lc).toarray()\n''', ''' del L, K, ev, age, w, hw, cw, lw, hu, hi\n Ss = sp.csr_matrix(S); del S\n Xd = Xc.astype(np.float32).toarray(); del Xc\n uucf = Ss @ Xd; del Xd\n Ld = Lc.astype(np.float32).toarray(); del Lc\n lk = Ss @ Ld; del Ld\n''')\nopen('build.py', 'w').write(s)\nEOF\npython -c \"\nimport build, numpy as np, time\nsig=build.build_signals('val', build.DEFAULT)\nnp.savez('/app/solution/sig_val.npz', **{k:v for k,v in sig.items()})\nG=build.val_truth(sig)\nfor w in [dict(pop=1), dict(uucf=1), dict(lk=1), dict(pop=0.3,uucf=1,lk=0.5), dict(pop=0.1,uucf=1,lk=0.3), dict(pop=0.3,uucf=1,lk=1)]:\n print(w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\n\"; echo EXIT $?; cat /sys/fs/cgroup/memory.peak", "description": "Use sparse-times-dense products and rerun baselines", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T09:58:15.167Z", "content": "[val] signals built in 45s\n{'pop': 1} (np.float64(0.02201803060170877), np.int64(4263))\n{'uucf': 1} (np.float64(0.030788457925464877), np.int64(4263))\n{'lk': 1} (np.float64(0.027891681807675865), np.int64(4263))\n{'pop': 0.3, 'uucf': 1, 'lk': 0.5} (np.float64(0.03333829901516227), np.int64(4263))\n{'pop': 0.1, 'uucf': 1, 'lk': 0.3} (np.float64(0.0331427068863448), np.int64(4263))\n{'pop': 0.3, 'uucf': 1, 'lk': 1} (np.float64(0.03410924053662473), np.int64(4263))\nEXIT 0\n8589934592", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:58:15.171Z", "data": { "type": "total_tokens_reminder", "text": "14955322 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:58:15.173Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 13, "role": "agent", "timestamp": "2026-10-03T09:58:21.264Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:58:21.264Z", "text": "NDCG lands around 0.034, which is low in absolute terms but expected given this is a cold-start discovery task, and the 4263 scored users matches the evaluator's count. With about 17 minutes left, my priority is to first lock in a safe test submission using the current best blend while running the test stage in the background, then use the remaining time to tune blend weights and other parameters on validation.\n\nGiven each build takes roughly 45 seconds and memory is tight (running test and validation builds concurrently risks exceeding 8GB), I'll run things sequentially: submit the safety version first with weights pop=0.3, uucf=1, lk=1, then move to tuning knn and tau parameters afterward.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:58:21.267Z", "text": "Baseline works (NDCG@10 ≈ 0.034 on 4263 scored users, matching the evaluator's user count). First I'll secure a valid test submission with the current best blend, then tune." }, { "kind": "tool", "timestamp": "2026-10-03T09:58:24.854Z", "callId": "toolu_019QV1i3PmRtfA4cMH6buXXY", "name": "Bash", "input": { "command": "sed -i \"s/^WTS = .*/WTS = dict(pop=0.3, uucf=1.0, lk=1.0)/\" build.py && python build.py test && python -c \"\nimport pandas as pd, numpy as np\ndf=pd.read_parquet('submission.parquet'); print(df.shape, df.uid.nunique(), df.groupby('uid').size().agg(['min','max']), df.groupby('uid')['rank'].agg(['min','max']).agg(['min','max']))\nz=np.load('/task/data/test_eligible_candidates.npz'); m=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool)\nur={u:i for i,u in enumerate(z['uids'])}; ic={it:i for i,it in enumerate(z['item_ids'])}\nprint('all eligible', all(m[ur[u],ic[i]] for u,i in zip(df.uid,df.item_id)), 'dups', df.duplicated(['uid','item_id']).sum())\n\"; cat /sys/fs/cgroup/memory.peak", "description": "Build test submission and validate its structure", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T09:59:19.392Z", "content": "[test] signals built in 46s\n(845600, 3) 8456 min 100\nmax 100\ndtype: int64 min max\nmin 1 100\nmax 1 100\nall eligible True dups 0\n8589934592", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T09:59:19.395Z", "data": { "type": "total_tokens_reminder", "text": "14954153 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T09:59:19.397Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-10-03T09:59:23.211Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T09:59:23.211Z", "text": "With a valid submission saved and about 15 minutes left, I want to quickly tune the blend weights on the saved signals since that's fast and in-process, and also try an alternative signal build with different knn, hist_tau, sim_pow, and cand_tau values to compare against the current setup, running both sequentially to avoid memory issues.