1.6万 件 ・ 関連度順
概要と使いどころ
Use when designing or implementing search, filtering, sorting, query bars, and result states in Apple-platform apps — keep search, filter, and sort as separate jobs with clear no-result states.
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。
Retrieve the current system date and time to anchor the agent in present reality. Use for questions involving today, current time, recent events, or information newer than the model's training data to prevent knowledge-cutoff errors and fictionalization of real facts.
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。
Triage a GitHub issue that already has the cc-issue-created label and is assumed to be ready to start. First inspect the comment history to decide whether human confirmation is needed (cc-need-human-check, highest priority); otherwise decide whether the issue should be closed as not needed, unanswered confirmation items remain (cc-answer-issue-questions), the description is stale relative to settled comment-history content and must be refreshed before execution (cc-update-issue), or it can move to execution (cc-exec-issue). Dependency checks are out of scope.
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。
Update an existing GitHub Issue's description based on the issue number. Reads all issue comments and reflects any items not yet captured in the description. After refreshing the description, reviews it for remaining ambiguities or unclear requirements and posts any open questions as a follow-up 確認事項 comment.
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。
INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by create-issue, create-issue-from-issue-number, and update-issue. It formats an implementation-ready GitHub Issue body, runs the pre-posting checklist, executes `gh issue create` or `gh issue edit`, and optionally posts a 確認事項 follow-up comment. Invoke this skill ONLY from one of the three parent skills via the Skill tool, after the parent has completed analysis. If a user asks to 'format an issue body' or similar, route them to the appropriate parent skill (/create-issue, /create-issue-from-issue-number, or /update-issue) rather than invoking this one directly.
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。
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。
Virtual QA Lead that turns requirements into review-ready, TestRail-importable test cases in one conversation. Use it whenever the user has requirements, a spec, a user story, or acceptance criteria (PDF, DOCX, XLSX, TXT, MD, or pasted text) and wants test cases, a test plan, a checklist, test coverage, a regression suite, or a TestRail/Qase/Zephyr export, even without saying "test cases" ("write tests for this spec", "what should we check here?"). Especially useful when they attach their team's existing test cases to match. When the spec describes an API (endpoints, status codes, schemas, auth, OpenAPI/Swagger), Casely also builds a runnable Postman collection, so use it for "API tests", "Postman collection", or "tests I can run in Newman/CI". Triggers on the same request in any language.
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。
Transforms data into compelling visual narratives by applying narrative structure, annotation techniques, scrollytelling patterns, and honest framing to data journalism, presentations, and infographics. Use when creating data-driven articles or reports, designing infographics with narrative, building scrollytelling experiences, annotating charts to guide interpretation, or when user mentions data storytelling, presentation design, annotated chart, narrative visualization.
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。
Audit or update this Windows computer's fixed third-party Codex helper toolchain when the user requests local helper-tool maintenance. Always show the complete registered list and obtain confirmation for each update group; exclude Codex itself and general runtimes unless explicitly added.
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 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
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 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.
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。
Generate/edit images with Nano Banana Pro (Gemini 3 Pro Image). Use for image create/modify requests incl. edits. Supports text-to-image + image-to-image; 1K/2K/4K; use --input-image.
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。
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.
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 completing tasks, implementing major features, or before merging to verify work meets requirements
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。
Extract clean markdown, text, or targeted chunks from one or more known, publicly accessible webpage URLs via the Tavily CLI. Use when the URLs are already supplied or discovered and the user wants page content rather than more URLs. Do not use to find an unknown page (tavily-search), list a site's URL structure (tavily-map), bulk-extract a site section (tavily-crawl), or retrieve named platform, authenticated, cookie-dependent, or platform-native content (Agent Reach). Can process up to 20 public URLs per 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。
Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
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 a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a UI should look like.
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。
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.
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 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
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。
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
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 writing, moving, reviewing, or auditing documentation in the deepseek-harness repo — choosing hierarchy and detail, separating tutorials from references, checking tutorial progression, trimming doc slop, responding to a verify-doc-budgets failure, or requests like "improve the docs", "audit the docs", "where should this be documented", or "this doc is too long".
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。
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)。
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。
Europe PMC RESTful Web Service 多源文献聚合检索原子 skill。一次调用同时召回 PubMed + PMC + AGRICOLA + CABI + 预印本(bioRxiv / medRxiv / ChemRxiv 等)+ 灰色文献,自动按 PMID/PMCID/DOI 三级去重,返回完整元数据(含全文 OA 链接、citations metadata、grants 资助信息)。当任务出现 Europe PMC、多源文献聚合、广召回、RAG 检索、预印本搜索、bioRxiv、medRxiv、全文链接、DOI 检索、跨库去重、灰色文献、资助检索 等触发词时使用。**定位**:广召回 · 多源聚合 · RAG 友好(与 pubmed-eutils 互补——后者做精准官方源、Clinical Queries;本 skill 做"撒网"式广召回)。**不做**:NCBI E-utilities 直检(交给 pubmed-eutils)、临床试验(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)。
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。
使用 AACT (Aggregate Analysis of ClinicalTrials.gov) PostgreSQL 数据仓库进行批量、历史、聚合性临床试验数据挖掘。Use this skill when the user requests bulk SQL analysis over the full clinical trials data warehouse — historical trial trends, disease landscapes, similar-design matching, or multi-year aggregations across hundreds of thousands of NCT records. 触发场景包括：AACT 查询、临床试验批量分析、PostgreSQL 试验数据、全量 NCT 检索、试验数据挖掘、历史试验分析、clinical trials data warehouse、SQL trials、bulk trial analysis、disease landscape、试验设计相似性匹配、跨年度聚合、sponsor/phase/country 多维统计。**与 clinical-trials-v2 差异**：本 skill 走批量 SQL · 离线大数据（PostgreSQL）；v2 走实时 API · 单查询。两者互补：单条 NCT 实时状态用 v2，百万级历史挖掘用本 skill。支持云端公共 PostgreSQL（aact-db.ctti-clinicaltrials.org · 零部署）和每日 dump 本地还原（高性能 · 离线）两种连接方式，自动检测优先用本地。跨平台（macOS/Linux/Windows）参数化 SQL 防注入，read-only 强制保护。
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。