Claude Code Skills · page 66

Use when a task is multi-step, may span context resets or sessions, uses subagents, or risks losing state before completion.

Use when receiving code review feedback before implementing suggestions, especially when feedback is unclear, risky, disputed, or technically questionable.

Use when the user asks to create, write, update, amend, supersede, or evaluate an ADR, architecture decision record, durable architecture decision, decision log, or baseline sync after architecture-changing work.

Use when explicitly requesting an independent code review, after subagent-driven implementation slices, before merging high-risk work, or when verification finds evidence, baseline, architecture, compatibility, or retirement uncertainty that needs reviewer scrutiny.

Use when executing implementation plans with independent tasks in the current session

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

Use when the user explicitly requests strict or test-first TDD, or when the current conversation already contains an explicit `TDD Route: strict` decision from another Aegis workflow.

Use when the user says `aegis:update`, asks to update or upgrade an installed Aegis method-pack, wants the latest Aegis version, or asks whether Aegis is current on this host.

Use when starting a turn or checking Aegis skill routing.

Use when starting feature work that needs isolation from the current workspace, or when an implementation plan should run outside the active working tree.

Use when about to claim work is complete, fixed, passing, verified, release-ready, or ready to commit, merge, publish, or hand off.

Use when you have a spec or requirements for a multi-step task, before touching code

Use when creating new skills, editing existing skills, or verifying skills work before deployment

Collect and synthesize opinions from multiple AI agents. Use when users say "summon the council", "ask other AIs", or want multiple AI perspectives on a question.

This skill should be used when the user is building, planning, or strategizing and the key question is whether to optimize content (what) or change form (how/medium). Trigger on "내용 vs 형식", "content vs form", "metamedium", "형식을 바꿔볼까", "새로운 포맷", "관점 전환", "perspective shift", "다른 방법 없을까", "같은 방식이 안 먹혀", "diminishing returns". Applies Alan Kay's metamedium concept to surface form-level alternatives. For requirement clarification use vague; for strategy blind spots use unknown.

This skill should be used when the user provides a strategy, plan, or decision document and wants to surface hidden assumptions and blind spots using the Known/Unknown 4-quadrant framework. Trigger on "known unknown", "4분면 분석", "blind spots", "뭘 놓치고 있지", "뭘 모르는지 모르겠어", "전략 점검", "전략 분석", "assumption check", "가정 점검", "quadrant analysis", "what am I missing". Strategy-level blind spot analysis with hypothesis-driven questioning. For requirement clarification use vague; for content-vs-form reframing use metamedium.

This skill should be used when the user's request or requirement is ambiguous and needs iterative questioning to become actionable. Trigger on "clarify requirements", "refine requirements", "요구사항 명확히", "요구사항 정리", "뭘 원하는 건지", "make this clearer", "spec this out", "scope this", "/clarify". Turns vague inputs into concrete specs. For strategy blind spots use unknown; for content-vs-form reframing use metamedium.

개발 커뮤니티에서 기술 주제에 대한 다양한 의견 수집. "개발자 반응", "커뮤니티 의견", "developer reactions" 요청에 사용. Reddit, HN, Dev.to, Lobsters 등 종합.

This skill should be used when the user asks to "기술 의사결정", "뭐 쓸지 고민", "A vs B", "비교 분석", "라이브러리 선택", "아키텍처 결정", "어떤 걸 써야 할지", "트레이드오프", "기술 선택", "구현 방식 고민", or needs deep analysis for technical decisions. Provides systematic multi-source research and synthesized recommendations.

This skill should be used when the user asks to "트윗 가져와", "트윗 번역", "X 게시글 읽어줘", "tweet fetch", "트윗 내용", "트윗 원문", or provides an X/Twitter URL (x.com, twitter.com) and wants to read, translate, or analyze the tweet content. Also useful when other skills need to fetch tweet text programmatically.

This skill should be used when the user asks to "check email", "read emails", "send email", "reply to email", "search inbox", or manages Gmail. Supports multi-account Gmail integration for reading, searching, sending, and label management.

Google 캘린더 일정 조회/생성/수정/삭제. "오늘 일정", "이번 주 일정", "미팅 추가해줘" 요청에 사용. 여러 계정(work, personal) 통합 조회 지원.

Interactive markdown review with web UI. Use when user says "review this", "check this plan", "피드백", "검토해줘" or specifies a file path to review.