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T09:59:23.213Z", "text": "A valid submission is saved. Now I'll tune blend weights on the saved validation signals, then test a couple of alternative signal parameters." }, { "kind": "tool", "timestamp": "2026-10-03T09:59:25.754Z", "callId": "toolu_01QwbFF1AbSQABakY34drHP5", "name": "Bash", "input": { "command": "python -c \"\nimport build, numpy as np, itertools\nz=np.load('sig_val.npz'); sig={k:z[k] for k in z}\nG=build.val_truth(sig)\nbest=(0,None)\nfor p,l in itertools.product([0.15,0.3,0.5,0.8],[0.7,1.0,1.5,2.0]):\n w=dict(pop=p,uucf=1,lk=l); r=build.ndcg10(sig, build.blend(sig,w), G)[0]\n print(w, round(r,5), flush=True)\n if r>best[0]: best=(r,w)\nprint('BEST',best)\n\"", "description": "Grid-search blend weights on validation", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T10:01:18.189Z", "content": "{'pop': 0.15, 'uucf': 1, 'lk': 0.7} 0.0337\n{'pop': 0.15, 'uucf': 1, 'lk': 1.0} 0.03454\n{'pop': 0.15, 'uucf': 1, 'lk': 1.5} 0.03355\n{'pop': 0.15, 'uucf': 1, 'lk': 2.0} 0.03316\n{'pop': 0.3, 'uucf': 1, 'lk': 0.7} 0.03324\n{'pop': 0.3, 'uucf': 1, 'lk': 1.0} 0.03411\n{'pop': 0.3, 'uucf': 1, 'lk': 1.5} 0.03412\n{'pop': 0.3, 'uucf': 1, 'lk': 2.0} 0.03352\n{'pop': 0.5, 'uucf': 1, 'lk': 0.7} 0.03327\n{'pop': 0.5, 'uucf': 1, 'lk': 1.0} 0.03363\n{'pop': 0.5, 'uucf': 1, 'lk': 1.5} 0.03391\n{'pop': 0.5, 'uucf': 1, 'lk': 2.0} 0.03386\n{'pop': 0.8, 'uucf': 1, 'lk': 0.7} 0.03295\n{'pop': 0.8, 'uucf': 1, 'lk': 1.0} 0.03359\n{'pop': 0.8, 'uucf': 1, 'lk': 1.5} 0.03343\n{'pop': 0.8, 'uucf': 1, 'lk': 2.0} 0.03392\nBEST (np.float64(0.03453778585602249), {'pop': 0.15, 'uucf': 1, 'lk': 1.0})", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T10:01:18.192Z", "data": { "type": "total_tokens_reminder", "text": "14953486 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T10:01:18.193Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-10-03T10:01:22.528Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T10:01:22.528Z", "text": "The gains from weight tuning are tiny, within noise. With limited time left, I'll try one alternative: sharper neighbour weighting and a shorter candidate window, rebuild and evaluate that, then decide whether to commit to it before writing the README.\n\n" }, { "kind": "message", "timestamp": "2026-10-03T10:01:22.529Z", "text": "Weight differences are within noise. With the remaining time I'll test one alternative neighbour configuration (sharper similarity, fewer neighbours, shorter candidate decay) and keep whichever wins." }, { "kind": "tool", "timestamp": "2026-10-03T10:01:24.359Z", "callId": "toolu_01E1jd6GgG2AGgusErnQjrv3", "name": "Bash", "input": { "command": "python -c \"\nimport build, numpy as np\nP=dict(build.DEFAULT); P.update(knn=100, sim_pow=2.0, cand_tau=14)\nsig=build.build_signals('val', P)\nG=build.val_truth(sig)\nfor w in [dict(pop=0.15,uucf=1,lk=1), dict(pop=0.3,uucf=1,lk=1.5)]:\n print('ALT', P, w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\n\"", "description": "Evaluate alternative neighbour parameters on validation", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T10:02:11.228Z", "content": "[val] signals built in 34s\nALT {'pop_tau': 14, 'pop_rec_w': 0.3, 'hist_tau': 60, 'hist_rec_w': 0.5, 'idf_pow': 0.5, 'knn': 100, 'sim_pow': 2.0, 'cand_tau': 14, 'cand_rec_w': 0.5} {'pop': 0.15, 'uucf': 1, 'lk': 1} (np.float64(0.02762576089150206), np.int64(4263))\nALT {'pop_tau': 14, 'pop_rec_w': 0.3, 'hist_tau': 60, 'hist_rec_w': 0.5, 'idf_pow': 0.5, 'knn': 100, 'sim_pow': 2.0, 'cand_tau': 14, 'cand_rec_w': 0.5} {'pop': 0.3, 'uucf': 1, 'lk': 1.5} (np.float64(0.02611927965024596), np.int64(4263))", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T10:02:11.231Z", "data": { "type": "total_tokens_reminder", "text": "14952354 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T10:02:11.233Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-10-03T10:02:16.040Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T10:02:16.040Z", "text": "That result was worse, so I'll keep the defaults and try the opposite direction instead (knn=500, sim_pow=1, cand_tau=60) with limited time left. I'll run one more build and eval, and if it improves, rebuild for test while drafting the README in parallel to save time.