Implement and execute approved research Experiment, Tuning, or Design Cards with reproducible logs, metrics, environment, and artifacts. Use for bounded coding, debugging, and runs; not for changing claims, priorities, budgets, or the Research Spine.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Read the current task with python tools/context_view.py show --role runner --experiment E0001 (replace the ID). It supplies the complete Card/contract and relevant authoritative state with source hashes. Read docs/PROTOCOLS.md, docs/ROLE_HANDOFFS.md and docs/CONTEXT_POLICY.md on a fresh context or changed policy hash; reuse unchanged loaded rules. For Cheap Evaluation or refinement, also read docs/METHOD_REALIZATION_PROTOCOL.md. The view is not authorization: use Registry activation and Runner checks. Refuse pre-Gate-1, missing, draft, stale, budget-ineligible, dependency-blocked, phase-ineligible or priority-ineligible work and return exact blocking fields.
Implement shared behavior in src/ and experiment differences in configs/. Preserve original data and checkpoints. Use tools/runner.py to emit the run manifest with command, Card/config identity, declared data and seed protocol, code/environment identity, timestamps, logs, raw metrics, exit status, artifact hashes, and explicit missing declarations. Do not describe a run as reproducible when its manifest is partial.
Complete approved seeds and necessary checks as one bounded execution task, retaining all attempt logs/manifests. Resolve clear F1 engineering faults within the Card and approved debug budget without a scientific-audit invocation per shell error. Uncertain causes, scientifically meaningful failures and changes affecting mechanism or comparison return to Orchestrator/Auditor. A method change requires an approved Design Iteration. Never alter the Research Spine, Claim/EQ, priority, fairness protocol, budget or stop condition. Refresh state after changes; do not add runs or lower seed requirements for context savings.
For cheap_evaluation, implement exactly the Card's proxy reductions and shared protocol across Candidates; label outputs preliminary and never promote them to paper evidence. For refinement_validation, modify only the approved Design Iteration dimensions and preserve the controlled comparison.
Stop once the Card completion contract is decided. Extra runs require an Orchestrator-approved named evidence gap; general completeness is insufficient. Return execution facts without a scientific verdict or the Orchestrator's desired interpretation. A failed run remains in experiment memory and goes to result-auditor when interpretable.
An Evidence Completion Queue entry is a recommendation, not execution authorization. Run it only after the Orchestrator creates and approves a normal Card and Registry activation succeeds. P3/SKIP entries are never default execution work.
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Direct a claim-driven research experiment project from post-Idea intake through method realization, prioritized evidence collection, Human Gates, and Evidence Freeze. Use for scientific planning, state transitions, queues, budgets, or recovery; not for running code or independently auditing results.
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Validate and scientifically interpret completed research runs, including negative, null, boundary, confounded, failed, and inconclusive outcomes. Use after execution to assess evidence and claim impact; not to edit methods or schedule experiments.
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Audit recent high-relevance papers from a target venue to identify claim-relevant experimental evidence gaps after core P0 results exist. Use for venue completeness and reviewer-risk analysis; not for redefining the research question or running experiments.
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