3,312 件 ・ 関連度順
概要と使いどころ
Use BEFORE editing shared code — enumerates every call site, event consumer, queue worker, API client, migration, and test that a planned change will touch, with a file:line citation per dependency.
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 picking which analysis or project-analysis-* skill fits a request — routes by scope, framework, and symptom — even if the user just says 'analyze this' or 'dig into the codebase'.
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 the user shares a Sentry error, Jira bug ticket, or error description and wants root cause analysis. Also for proactive bug hunting and code audits for hidden bugs.
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。
[Architecture] Use when you need to research, analyze, and compare tech stack options as a solution architect.
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。
[Testing] Use when you need to run tests locally and analyze the summary report.
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。
[Planning] Use when you need to research, analyze, and plan technical solutions that are scalable, secure, and maintainable.
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。
[Documentation] Use when you need to generate a detailed, AI-analyzed release document from git history over a time range or custom prompt.
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。
[Implementation] Use when you need to analyze and fix issues [INTELLIGENT ROUTING]. Flag: --target={ci|issue|logs|test|types|ui} scopes the fix; --target=types resolves TypeScript errors inline.
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。
[Research] Use when you need to analyze market landscape: competitors, sizing (TAM/SAM/SOM), trends, SWOT, customer segments.
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。
[Planning] Use when the user provides an implementation plan file and asks to analyze it, assess impact, update specifications, or verify planned changes.
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。
[Code Intelligence] Use when you need to analyze the blast radius of current code changes using the structural knowledge graph.
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。
[Code Quality] Use when the user asks to analyze package upgrades, check for outdated dependencies, plan npm/NuGet updates, or assess breaking changes in package updates.
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。
[Architecture] Use when you need to analyze business domain: bounded contexts, aggregates, entities, ERD, domain events, and cross-context integration.
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 planning the migration of a legacy system or monolith — analyzes the source codebase (current repo, or a repo URL cloned to a temp dir) and produces an evidence-based Strangler Fig migration plan with per-domain SPECs. Default target is .NET (Blazor WebAssembly, .NET MAUI, ASP.NET Core); other stacks allowed by user decision. Do NOT use to implement code or when approved SPECs already cover the migration.
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。
Analyzes a dataset of companies or profiles to identify market patterns. Use when the user provides a larger dataset and wants segments, dominant categories, gaps, and emerging patterns.
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。
Re-analyze an existing GitHub Issue using its current title and body as input, refresh the implementation plan against the latest code state, and update the Issue in place. Use this when the user provides an Issue number (numeric, `#`-prefixed, or Issue URL) and wants to regenerate the Explore-based analysis. For reflecting comment-driven updates instead, use update-issue. For creating a brand-new Issue from a natural-language task description, use create-issue.
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。
Create an implementation plan and a GitHub Issue based on the task description provided as an argument. Use this when the user supplies a natural-language task description (not an issue number) and wants a new implementation-ready Issue. If the input is an existing issue number, use create-issue-from-issue-number (re-analyze) or update-issue (reflect comments) instead.
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。
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.
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 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.
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。
Analyzes article structure, identifies positions requiring visual aids, generates illustrations with Type × Style × Palette three-dimension approach. Use when user asks to "illustrate article", "add images", "generate images for article", or "为文章配图".
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。
Correlate scanner results with CVE and exploit intelligence and prioritize by CVSS and exploitability.
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, 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.
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。
Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.
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。
Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.
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。