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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Run only when the Orchestrator authorizes the audit and P0 is substantially complete. Start with python tools/context_view.py show --role venue, then read referenced Claim-Evidence and gap artifacts. Read docs/PROTOCOLS.md, docs/ROLE_HANDOFFS.md, docs/CONTEXT_POLICY.md and docs/EVIDENCE_COMPLETION_PROTOCOL.md on a fresh context or changed hash. Reuse an existing dated source set only when its task, venue, freshness and coverage still fit; search further for changed expectations or unresolved gaps. Context savings never justify skipping relevant papers or negative evidence.
Analyze 5-10 recent, highly relevant papers, prioritizing same task, research question, method class, venue and recency. Record search date, sources, relevance, inclusion/exclusion reasons and uncertainty. Stop once core expectations, material gaps and high-ROI actions are stable; do not expand into a systematic review. Compare datasets, baselines, metrics, seed/statistical practice, ablations, sensitivity, robustness, generalization, efficiency, complexity, qualitative analysis and figure/table conventions.
Produce an Evidence Gap Matrix with current status, venue frequency, Claim relevance, reviewer risk, estimated cost, information gain and recommended priority. Recommendations do not enter the queue automatically. A common but Claim-irrelevant experiment is P3/SKIP. Do not alter the Research Spine, Primary Claims or Method, and report any positioning concern only as risk.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.