2,669 件 ・ 関連度順
概要と使いどころ
Use when a problem requires structured step-by-step reasoning before acting. Prevents jumping to conclusions or guessing root causes without evidence. Triggers on complex debugging, multi-step analysis, hypothesis verification, or when mk:fix invokes diagnosis phase. NOT for being stuck on approach (too many implementations, forced assumptions, innovation block) — use mk:problem-solving instead.
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Use when a decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).
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Task-sized forensic analysis of Vivado synthesis and routed timing evidence for processor RTL. Use when Codex must trace a named setup or hold path, audit whole-design timing populations, compare implementation runs, map primitive, LUT, CARRY, BRAM, DSP, MUXF, register, and routed-net stages back to Chisel or generated RTL, distinguish logic from routing pressure, identify missing DCP queries, or write an evidence-backed timing report. This skill is read-only by default and stops at ranked modification directions. Use optimize-chisel-fpga-timing for RTL edits and routed A/B implementation closure.
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OpenClaw-native SIEGER-aligned customer intelligence. Use a structured evidence bundle to produce a bilingual report with claim-level evidence, decision gates, industry-specific analysis, and proposed sales angles that require explicit approval before email drafting.
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Use for analytical thinking, or when a question needs breaking down and evidence: "why did metric X drop?", "what drives this?", "break this down", "what data do we need?". Builds a tree, hypotheses and evidence, ends with an answer and key uncertainty. Skip for quick verdicts.
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Review a small code or documentation change and return only evidence-backed correctness findings. Use in the Codex How To local plugin lab; do not modify files or post remote comments.
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Builds the "Report Sources" child page for a finished Weekly Community Signal report — an evidence-backed defense of WHY every item landed in the column/section/rank it did. The agent argues each placement like a lawyer (claim → evidence → rule → rebuttal) but is under oath: it CANNOT invent, spin, or overstate — every claim ties to a verifiable fact (issue #, label, PR type, quoted comment, npm output, the front-door test). Not CYA — it's so Nathan can defend the report with facts and see the exact reasoning behind each call. Runs after the report (invoked by weekly-report, last steps) and on "report sources", "defend the report", "why is this here".
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Annotated Documents: optional experimental application for standalone HTML with evidence. Use when the user explicitly requests Annotated Documents or a kpopper HTML document with evidence, refreshes an existing kpopper document, or has a standing preference for this application. Generic HTML requests do not select it. Install the HTML runtime before use. Not for the record's own page or an ordinary website. Requests come in any language.
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Use when claiming 'done', suggesting a commit, push, or PR — runs the evidence gate so completion claims come from fresh output in this message, not memory or earlier runs.
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Run a Mixture of Experts (MoE) audit on any topic, plan, codebase, evidence archive, or process. Dynamically spawns specialized subagents with different critical lenses to cross-reference findings and detect flaws, overlap, retention issues, or drift. Use when designing architectures, analyzing complex code, verifying multi-step plans, classifying evidence artifacts, or looking for duplicated intent in a repo.
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Medical evidence grading orchestrator. 当用户需要评估医学文献的证据强度、比较多篇论文的质量、查找某疾病的高等级证据(指南或 RCT)、或验证临床声明的证据支持度时,调用本 skill。整合 6 个原子 skill 的检索结果,按证据金字塔自动分级为 A/B/C/D。优先使用 BioMCP fast-path(~3-5s),无 BioMCP 时回退 slow-path(~15-25s)。
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Europe PMC RESTful Web Service 多源文献聚合检索原子 skill。**使用本 skill 进行医学文献广召回 / RAG 知识库构建 / 跨库聚合 / 预印本扫描 / 全文链接发现**。一次调用同时召回 PubMed (MED) + PMC + AGRICOLA + CABI + 预印本(bioRxiv / medRxiv / ChemRxiv / Research Square)+ NCBI Bookshelf + 专利,自动按 PMID/PMCID/DOI 三级去重,返回完整元数据(全文 OA 链接、citations、grants 资助信息)。**定位:广召回 · 多源 · RAG 友好**(与 pubmed-eutils 互补 — 后者做单源精准官方检索 + Clinical Queries;本 skill 做"撒网"式多源聚合)。触发词:Europe PMC / EuropePMC / 多源文献聚合 / 广召回 / RAG 召回 / RAG 知识库 / 预印本搜索 / preprint search / bioRxiv / medRxiv / ChemRxiv / DOI 检索 / DOI lookup / 全文链接 / full-text links / OA 全文 / PMCID / 灰色文献 / grey literature / 跨库去重 / 资助检索 / grant search / NIH funded / Wellcome funded / citations metadata。**不做**:NCBI E-utilities 直检(→ pubmed-eutils)、临床试验注册(→ clinical-trials-v2)、全文 XML 抽取(→ bioc-fulltext-fetch)、实体识别(→ pubtator-entity-search)、证据分级(→ medical-evidence-grading)、参考文献终态写入(→ evidence-appendix-sync)。
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当用户询问 ClinicalTrials.gov / 临床试验 / NCT / 试验注册 / 招募状态 / phase 1/2/3 / RCT lookup / sponsor 检索 / interventional trial / observational study / 主次要终点 / trial outcomes / 试验招募中 / recruiting status 时,通过 ClinicalTrials.gov API v2 实时检索官方注册数据。专注实时单次查询(≤1000 条/请求),典型场景: "某 NCT 试验最新状态"、"BTK 抑制剂 Phase 3 在招募试验"、"某试验已发布的主次要终点结果"。明确边界: 批量 >10k 试验 / 全库 SQL 历史分析 → aact-bulk-trials; 关联 PubMed 文献 → pubmed-eutils; RCT 自动 GRADE 评级 → medical-evidence-grading; 引用入报告附录 → evidence-appendix-sync。跨平台无锁(Claude Code / Codex / Cursor 通用)。
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Run evidence-disciplined Xiaohongshu social listening for foreign pharma employee pain points. Use when the user asks to collect and analyze notes/comments about pharma companies, aliases, roles, compliance, medical affairs, or AI topics, then produce a cited and scored sampled-insight report. Includes independent-evidence counting, MediaCrawler batch collection, and CAPTCHA/461 fallback to Agent Reach/OpenCLI.
