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。
Deep, evidence-first review of GitHub bugs and PRs: reads the code before forming opinions, finds root causes, decides whether an issue is stale, duplicate, or real, and proposes the best fix. Use when an issue or PR needs real investigation rather than a summary.
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.
High-confidence, evidence-first, code-aware review. The goal is not a generic summary. The goal is to understand the bug class, find the real cause if possible, decide the best fix after reading enough code, and call out whether a larger refactor would improve the design.
Use gh, not web browsing, for GitHub refs:
gh issue view <n> --json number,title,state,author,body,comments,labels,updatedAt,url
gh pr view <n> --json number,title,state,author,body,comments,reviews,files,commits,statusCheckRollup,mergeStateStatus,headRefName,headRepositoryOwner,url
gh pr diff <n> --patch
For repo-local review, also inspect:
git status --short --branch
git fetch origin
git log --oneline --decorate -20
rg "<key symbol/error/config/endpoint>"
Always answer these, explicitly:
Read past the first touched file. Follow the real call path:
When behavior depends on a dependency, read the upstream docs/source/types or current package contract before assuming.
Prefer current source and executable proof over issue comments. Treat stale comments, old CI, and old release behavior as hints until rechecked.
For bug/regression reviews, include a compact Provenance: answer when feasible:
git log -S/-G, git blame, linked PRs/issues, and tests.introduced by, made visible by, or carried forward by.clear, likely, or unknown.Good fixes usually:
Call out when a fix is only symptom-level. If a slightly larger refactor makes the invariant obvious and reduces future bugs, recommend it.
Lead with findings when reviewing a PR. Findings need file/line/symbol references and a concrete failure mode. Avoid vague "consider" comments.
If no blocking issues:
Do not approve, comment, close, merge, push, or land unless the user asked for that action.
For bugs/issues:
main already fixes it.main, only comment/close when the user asks.Ref: #123 / PR #456
Surface: <runtime/CLI/provider/channel/docs>
Bug: <one or two sentences>
Cause: <code path + confidence>
Provenance: <introduced/made visible/carried forward by commit/PR/date, or N/A/unknown>
Best fix: <what should change and why>
Refactor: <yes/no, specific shape>
Proof: <tests/live/CI/source/dependency docs>
Risk: <remaining uncertainty>
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。