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T10:02:16.044Z", "text": "The sharper config performed worse, so I'll keep the defaults. I'll try one more variant with more neighbours and longer decay, while also drafting the README and running the public evaluator on validation rankings as a sanity check.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T10:02:17.194Z", "callId": "toolu_01REsGgke7miz2HHp2cyP9nj", "name": "Bash", "input": { "command": "python -c \"\nimport build, numpy as np\nP=dict(build.DEFAULT); P.update(knn=500, cand_tau=60)\nsig=build.build_signals('val', P)\nG=build.val_truth(sig)\nfor w in [dict(pop=0.15,uucf=1,lk=1), dict(pop=0.3,uucf=1,lk=1.5)]:\n print('ALT2', w, build.ndcg10(sig, build.blend(sig,w), G), flush=True)\n\"", "description": "Evaluate wider-neighbourhood parameters on validation", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T10:03:48.500Z", "content": "[val] signals built in 76s\nALT2 {'pop': 0.15, 'uucf': 1, 'lk': 1} (np.float64(0.02907243409790724), np.int64(4263))\nALT2 {'pop': 0.3, 'uucf': 1, 'lk': 1.5} (np.float64(0.029917715263489998), np.int64(4263))", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T10:03:48.503Z", "data": { "type": "total_tokens_reminder", "text": "14951508 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T10:03:48.505Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 17, "role": "agent", "timestamp": "2026-10-03T10:03:55.073Z", "blocks": [ { "kind": "reasoning", "timestamp": "2026-10-03T10:03:55.073Z", "text": "This setting is also worse, so I'll stick with DEFAULT using WTS pop=0.15, uucf=1, lk=1, since the difference is marginal and within noise even though the existing submission uses pop=0.3. With about 5 minutes left and a ~50s rebuild time, I'll update WTS, rebuild the test, and write the README in parallel.\n\nI'm weighing whether to also rerun validation.parquet, but running two concurrent builds risks OOM and could corrupt the submission, so safer to build the test to a temp file first then move it. I'll skip the validation rerun since I already have the numbers from the in-process run.\n\n" }, { "kind": "reasoning", "timestamp": "2026-10-03T10:03:55.076Z", "text": "The default signals outperformed both alternatives, so I'll finalize with those and the best validated blend weights—rebuilding the test submission to a temp file first to keep the saved one intact—then write the README.\n\n" }, { "kind": "tool", "timestamp": "2026-10-03T10:04:06.912Z", "callId": "toolu_011qGMvgokCUsatbixa5QxCZ", "name": "Bash", "input": { "command": "sed -i \"s/^WTS = .*/WTS = dict(pop=0.15, uucf=1.0, lk=1.0)/\" build.py && sed -i \"s#top100(sig, sc).to_parquet(OUT + 'submission.parquet', index=False)#top100(sig, sc).to_parquet(OUT + 'submission_tmp.parquet', index=False)#\" build.py && python build.py test && python -c \"\nimport pandas as pd, numpy as np, os\ndf=pd.read_parquet('submission_tmp.parquet'); assert df.shape==(845600,3) and df.uid.nunique()==8456 and df.duplicated(['uid','item_id']).sum()==0\nassert (df.groupby('uid')['rank'].agg(['min','max','count'])==[1,100,100]).all().all()\nz=np.load('/task/data/test_eligible_candidates.npz'); m=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').astype(bool)\nur={u:i for i,u in enumerate(z['uids'])}; ic={it:i for i,it in enumerate(z['item_ids'])}\nassert all(m[ur[u],ic[i]] for u,i in zip(df.uid,df.item_id))\nos.replace('submission_tmp.parquet','submission.parquet'); print('submission updated OK')\n\" && sed -i \"s#OUT + 'submission_tmp.parquet'#OUT + 'submission.parquet'#\" build.py && rm -f sig_val.npz && cat > README.md <<'EOF'\n# Made-for-You discovery ranker\n\n`build.py val` builds validation signals from `train_*` only, reports in-process graded\nNDCG@10 against `val_*` outcomes, and writes `validation.parquet`.