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Evidence-grounded AI research workflow for turning supplied economics, finance, management, and social-science papers or structured records into versioned PaperReading artifacts. Use when Codex must ingest text, Markdown, or a text-based PDF; separate source-grounded claims from researcher analysis; bind findings to evidence IDs and locators; review extraction candidates; migrate v0.2 PaperRecord JSON; validate or verify a v0.3 PaperPackage; generate research extensions; export reviewable JSON or Markdown; or safely update a compatible 13-field literature workbook.
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Design, audit, harden, review, or upgrade MCP servers and MCP Apps with revision-aware protocol profiles, explicit HTTP/deployment boundaries, capability-centered validation, bilingual reporting, and reproducible evidence. Use for MCP server architecture, JSON-RPC or Streamable HTTP bugs, security hardening, protocol-version migration, MCP Apps widget boundaries, review reconciliation, test planning, and public engineering evidence.
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Analyze, design, write, revise, translate, and review empirical or technical research papers through an evidence-bounded dialectical workflow. Use when Codex must develop a research question, integrate reference papers with a user's ideas, build phenomenon-mechanism-conditions reasoning, design methods or experiments, restructure or edit a manuscript, translate Chinese academic text into restrained English, answer reviewers, or run multi-round peer-review convergence without fabricating evidence.
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Foundational ethics, authority hierarchy, and structural principles for the Predicate agent. Always-on law (composable system-prompt core): Truth>Harmony, Evidence>Authority, Halt>Assumption, Outcomes>Process — four ordered principles that govern every walk. Reference (by-moment): conflict resolution, ethics adjudication, novel situations, precedence walkthrough, entropy diagnostics, principled resistance. Trigger (reference depth): resolving rule conflicts, ethics calls, novel situations. Prompt contains: constitution, principles, precedence, truth over harmony, outcomes over process, evidence over authority, principled resistance.
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Build an evidence-backed working model of an existing codebase. Use when you must orient in a large or unfamiliar repository; explain architecture or a subsystem; trace a request, runtime, control, or data flow for a concrete question or bug; analyze a branch, commit, diff, or pull request for behavioral impact and blast radius; or assess AI-generated or changed code against requirements, contracts, invariants, tests, and runtime evidence. Default to read-only investigation. Do not trigger only because a coding task touches an existing repository; use when understanding or verification is the requested outcome or a substantial prerequisite.
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spec-drift issue が起票され diff トリアージが必要なとき、C11 が再構成した issue 単位の完全 diff を hunk 化し name/type/required/enum/semantics 各軸の影響を before/after/evidence 付きで判定して triage-report を出したいときに使う。
Default material, information-density hierarchy, clean-rendering, negative-prompt hygiene, and evidence-preserving photo-repair layer for IM2 / GPT Image 2 / gpt-image-2 / image_gen. Use for clean generation/redraws, artifact cleanup, focal-detail control, atmospheric depth, physically coherent light paths, air scattering, volumetric light, light ratio, material realism, architecture/terrain/hard-surface scene mass and structural grounding, and 企业/公司活动/门店/团队合影/商品/餐饮/证件文档照片修复. Preserve identity, headcount, placement, product facts, text, logo, QR, date, price, and scene evidence; prefer residual blur over invented restoration detail.
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Performs memory forensics analysis using Volatility 3 to extract evidence of malware execution, process injection, network connections, and credential theft from RAM dumps captured during incident response. Covers memory acquisition, process analysis, DLL inspection, and malware detection. Activates for requests involving memory forensics, RAM analysis, Volatility framework, memory dump investigation, volatile evidence analysis, or live memory acquisition.
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Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache investigation.
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Extract and analyze Windows Registry hives to uncover user activity, installed software, autostart entries, and evidence of system compromise.
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