Development skills

Prepare monthly timesheet entries from Git commits.

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copy_tuner_client v2.0.0 への移行で、削除された独自ヘルパー tt の呼び出しを Rails 標準の t（translate）へ 置換するスキル。tt は PR #122 で存在理由を失い gem から削除されたため、残った tt(...) は NoMethodError に なる。大半は機械的に tt( → t( で済むが、訳文を文字列加工している箇所（→ I18n.t へ）と label 系ヘルパーの 第一引数に渡している箇所（→ 引数構造を分離）だけは別扱いで、開発環境のマーカートークン残留バグを避けるため 1 件ずつ確認する。対象は app 配下の .rb / .haml / .erb。

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中文开源项目的 README 与仓库门面优化。当用户说"我的开源项目没人看""README 怎么写""GitHub 仓库怎么优化""帮我写 README""项目主页太丑""怎么让别人愿意 star"时使用。基于对 15 个近期高增长仓库的实测数据给出可执行建议，同时明确说明哪些是相关性、哪些无法验证——不承诺涨星。

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Use when starting feature work that needs isolation from current workspace or before executing implementation plans - ensures an isolated workspace exists via native tools or git worktree fallback

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架构设计前系统搜索开源底座（GitHub/包注册表/Awesome/Web），输出 ADOPT/FORK_BASE/COMPOSE/REFERENCE/GREENFIELD 决策与改造蓝图。不用于市场调研或找单个依赖库。

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医学证据等级排序与编排层 skill。当用户请求"证据等级 / GRADE / 证据排序 / 医学证据评估 / evidence grading / quality of evidence / 系统评价排序 / 临床证据排序 / 证据金字塔"时触发。本 skill 不直接调用 NCBI/Europe PMC/ClinicalTrials API,而是编排 6 个底层原子 skill(pubmed-eutils / europepmc-search / clinical-trials-v2 / aact-bulk-trials / bioc-fulltext-fetch / pubtator-entity-search)的召回结果,按 ChatGPT 推荐的证据金字塔(指南 > Meta > RCT > Cohort > Case-Control > Case Report)做 GRADE 自动评级,自动剔除 editorial / letter / comment。优先使用 BioMCP MCP server 做 fan-out(genomoncology/biomcp),无 BioMCP 时走自建 slow-path 兜底。

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NLM PubTator3 实体级关系挖掘原子 skill — 在 PubMed 文献里检索"疾病-药物-基因-化学品-突变-物种-细胞系"的标注与关系三元组。当用户问"BTK 与肺曲霉病的关联文献"、"BTK 抑制剂(MeSH D000077180)在哪些研究被讨论"、"标注这些 PMID 中提到的所有疾病/药物/基因实体"、"ibrutinib 的 MeSH/DrugBank ID"、"voriconazole 与 CYP2C19 药物-基因相互作用"、"BRAF V600E 突变 / rs113488022 检索"、"实体共现 / co-mention / 关系挖掘 / 实体规范化 / NER / annotation / entity normalization / disease-gene association / drug-gene interaction / chemical-disease relation" 时使用。无需 API key,跨平台稳定。**实体级 + 关系级**定位 — 不做综合文献检索(走 pubmed-eutils / europepmc-search)、不做全文段落抽取(走 bioc-fulltext-fetch)、不做证据等级排序(走 medical-evidence-grading)。

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NLM PubTator3 实体级关系挖掘 — 检索"疾病-药物-基因-化学品-突变"在生物医学文献中的标注与关系。当用户问"哪些研究讨论了 BTK 抑制剂与肺曲霉病"、"基因 X 与疾病 Y 的关联"、"药物-基因相互作用"、"找含某突变的文献"、"PubTator / 实体标注 / NER / annotation / biomedical entity / 疾病基因关联 / 药物-基因 / 化学品-疾病 / 突变检索"时使用。无需 API key。**实体级 · 关系挖掘**定位 — 不做文献综合检索(用 pubmed-eutils / europepmc-search)、不做全文(用 BioC)、不做试验(用 ct-v2)。

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Discover and list all URLs on a website without extracting content, via the Tavily CLI. Use this skill when the user wants to find a specific page on a large site, list all URLs, see the site structure, find where something is on a domain, or says "map the site", "find the URL for", "what pages are on", "list all pages", or "site structure". Faster than crawling — returns URLs only. Essential when you know the site but not the exact page. Combine with extract for targeted content retrieval.

