1.3천건 · 관련도순
Use when Codex feels slow or bloated, when local sessions/logs/worktrees/config have grown over time, or when a user wants safe maintenance for Codex Desktop/CLI state. Provides a read-only report by default, backs up before applying changes, archives instead of deleting, normalizes Windows extended paths, prunes dead config projects, rotates large logs, and moves stale worktrees.
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Run a company event — the launch party, the customer day, the team celebration — as the operation it is: the goal that shapes every choice, the budget with its forgotten lines, the vendor and venue coordination, the run-of-show with owners, and the day-of roles that keep hosts hosting. Use when asked plan the company event, organize our customer day/holiday party/launch event, what am I forgetting for this event, or be the run-of-show for Thursday. Produces the goal-shaped plan, the budget with the forgotten lines, the run-of-show, and the day-of role card.
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Autonomous development with experimental self-correction. Iterates through quality gates until antifragile or blocked.
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Detect insecure JWT (JSON Web Token) implementations in a codebase using a two-phase approach: first map all JWT issuance and verification sites to understand the token lifecycle and signing configuration, then check each verification site for exploitable weaknesses such as algorithm confusion, missing signature verification, weak secrets, header injection, and missing claim validation. Requires sast/architecture.md (run sast-analysis first). Outputs findings to sast/jwt-results.md. If no JWT usage is found in Phase 1, Phase 2 is skipped. Use when asked to find JWT, token forgery, or authentication bypass bugs.
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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al. 2023), Pharmer / Pharmit for search, and PharmacoForge for protein-pocket-conditioned pharmacophore generation (Flynn et al. 2025), covering ligand-based pharmacophores from active-set alignment and receptor-based pharmacophores from binding-pocket geometry. Explicitly handles feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.
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Deep bug analysis to break the fix-forget-repeat cycle. Analyzes root cause category, why fixes failed, prevention mechanisms, and captures knowledge into specs. Use after fixing a bug to prevent the same class of bugs.
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24/7 autonomous business operations orchestrator with business health scoring, canary monitoring, and safety guardrails. Schedules and runs all business functions automatically — content publishing, social media posting, outreach sequences, ad monitoring, financial reporting, health checks, and post-deploy verification. Use when the user wants to automate operations, schedule tasks, set up autonomous workflows, or says 'automate this', '24/7', 'run automatically', 'schedule', 'cron', 'autonomous', or 'set and forget'.
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Systematic content refresh discipline. Quarterly audits, refresh prioritization (which pieces, when, how deep), refresh-vs-merge-vs-delete decisions, the lifecycle that distinguishes intentional refresh from set-and-forget decay. Builds on the refresh sections of pillar-content-architecture and editorial-qa with a program-level discipline. Triggers on content refresh, content decay, content audit, refresh prioritization, content lifecycle, refresh strategy, traffic decay, ranking drop, content freshness, evergreen content, content maintenance. Also triggers when traffic is eroding silently across an aging content library, when teams cannot decide which pieces to refresh, or when refresh work is happening but the impact is unclear.
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Conducts structured requirements workshops to produce feature specifications, user stories, EARS-format functional requirements, acceptance criteria, and implementation checklists.
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Design a spaced retrieval schedule for any topic list and timeline. Use when planning units, term sequences, or revision programmes.
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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.
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Measure and shrink the always-loaded context of a Claude Code harness (CLAUDE.md + rules without paths frontmatter) back under budget — migrate narrative to reference files, convert rules to path-scoped or skill-triggered loading, and verify zero governance loss with deterministic preservation checks. Use on "harness diet", "context diet", "rules diet", "CLAUDE.md too long", "always-load budget", "trim my rules", "context bloat", "하네스 다이어트", "룰 다이어트", "컨텍스트 다이어트", "always-load 줄여줘". Not for one-off prose editing or project docs cleanup.
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Apply STRIDE methodology to systematically identify threats. Use when analyzing system security, conducting threat modeling sessions, or creating security documentation.
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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.
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Use when verifying build/test/lint before commit, PR, or completion claims. Runs verification pipeline in fresh subagent context with auto-repair. Triggers on /handoff-verify, pre-commit check, build verification, test validation.
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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.
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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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세션 변경사항을 분석하여 검증 스킬 누락을 탐지합니다. 기존 스킬을 동적으로 탐색하고, 새 스킬을 생성하거나 기존 스킬을 업데이트한 뒤 프로젝트 CLAUDE.md를 관리합니다.
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Run the adversarial verification loop — implement, then hand the change to a fresh checker that did not write it, fix what it finds, and re-dispatch until APPROVE. Use before claiming any behavioural change is done, and on requests like "review loop", "adversarial review", "independent review", "get this verified", "is this actually done", "maker checker", "second pair of eyes on this change". Not for prose, docs or typo edits.
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Expert guidance for Next.js Cache Components and Partial Prerendering (PPR). **PROACTIVE ACTIVATION**: Use this skill automatically when working in Next.js projects that have `cacheComponents: true` in their next.config.ts/next.config.js. When this config is detected, proactively apply Cache Components patterns and best practices to all React Server Component implementations. **DETECTION**: At the start of a session in a Next.js project, check for `cacheComponents: true` in next.config. If enabled, this skill's patterns should guide all component authoring, data fetching, and caching decisions. **USE CASES**: Implementing 'use cache' directive, configuring cache lifetimes with cacheLife(), tagging cached data with cacheTag(), invalidating caches with updateTag()/revalidateTag(), optimizing static vs dynamic content boundaries, debugging cache issues, and reviewing Cache Component implementations.
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Clean up translation-ese and AI-writing tics in Korean prose so it reads like a person wrote it — content (facts, numbers, proper nouns, quotes) stays 100% unchanged, only style and rhythm are touched. Single-pass, done in this session. Triggers — AI 티 없애줘, 번역투 고쳐줘, 한글 자연스럽게 윤문해줘, sounds like a machine translation, remove AI tells from this Korean text, make this Korean read naturally. Not for: marketing copy generation (write it clean the first time — see rules/korean-writing-quality.md), English text, plain spell-check (see korean-spell-check), or book-length manuscripts.
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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.
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Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
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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.
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