Answers GitHub read questions from a local cache first and spends live API calls only when freshness or writes matter, with freshness policies for issues, PRs, and repos. Use when agent workflows hit GitHub rate limits or repeat the same API reads.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Attribution: Sourced from steipete/agent-scripts by Peter Steinberger.
Goal: answer common GitHub read questions from local cache first, then spend live GitHub API calls only where freshness or writes matter.
Use gh normally. Prefer these local/cached reads first:
gh search issues "<terms>" -R owner/repo --state open --json number,title,state,url,updatedAt,labels,author
gh search prs "<terms>" -R owner/repo --state open --json number,title,state,url,updatedAt,isDraft,author
gh issue list -R owner/repo --state open --author user --assignee user --label bug --json number,title,url
gh pr list -R owner/repo --state open --author user --label dependencies --json number,title,url
gh issue view 123 -R owner/repo --json number,title,state,body,comments,labels,url
gh pr view 123 -R owner/repo --json number,title,state,body,comments,labels,files,commits,statusCheckRollup,url
gh pr checks 123 -R owner/repo --json name,state,detailsUrl,workflow
gh run list -R owner/repo --branch branch-name --json databaseId,workflowName,status,conclusion,url
gh pr diff 123 -R owner/repo --patch
Use exact refs and narrow fields. Avoid broad loops like one gh issue view per result when a single gh search or gh issue list --json ... can answer the first-pass question.
For CI, avoid tight gh run list / gh run view polling loops. After a push or workflow dispatch, identify one exact run, then poll it with backoff. Fetch full logs only for failed jobs or when the user explicitly asks for logs.
Local answers are good for discovery, duplicate search, old thread review, author/label triage, and "is there likely already an issue/PR?" checks.
Use a live call when:
For PR review, prefer hydrating exact PR details once with gh pr view --json when you know you will inspect files, commits, checks, or run summaries repeatedly.
After a write, do one targeted readback, not a broad rescan.
Batch questions by repo and state. Reuse data already printed in the session. Back off CI polling; inspect logs only for failing runs or the exact run under review.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when long-lived agents, project memories, system prompts, or playbooks are repeatedly summarized, rewritten, or optimized and risk losing domain detail, provenance, or execution learnings.
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 this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level 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。
Adds educational comments to a code file so it becomes a learning resource, asking for the file (with a list of close matches) if none is given. Use when preparing code for learners, onboarding, or teaching.
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。
Write, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
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。
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
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。
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
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。