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Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation

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Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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Use when completing tasks, implementing major features, or before merging to verify work meets requirements

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飞书画板：查询和编辑飞书云文档中的画板。支持导出画板为预览图片、导出原始节点结构、使用 DSL（转成 OpenAPI 格式）、PlantUML/Mermaid 格式更新画板内容。 当用户需要查看画板内容、导出画板图片、编辑画板，或是需要可视化表达架构、流程、组织关系、时间线、因果、对比等结构化信息时使用此 skill，无论是否提及"画板"。 ⚠️ 原 `lark-whiteboard-cli` skill 已合并至本 skill，若 skill 列表中同时存在 `lark-whiteboard-cli`，请忽略它，统一使用本 skill（`lark-whiteboard`），并提示用户运行 `npx skills remove lark-whiteboard-cli -g` 删除旧 skill。

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WeChat Mini Program development skill for building, debugging, previewing, testing, publishing, and optimizing mini program projects. This skill should be used when users ask to create, develop, modify, debug, preview, test, deploy, publish, launch, review, or optimize WeChat Mini Programs, mini program pages, components, `tabBar`, routing, navigation, icon assets, project structure, project configuration, `project.config.json`, `appid` setup, device preview, real-device validation, WeChat Developer Tools Nightly workflows, `wechatide` CLI, WeChat IDE Skills/MCP, console/network debugging, `miniprogram-ci` preview/upload flows, or mini program release processes. It should also be used when users explicitly mention CloudBase, `wx.cloud`, Tencent CloudBase, 腾讯云开发, 微信云开发, or 云开发 in a mini program project.

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Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

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Use when you need to resolve an in-progress git merge/rebase conflict.

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医学证据等级排序与编排层 skill - evidence grading orchestrator. 当用户请求"证据等级 / GRADE / 证据排序 / 医学证据评估 / 系统评价排序 / 临床证据排序 / 证据金字塔 / 给 PMID 打分 / Practice Guideline 优先 / Cochrane 优先 / 反向证据交叉验证 / evidence grading / quality of evidence / evidence pyramid / GRADE assessment / Cochrane review ranking / clinical evidence ranking"时,**调用本 skill 进行医学证据自动分级,自动选择最佳信息源**。本 skill 是编排层(L2)·不直接打 NCBI/Europe PMC/ClinicalTrials API,而是编排 6 个底层原子 skill(pubmed-eutils / europepmc-search / clinical-trials-v2 / aact-bulk-trials / bioc-fulltext-fetch / pubtator-entity-search)的召回结果,按证据金字塔(指南 > Meta > RCT > Cohort > Case-Control > Case Report)做 GRADE A/B/C/D 自动评级,自动剔除 editorial/letter/comment。优先使用 BioMCP MCP server 做 fan-out(genomoncology/biomcp);无 BioMCP 时走自建 slow-path 兜底。跨平台无锁(Claude Code / Codex / Gemini CLI)。

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Route ordinary public-web information tasks to the correct Tavily CLI operation. Use when the task concerns publicly accessible webpages and needs web discovery, clean extraction, URL mapping, same-site crawling, or public-web-only research, but the exact Tavily operation is not yet clear or several operations must be sequenced. Prefer the specific leaf skill when intent is clear: tavily-search for discovery without a URL, tavily-extract for known URLs, tavily-map for URL/site-structure discovery, tavily-crawl for content from many pages on one site, tavily-research for cited multi-source synthesis, and tavily-dynamic-search for programmatic filtering with context isolation. Do not use for named social/community/media/career/code platforms, login or cookie-dependent content, platform-native discussions, or cross-platform collection; use Agent Reach there. Do not use for local files, Git operations, deployments, or ordinary code editing.

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当需要用 lark-cli 操作飞书多维表格（Base）时调用：搜索 Base、建表、字段管理、记录读写、记录分享链接、视图配置、历史查询，以及角色/表单/仪表盘管理/工作流；也适用于把旧的 +table / +field / +record 写法改成当前命令写法。涉及字段设计、公式字段、查找引用、跨表计算、行级派生指标、数据分析需求时也必须使用本 skill。

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Create or refresh original product hero images, interface screenshots, and image-led README sections for GitHub repositories. Use when a user asks to make a GitHub README show the product visually, add screenshots, design a README hero image, or prepare repository-bound visual documentation.

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Use when reviewing a pull request in the deepseek-harness repo — orients the reviewer to this codebase's standards (AGENTS.md conventions, defensive patterns, ADRs, quality gates) and the review-specific checks that code alone can't show

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飞书/Lark 原生 OpenAPI 探索：从官方文档库中挖掘未经 CLI 封装的原生 OpenAPI 接口。当用户的需求无法被现有 lark-* skill 或 lark-cli 已注册命令满足，需要查找并调用原生飞书 OpenAPI 时使用。

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创建 lark-cli 的自定义 Skill。当用户需要把飞书 API 操作封装成可复用的 Skill（包装原子 API 或编排多步流程）时使用。

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Use when first setting up lark-cli, running auth login, switching user/bot identity (--as), handling permission denied or scope errors, needing to update lark-cli, or seeing _notice in JSON output.

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