3,834 件 ・ 関連度順
概要と使いどころ
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
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 auditing repository documentation for drift, stale statements, broken references, or README mismatches, especially across published docs under `doc/` and key entry surfaces such as `README.md` (English default), `README.zh-CN.md`, `mcp/README.md`, and `mcp/README.zh-CN.md` during periodic maintenance or before preparing corrective PRs.
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 running or scheduling periodic repository review cycles that must dispatch the correct reviewer, aggregate findings, and escalate outcomes into reports, issues, or corrective PRs across API contracts, documentation freshness, code quality, and CloudBase skill quality.
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 medium-to-large changes need explicit requirements, technical design, and task planning before implementation, especially for multi-module work, unclear acceptance criteria, or architecture-heavy requests.
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。
Reduce defensive writing in drafts and revisions by removing unnecessary caveats, disclaimers, hedges, apology-like framing, negative self-limiting statements, and over-explanations while preserving necessary scope, accuracy, safety, legal, ethical, and methodological limits. Use when drafting or revising academic writing, papers, essays, abstracts, introductions, contribution statements, discussions, conclusions, technical explanations, grant writing, product writing, policy writing, professional reports, or any text the user wants to be clearer, stronger, more direct, more concise, more confident, less verbose, less caveated, less apologetic, or less defensive.
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, rewriting, or improving technical docs (quickstarts, how-tos, tutorials, concept pages, or API references).
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。
Optimize a prisma.io page for search engines and AI answer engines. Use when writing or reviewing blog posts, docs pages, or landing pages for SEO, GEO, AEO, AI citations, AI Overviews, ChatGPT/Perplexity visibility, featured snippets, metadata, or FAQ sections; when refreshing an existing page for freshness or rankings; or when asked why a page isn't ranking or being cited.
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。
Clean a raw transcript before ingestion: punctuate, paragraph, label speakers, fix mistranscribed technical terms, and split long recordings by topic. Use this skill when the user drops an auto-generated transcript, subtitle file, podcast or lecture transcript, meeting recording text, or asks to process a video or audio source. Do NOT use for text that is already prose, for summarising a transcript, or for the ingestion itself.
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。
SEO and GEO (Generative Engine Optimization) for organic traffic and AI search visibility. Covers technical SEO, content SEO, keyword strategy, schema markup, and optimization for AI search engines (ChatGPT, Perplexity, Gemini). Use when the user needs SEO audit, keyword research, GEO optimization, schema markup, or says 'SEO', 'search optimization', 'keywords', 'organic traffic', 'AI search', 'GEO', or 'rank higher'.
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 a CCAM reliability report from session outcomes, hook events, alerts, tool failures, and data freshness. Use for health reviews, incident follow-up, hook-delivery audits, or reliability trend summaries.
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。
Quick dashboard health and status overview — checks the Agent Monitor API (port 4820), reports session/agent/event counts from /api/stats, confirms WebSocket connectivity, reads the redacted hook status returned by /api/settings/info, and shows data freshness (last event timestamp). Use to verify the monitoring system is operational.
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。
Deep research and discovery before building something new. Explores local projects for reusable code, researches competitors, reads forums and reviews, analyses plugin ecosystems, investigates technical options, and produces a comprehensive research brief. Three depths: focused (30 min), wide (1-2 hours), deep (3-6 hours). Triggers: 'research this', 'discovery', 'explore the space', 'what should I build', 'competitive analysis', 'before I start building', 'research before coding'. Not for cited fact-checking research reports (a separate harness does those); this is pre-build product discovery.
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。
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
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。
Help a technical founder build whatever they want on Claude Managed Agents — an internal worker, a piece of their product, a customer-facing agent. Find out what they want to build, scope a v0, launch it in their account, grade it, iterate, and (if it should run on a clock) put it on a scheduled deployment, with everything bigger laid out as v1/v2. Use when a founder says "launch my agent", "/launch-your-agent", "build me a managed agent", or wants to build something on CMA. Keywords: managed agents, CMA, founder, launch, scheduled deployment, outcome rubric.
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。
Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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 the authoritative final method explanation for a submission-ready subquestion from the method card, human decision ledger, code plan, final results, and robustness 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。
Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.
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。
Translate a human-approved main method and usable baseline into a minimal language-neutral implementation and experiment contract. Use after G2.5 and data readiness, before Python or MATLAB code generation.
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 and run minimal reproducible MATLAB or Beita Tianyuan compatible code for the human-approved main method and usable baseline, with compact experiment artifacts and a canonical run summary.
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 and maintain one global mathematical symbol and unit table from the problem frame and active method cards, resolving cross-subquestion conflicts before code or paper work.
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。
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
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 and maintain global and method-specific mathematical-model assumptions from the problem frame, active method cards, data profile, and risk probes, while leaving necessity and impact judgments to the human modeler.
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。
Faithfully append a human modeler's choice and rationale to one canonical per-subquestion JSONL decision ledger. Use after a choice card is answered or when migrating legacy decision artifacts; never originate or improve the decision.
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。
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
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。