Research & Learning skills

GitHub Issueの確認事項に対して、コードベースやドキュメントを徹底的に調査し、根拠に基づいた回答を提供するスキル。Issueの最後のコメントに含まれる確認事項を調査・回答し、コメントに追記する。

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用 AI 系统学习任何东西的教练。当用户要"学 XX""怎么入门""帮我制定学习计划""我想搞懂 XX""考证/备考""复习"时使用。不是直接讲答案，而是带着走完整的学习循环：定目标→输入→主动回忆→输出→纠错→间隔复习→项目交付。

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辅导中国中小学生作业的家长助手。当用户要"辅导作业""孩子这题不会""怎么给孩子讲""检查作业""孩子不想写作业"时使用。重点是教家长怎么引导，而不是直接给答案。

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Conduct comprehensive AI-powered research with citations via the Tavily CLI. Use this skill when the user wants deep research, a detailed report, a comparison, market analysis, literature review, or says "research", "investigate", "analyze in depth", "compare X vs Y", "what does the market look like for", or needs multi-source synthesis with explicit citations. Returns a structured report grounded in web sources. Takes 30-120 seconds. For quick fact-finding, use tavily-search instead.

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Retrieves scientific papers from PubMed and creates plain-language research summaries. Use when users ask about medical research, scientific studies, clinical trials, disease treatments, or want to understand recent scientific literature on any biomedical topic.

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Search PubMed biomedical literature with natural language queries powered by Valyu semantic search. Full-text access, integrate into your AI projects.

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Investigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.

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Produce comprehensive synthesis with explicit citations from multiple ordinary public-web sources via the Tavily CLI. Use for detailed reports, comparisons, market or literature research, and questions whose answer requires multi-source web evidence rather than a quick lookup. Do not use for simple discovery (tavily-search), one known URL (tavily-extract), one-site URL discovery or bulk extraction (tavily-map/crawl), or research centered on named social, community, career, code, media, authenticated, or cross-platform sources (Agent Reach). Takes 30-120 seconds.

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Phase 4 Turn 1 of disease-market-sizing-orchestration. Generates report HTML with mandatory citation anchors per fact claim. Reads contract + staging + sources/. Every drug/treatment/stat/recommendation MUST carry [pmid:.../guideline:.../nct:...] anchor that resolves to an actual file in sources/. NEVER hardcode drug names or numerical claims. Use AFTER evidence recall (phase 3), BEFORE content-verification-layer (Turn 2).

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Converts Markdown to styled HTML with WeChat-compatible themes. Supports code highlighting, math, Mermaid (rendered to PNG via headless Chrome), PlantUML, footnotes, alerts, infographics, and optional bottom citations for external links. Use when user asks for "markdown to html", "convert md to html", "md 转 html", "微信外链转底部引用", or needs styled HTML output from markdown.

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Cite-or-Block 架构的基石原子 skill。把报告里的 citation 锚点 (例如 [guideline:CSCO-2024-NSCLC:§5.5.2] / [pmid:12345678:abstract]) 解析为源文件片段,并核对事实声明里的关键词是否在引用源原文里出现。 Foundation atomic skill for the Cite-or-Block architecture. Resolves citation anchors (e.g. [guideline:CSCO-2024-NSCLC:§5.5.2] / [pmid:12345678:abstract]) to raw source text fragments, and verifies whether claim keywords actually appear in the cited source. 使用场景 / Use when: 1. 报告生成后要做 fact-check (resolve_citation + verify_claim_against_source) 2. content-verification-layer 要扫全报告锚点 (parse_citations_in_text) 3. drug-citation-verifier / 任何 cross-check skill 需要"锚点 → 源原文" P0 守护:本 skill **绝不维护任何已知字典**。源里没有 = verified=False, 无例外、无 fallback、无"已知答案兜底"。 P0 guard: this skill **never maintains any built-in dictionary**. Not in source = verified=False. No fallback, no built-in answers.

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Cite-or-Block 架构下的药名 cross-check 原子 skill。给定报告文本与 .cache/<slug>/sources/,扫描每个药品提及 → 找最近 citation 锚点 → 核对锚点指向的源里是否真的提到了此药(通用名 + 商品名)。无字典、无 fallback、源里没 = critical。

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通过 ClinicalTrials.gov API v2 实时检索官方临床试验注册数据。触发词包括 ClinicalTrials.gov、临床试验、NCT、试验注册、招募状态、phase 1/2/3、RCT lookup、interventional trial、observational study。专注实时单次查询(≤1000 条/请求),适合"某试验最新状态/招募信息/主次要终点"场景。批量历史分析请用 aact-bulk-trials,文献检索用 pubmed-search/europepmc。

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Multi-source deep research using firecrawl and exa MCPs. Searches the web, synthesizes findings, and delivers cited reports with source attribution. Use when the user wants thorough research on any topic with evidence and citations.

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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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Use when [trigger symptoms, user intents, task contexts]. Include Chinese trigger phrases when useful. Do not summarize workflow here.

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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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Read and analyze Xiaohongshu comments from the user's signed-in Chrome session. Use to collect, export, summarize, or classify comments for content planning, follow-up posts, audience research, reply strategy, and 引流文案。

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从对标账号导入 script + 数据 → 拆 pattern + 派生 base rubric 信号 → 写到 benchmark.md / script_patterns.md / rubric_notes.md。**这是工具最早期信号的来源**——cold-start 用户没自己历史时全靠对标，发过历史的用户也建议至少 1 个对标做 sanity check。触发词："学这个账号"/"拆这几个对标视频"/"learn from"/"导入对标账号"/"找对标"。

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GStack learn method for capturing reusable lessons, triggers, and future retrieval cues after debugging, implementation, or release work.

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Use when a user wants an editable PowerPoint or PPTX made from a scientific or academic paper, paper PDF, DOI, PMID, arXiv link, article text, abstract with figure legends, or research notes for a journal club, group meeting, lab meeting, paper sharing, thesis seminar, or research talk; also when they ask for 论文转PPT, 文献汇报, 组会PPT, or 论文解读PPT. Not for general-purpose slide decks, business presentations, slide-image reconstruction, or paper-search-only requests.

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Convert literature reviews, paper folders, thesis drafts, journal-style reports, or research outlines into rigorous academic slide decks and review artifacts. Use when Codex must create, rebuild, or polish PPTX presentations from scholarly sources while preserving academic logic, citation-aware image/formula handling, editable layouts, figure explanations, template fidelity, and export-based visual QA.

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Generate editable learning PPTX decks by first creating HTML slides, or convert local HTML slide decks and single-page HTML presentations into editable PPTX files by rendering in a browser, reading DOM element positions, extracting inline SVG assets, and rebuilding text, shapes, gradients, bullets, and icons as separated PowerPoint objects. Use when a user asks to generate PPT/PPTX from a topic, outline, course material, study notes, or structured content, or asks for pixel-level/browser-positioned HTML-to-PPT/PPTX conversion with editable/separate elements instead of one full-slide screenshot.

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論文・研究ノートの主張を根拠に対応させ、編集可能な日本語の研究発表を作る。

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