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概要と使いどころ
Review other contributors' PRs to learn the codebase and build maintainer trust. Guides structured diff reading, helps formulate review comments, and teaches how to distinguish blocking issues from nits. Use when you want to learn a codebase by reviewing PRs, build trust with maintainers before contributing code, or practice code review on OSS repos. Not for reviewing your own PR — use oss-submit-pr for that.
日本語の概要は準備中です。原文の説明を表示しています。
Insert, format, and number math formulas in Word and LaTeX academic papers. Supports Chinese core journals and international journal formats. Invoke when user needs to insert LaTeX formulas into Word (OMML), validate formula formats per journal standards, or auto-number equations.
日本語の概要は準備中です。原文の説明を表示しています。
Insert, format, and cross-reference academic citations in Word (.docx) documents. Supports GB/T 7714-2015, IEEE, APA 7th.
日本語の概要は準備中です。原文の説明を表示しています。
Insert, format, and cross-reference academic citations in Word (.docx) documents. Supports GB/T 7714-2015 (Chinese), IEEE, and APA 7th.
日本語の概要は準備中です。原文の説明を表示しています。
Inserts, formats, and cross-references academic citations in docx. Invoke when user needs to insert references, fix citation order, add cross-reference hyperlinks, or reformat bibliography to Chinese (GB/T 7714) or international (IEEE/APA) standards.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to benchmark and improve an existing repo skill through repeated evals, prompt mutations, and score tracking.
日本語の概要は準備中です。原文の説明を表示しています。
Skill Optimization: Auto Research. Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-source comprehensive research using perplexity-researcher, claude-researcher, and gemini-researcher agents. Launches up to 10 parallel research agents for fast results. USE WHEN user says 'do research', 'research X', 'find information about', 'investigate', 'analyze trends', 'current events', or any research-related request.
日本語の概要は準備中です。原文の説明を表示しています。
Use when preparing to engage a target account, researching a company before outreach, or building pre-call intelligence. Triggers: 'research this company', 'account brief for [company]', 'company research', 'what do I need to know about [company]', 'pre-outreach research'.
日本語の概要は準備中です。原文の説明を表示しています。
Build an AEO-first topical map optimized for AI search citation — entity clusters, query fan-out coverage, information gain scoring, and multi-platform distribution. Produces entity map, content node architecture, schema blueprint, and 90-day publishing calendar. Use when "topical map", "content architecture", "site structure", "topic clusters", "pillar content plan", "AEO map", "AI search architecture", "entity map", "what content should we build", or any request to plan a site's topical structure for AI search visibility.
日本語の概要は準備中です。原文の説明を表示しています。
Run a learning retrospective on the Kai harness. Mines gate-failure logs and 30-day performance results into lessons, triages candidate lessons (promote/keep/retire), and graduates repeated lessons into enforced gate checks with golden corpus cases. Use when "retro", "what have we learned", "triage lessons", "promote lessons", "why does this keep failing", "harness retrospective", or monthly / after any heavy content sprint.
日本語の概要は準備中です。原文の説明を表示しています。
Analyze content performance patterns. Extract what works (hook types, formats, personas). Auto-update learned defaults so future content improves.
日本語の概要は準備中です。原文の説明を表示しています。
Write content using the most recent brief. Loads framework rules, persona hooks, learned patterns, and skill contract. Auto-runs quality gate on completion.
日本語の概要は準備中です。原文の説明を表示しています。
Produce customer case studies from interviews or data — Problem, Solution, Results structure with perception engineering and quality gates. Use when "case study", "customer story", "testimonial", "success story", "client results", or any request to document a customer win.
日本語の概要は準備中です。原文の説明を表示しています。
Convert deep research output (Claude Research, ChatGPT Deep Research, Gemini, Codex — any long-form MD/Word/text) into a consulting-quality HTML report: pyramid structure, numbers promoted to KPIs/charts, comparisons turned into framework tables, citations preserved. Use when the user provides research/investigation results and says レポートにして/HTMLにまとめて/資料にして/きれいにして, mentions ディープリサーチの結果/調査結果/リサーチ結果, or asks to research a topic AND deliver a report. NEVER invents numbers or facts not present in the source (盛らない原則).
Use when 高教社杯/CUMCM mathematical modeling papers need lightweight literature search, citation verification, method/background references, standards or policy sources, Chinese-English source routing, DOI/BibTeX checks, or claim-to-citation mapping without fabricated sources.
日本語の概要は準備中です。原文の説明を表示しています。
Working memory protocol — maintains context across sessions via CONTINUITY.md. Inspired by Loki Mode. Read at turn start, update at turn end. Captures mistakes and learnings to prevent repeating errors.
日本語の概要は準備中です。原文の説明を表示しています。
Convert a book or long document into a reusable AI mentor with source-grounded lessons, guided practice, citations, and persistent learning records.
日本語の概要は準備中です。原文の説明を表示しています。
Extracts structured citation data from academic repositories using BeautifulSoup4 with lxml parser. Parses DOI metadata, author affiliations, and reference lists from PubMed, arXiv, and Semantic Scholar HTML.
日本語の概要は準備中です。原文の説明を表示しています。
Score deep research agents on benchmark tasks using factual verification, report-quality scoring, and process evaluation before model or workflow changes ship.
日本語の概要は準備中です。原文の説明を表示しています。
Use NotebookLM through MCP or a local REST API to run cited Q&A, generate Studio artifacts, and manage high-volume research batches.
日本語の概要は準備中です。原文の説明を表示しています。
Lets agents operate Google NotebookLM/Gemini Notebook through a Python API, CLI, skill, MCP server, or REST server to create notebooks, add sources, ask cited questions, generate artifacts, and download exports.
日本語の概要は準備中です。原文の説明を表示しています。
Builds citation networks from Semantic Scholar API and CrossRef DOI metadata. Visualizes paper influence graphs using NetworkX, identifies seminal works, and tracks research lineage across fields.
日本語の概要は準備中です。原文の説明を表示しています。
Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.
日本語の概要は準備中です。原文の説明を表示しています。