\n`build.py test` refits the same pipeline on `train_* + val_*` and writes `submission.parquet`\n(100 eligible items per target user, ranks 1-100).\n\n## Model\nPer cutoff, three history-only signals are computed on the 30k-track catalog and blended by a\nweighted sum of per-user log-scaled scores, masked by the cutoff's eligibility bitmask:\n\n- **pop** (w=0.15): time-decayed (tau=14d) organic-weighted popularity of positive (>=50%) listens.\n- **uucf** (w=1.0): user-kNN collaborative filtering. User profiles = time-decayed (tau=60d),\n log-scaled, IDF-weighted, L2-normalised positive listens over the full item universe (organic\n listens weighted 2x recommended). Top-200 cosine neighbours; score = neighbours' time-decayed\n (tau=30d) positive listens on catalog items.\n- **lk** (w=1.0): same neighbours applied to neighbours' time-decayed likes of catalog items.\n\n## Validation (train-only history, val-window organic labels, 4263 scored users)\n| config | NDCG@10 |\n| --- | --- |\n| popularity only | 0.0220 |\n| uucf only | 0.0308 |\n| likes-kNN only | 0.0279 |\n| blend pop=0.3, uucf=1, lk=1 | 0.0341 |\n| **blend pop=0.15, uucf=1, lk=1 (selected)** | **0.0345** |\n| knn=100, sim^2, cand_tau=14 | 0.0276 |\n| knn=500, cand_tau=60 | 0.0299 |\n\nMemory note: the container has an 8GB limit; the pipeline keeps everything in float32 and uses\nsparse-neighbour x dense-candidate products to stay under it (~45s per stage).\nEOF\nls -la /app/solution", "description": "Rebuild final test submission with selected weights, validate, and write README", "timeout": 600000 }, "result": { "timestamp": "2026-10-03T10:05:01.375Z", "content": "[test] signals built in 46s\nsubmission updated OK\ntotal 1648\ndrwxr-xr-x 1 agent agent 4096 Oct 3 10:05 .\ndrwxr-xr-x 1 agent agent 4096 Oct 3 09:48 ..\n-rw-r--r-- 1 agent agent 1602 Oct 3 10:05 README.md\n-rw-r--r-- 1 agent agent 7570 Oct 3 10:05 build.py\n-rw-r--r-- 1 agent agent 1661140 Oct 3 10:04 submission.parquet", "isError": false } }, { "kind": "context", "timestamp": "2026-10-03T10:05:01.378Z", "data": { "type": "total_tokens_reminder", "text": "14949672 tokens left" } }, { "kind": "context", "timestamp": "2026-10-03T10:05:01.380Z", "data": { "type": "batching_reminder_sent", "text": "First privately list what you need next; then request every item that doesn't depend on another's result in this one response.", "model": "claude-fable-5-1", "clearAt": "next_user_message" } } ] }, { "id": 18, "role": "agent", "timestamp": "2026-10-03T10:05:08.282Z", "blocks": [ { "kind": "message", "timestamp": "2026-10-03T10:05:08.282Z", "text": "The final submission is in place and verified.\n\n**Deliverable**: `/app/solution/submission.parquet` has exactly 100 ranked, eligible, duplicate-free items for each of the 8456 target users, refit on train plus validation history. The build code is in `/app/solution/build.py` and the README documents the approach and results.\n\n**Model**: a blend of three history-only signals, masked by the cutoff's eligibility set.\n\n- Time-decayed, organic-weighted popularity of positive listens.\n- User-kNN collaborative filtering over the full item universe (decayed, IDF-weighted cosine profiles, top-200 neighbours), scored on neighbours' positive listens of catalog tracks.\n- The same neighbours applied to their likes of catalog tracks.\n\n**Validation results** (train-only history, organic val-window labels, 4263 scored users):\n\n| Config | NDCG@10 |\n| --- | --- |\n| Popularity only | 0.0220 |\n| User-kNN only | 0.0308 |\n| Selected blend (pop 0.15, uucf 1, likes 1) | 0.0345 |\n| Sharper neighbours (k=100, sim², 14d decay) | 0.0276 |\n| Wider neighbours (k=500, 60d decay) | 0.0299 |\n\n**Caveats**: blend-weight differences among the top settings were within about 0.001 and likely noise. The container's 8GB memory limit caused early OOM kills, which I fixed by keeping all matrices in float32 and using sparse-by-dense products. I did not rerun the public evaluator script on a validation file for the final weights because of the time budget. The in-process NDCG matches the evaluator's definition and user count." } ] } ] }