Use when work has genuinely independent streams or distinct builder, evaluator, domain, and integration roles that require bounded multi-agent command scaled from 5 to 100+ agents.
中文简介正在准备中。当前显示原文说明。
Use when a decision-relevant question needs multi-source search, claim-level citations, contradiction handling, uncertainty, or a durable wiki handoff.
中文简介正在准备中。当前显示原文说明。
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
<skill_contract> <input>Decision-relevant question, audience, scope, recency, source and privacy boundaries, budget, and required deliverable.</input> <output>An answer-first synthesis with source and claim ledgers, citations, contradictions, uncertainty, and durable handoff.</output> <done>Decision-critical claims are traceable and adversarially checked, unresolved gaps are explicit, and the stop rule is met.</done> <non_goals>Link collection, uncited material claims, privacy leakage, exhaustive search, or autonomous high-impact experiments.</non_goals>
Research is an evidence loop, not a volume contest. Define the decision, gather the minimum diverse evidence, maintain source and claim ledgers, attack the synthesis, then stop when additional search no longer changes the answer.
Provide: research question, audience/decision, scope, recency, allowed/excluded sources, privacy boundary, budget, output format, and wiki destination if durable. Load references/research-ledgers.md for ledger schemas.
Choose one mode: evidence brief, knowledge curation, recency pulse, domain intelligence, scientific-method audit, or heavy research. Convert the question into 3-7 information requirements, decision criteria, interruption points, and a source-access plan.
</intake><unknowns_gate>
If the decision, recency window, private-data authority, or source boundary materially changes the result, return NEEDS_INPUT. Record known, probeable, testable, and inaccessible unknowns. Do not treat inaccessible sources as supporting evidence.
</unknowns_gate>
<execute>wiki-ingest rather than writing source claims directly into governed concepts.For high-stakes conclusions, use independent review. Obtain approval before accessing private systems, publishing, or launching experiments. Any durable write needs a diff-based rollback path.
</execute> <evaluate>Audit every material claim against the ledger. Check citation entailment, source independence, freshness, contradiction coverage, privacy, and whether the recommendation changes under plausible uncertainty. Stop when new searches repeat known evidence or cannot change the decision.
</evaluate><retry_policy>
max_attempts: 3 research passes: discovery, gap-fill, adversarial. A retry must target a named gap with a changed query/source class. Stop on duplicate evidence, repeated access failure, or NO_PROGRESS.
</retry_policy>
<state_contract>
Persist {run_id, status, attempt, budget, evidence, unknowns, last_error, next_action} plus question version, mode, information requirements, source/claim ledgers, search trace, contradictions, privacy decisions, approval, draft version, and rollback/write receipts.
</state_contract>
NEEDS_INPUT: scope, decision, privacy, or freshness boundary is ambiguous.INSUFFICIENT_EVIDENCE: sources cannot support the requested confidence or recommendation.BLOCKED_PERMISSION: private or restricted evidence is unavailable; exclude it explicitly.VERIFY_FAILED: citation does not entail the claim or key contradiction is unresolved.NO_PROGRESS: a changed pass yields no decision-relevant evidence.BUDGET_STOP: synthesize covered scope and list gaps. max_attempts: 3.Return status, result (answer-first synthesis and recommendation), evidence (claim/source ledgers and citations), unknowns, and next_action including approval or wiki handoff.
</skill_contract>
还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
Use when work has genuinely independent streams or distinct builder, evaluator, domain, and integration roles that require bounded multi-agent command scaled from 5 to 100+ agents.
中文简介正在准备中。当前显示原文说明。
Use when designing or refactoring a model-native engineering workflow with bounded autonomy, probes, custom evaluation, durable state, and verified write-back.
中文简介正在准备中。当前显示原文说明。
Use when property-service operations need an AI plus ontology plus DMAIC design for work orders, dispatch, quotes, evidence, CTQ metrics, and control dashboards.
中文简介正在准备中。当前显示原文说明。
Use when a personal or team operating system needs a bounded redesign using Four-C, closed-loop controls, 70/30 allocation, 3B creativity, experiments, and prediction-error learning.
中文简介正在准备中。当前显示原文说明。
Use when a goal must be converted into a repeatable behavior, cue, SOP, review cadence, and identity-aligned reinforcement.
中文简介正在准备中。当前显示原文说明。
Use when source material must be transformed into a compact, evidence-aware model for learning, decisions, or an Obsidian concept note.
中文简介正在准备中。当前显示原文说明。