This skill should be used when the user asks to "카톡 보내줘", "카카오톡 메시지", "KakaoTalk message", "채팅 읽어줘", "~에게 메시지 보내줘", or needs to send/read messages via KakaoTalk on macOS.

Generate Korean podcast episodes from any source (URLs, tweets, articles, PDFs) — analyzes content, writes a script, generates audio via OpenAI TTS, converts to MP4, and auto-uploads to YouTube. Use this skill whenever the user says 'make a podcast', 'convert to podcast', 'podcast', 'create an episode', 'turn this into audio', 'YouTube podcast', 'turn this article into a podcast', 'publish as audio', or provides sources and wants them transformed into a listenable format. Supports partial execution: script-only, TTS-only, or upload-only.

This skill should be used when user wants to access, capture, or reference Claude Code session history. Trigger when user says "capture session", "save session history", or references past/current conversation as a source - whether for saving, extracting, summarizing, or reviewing. This includes any mention of "what we discussed", "today's work", "session history", or when user treats the conversation itself as source material (e.g., "from our conversation").

This skill should be used when the user asks to "analyze session", "세션 분석", "evaluate skill execution", "스킬 실행 검증", "check session logs", "로그 분석", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.

This skill should be used when the user asks to "wrap up session", "end session", "session wrap", "/wrap", "document learnings", "what should I commit", or wants to analyze completed work before ending a coding session.

Analyze tasks and dynamically assemble expert agent teams using Claude Code's TeamCreate API. Scouts your codebase, designs optimal agents, and executes with validation.

This skill should be used when the user asks to "유튜브 정리", "영상 요약", "transcript 번역", "YouTube digest", "영상 퀴즈", or provides a YouTube URL for analysis. Extracts transcript, generates summary/insights/Korean translation, and tests comprehension with 9 quiz questions across 3 difficulty levels. Optional Deep Research for web-based follow-up.

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5-pass structured code review — correctness, security, performance, readability, consistency

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Generate and verify tests — happy path, edge cases, error paths — using the project's own framework and patterns

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Use when detecting and running project build systems automatically. Supports npm/yarn/pnpm/pip/poetry/gradle/maven/cargo/go/make. Triggers on build, test run, project setup, package manager detection.

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Use when starting Claude Code projects, writing CLAUDE.md/spec.md, dispatching subagents, or requesting Agent Teams parallel development. Covers Spec-Driven Development, Context Engineering, and post-dev workflow.

Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.

Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.

Manage major dependency version upgrades with compatibility analysis, staged rollout, and comprehensive testing. Use when upgrading framework versions, updating major dependencies, or managing breaking changes in libraries.

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.

Use when adding new error messages to React, or seeing "unknown error code" warnings.

Trigger when the user requests a review of frontend files (e.g., `.tsx`, `.ts`, `.js`). Support both pending-change reviews and focused file reviews while applying the checklist rules.

세션 변경사항을 분석하여 검증 스킬 누락을 탐지합니다. 기존 스킬을 동적으로 탐색하고, 새 스킬을 생성하거나 기존 스킬을 업데이트한 뒤 프로젝트 CLAUDE.md를 관리합니다.

Use when searching, installing, or improving AI skills and prompts via prompts.chat or skills.sh. Triggers on skill search, prompt lookup, install skill, improve prompt, prompts.chat.

Guides security professionals in implementing defense-in-depth security architectures, achieving compliance with industry frameworks (SOC2, ISO27001, GDPR, HIPAA), conducting threat modeling and risk assessments, managing security operations and incident response, and embedding security throughout the SDLC.

Use when security verification is needed - pre-commit security checks, vulnerability scanning, STRIDE threat analysis. Integrates with /handoff-verify --security and /commit-push-pr. CWE Top 25 based.

Use when wrapping up a session before ending. 4 parallel subagents detect doc updates, repeated patterns, learning points, and follow-ups, then a verifier deduplicates and presents choices. Triggers on /session-wrap, session wrap, session cleanup, end of session.

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Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.

Summarize or extract text/transcripts from URLs, podcasts, and local files (great fallback for “transcribe this YouTube/video”).

Use when orchestrating Agent Teams for parallel development - team composition, task distribution, dependency management, and result aggregation. Triggers on /orchestrate, multi-agent coordination, parallel team work, task delegation to teammates.