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Request write permission from the user before making file changes. MUST be invoked before any write attempt when the write gate is closed. Creates scoped approval via AskUserQuestion + TaskCreate, opens the write gate, and closes it when work is done. Triggers: write hook blocks an edit, agent is about to write files outside .craft/, agent needs to modify project source files. This skill requires user approval 100% of the time - never bypass AskUserQuestion.

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Rigorous self-assessment checklist before marking any task as complete. Use when about to claim task completion, before final commit, when user asks "is it done?", or when transitioning from implementation to reporting. Prevents premature completion claims by requiring evidence for every assertion.

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You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or when users request help with ideation, marketing, and strategic planning. Explores user intent, requirements, and design before implementation using research-validated prompt patterns.

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Analyze git repository history to produce a codebase risk profile before code review. Probes repo size, runs 7 parallel git analysis pipelines (hotspots, bug magnets, bus factor, contributor momentum, firefighting frequency, newly added files), cross-references hotspots with bug magnets to identify high-risk files with primary owner annotation, and writes walkthrough/recon-report.md. Use before code review to prioritize reviewer attention, at the start of linear-walkthrough Phase 1 Discovery to weight file coverage, or when onboarding to an unfamiliar codebase.

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Activates Standard of Excellence enforcement for the current session. Load before starting any task to apply completion standards: finish the whole thing, fix the root cause, ship the complete working result. Blocks partial solutions, workarounds, deferred threads, and invented content limits when the permanent solve is within reach. Triggers: 'do the whole thing', 'boil the ocean', 'standard of excellence', 'finish it completely', before starting any implementation, refactoring, or multi-step task where partial output is a risk. Does NOT apply to: read-only queries, one-line typo fixes, pure knowledge questions, or single-output summarize requests.

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Audits fielded survey-response data for registered elements, data quality, bot and AI-automation screening, and sample integrity, and produces an appendix-ready quality report. Use at soft launch before full field, after fielding before outcomes are analyzed, or when asked what a study did about bots and AI respondents.

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Generates 3-5 conceptually distinct approaches (Novel, Surprising, Diverse, Conventional) and holds for selection before implementing. Use before committing to an approach when a task has more than one non-obvious solution; --codex delegates the brainstorm to a fresh subagent.

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Generates 3-5 conceptually distinct approaches labeled Novel, Surprising, Diverse, or Conventional, and holds for the user's selection before implementing. The --codex mode runs the brainstorm on GPT-6 Astra via codex exec, then has Codex implement the selected approach. Use before committing to an approach when a creative, architectural, or analytical task has more than one non-obvious solution; use --codex when another model family should widen the range.

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Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.

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Grill the user about a requirement, decision, or idea before implementation, then produce an actionable planning report without writing code. Use when the user wants to clarify requirements, stress-test an idea, compare approaches, or plan before coding.

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Semantic codebase search, code indexing, and prompt enhancement via standalone CLI. Use when: (1) Semantic code search with natural language queries, (2) Code indexing for remote codebase retrieval, (3) Prompt enhancement with codebase context, (4) Before grep/find/glob operations for better accuracy, (5) Complex requirements clarification, (6) Large codebase navigation. Triggers: "search context", "enhance prompt", "find code that", "index project", "clarify requirements". IMPORTANT: Always use ace-tool BEFORE grep/find/glob for semantic-level code location.

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Synchronize existing READMEs with local Git code changes and every language edition. This skill should be used for update readme, sync readme, 更新 README, 更新项目文档, 同步多语言 README, README を更新して, README 업데이트해 줘, обнови README, or /readme-update. Ask for an exact project version or explicit unchanged choice before editing; inspect committed, staged, unstaged and untracked changes, weave each change into its natural position (never just the beginning or end), keep the flowchart current, and keep all language editions identical apart from the language.

Generate or redesign evidence-based README files for apps, libraries, CLIs, monorepos, and agent skills. This skill should be used for /readme-write, /readme, generate readme, write readme, improve readme, 帮我写 README, 美化 README, README を書いて, README 작성해 줘, напиши README, or their equivalents in any language. Confirm reader and visual preferences before generation; every README includes a source-backed flowchart, and every language edition keeps identical content apart from the language. Git-driven updates are routed to the companion readme-update skill.

Review changed Anabasis v2 code for reuse, simplification, efficiency and altitude, then apply the fixes. Quality only: it hunts no bugs, which /code-review does. Starts from what the repository already finds (the lint plugins, the whole-tree census, the not-slop ledger), measures before and after with reduce-complexity's scripts, and holds a pass to its budgets and to the rules no pass trades. Use as /simplify on your own diff before a commit, or when the operator asks whether code is simple enough, why something exists, to go one layer higher, or to remove something and find another way.

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Use before opening a PR, or whenever asked to self-review a diffusers contribution. Applies the same rubric as the `@claude` CI (checks the diff against references/review-rules.md, traces call paths for dead code). Reports findings grouped by severity, flagging what to fix before submitting (blocking issues + dead code) vs what to leave for the actual review. Report-only — does not edit files.

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Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.

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Use when a feature branch is complete and its history must be cleaned up before merging back to dev — as the final step before creating or merging a PR, or when the user says "prepare for merge", "squash the branch", "clean up branch history", or "/prepare-merge".

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You MUST use this before any creative work or multi-step task. Explores intent and writes execution-ready plans before coding.

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Run a real-task benchmark suite against OpenClaw agents so model or harness changes can be compared before they hit production workflows.

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Uses mitmproxy2swagger to turn captured mitmproxy flows or HAR exports into a first-pass OpenAPI document that an agent can refine, validate, and hand off to downstream tooling. Invoke it when the API already exists but the contract does not, and you need a concrete spec before generating clients, mocks, tests, or reviewable docs.

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Run structured prompt-injection attack and defense experiments against an LLM-integrated app before production by measuring attack success and testing detection or recovery pipelines.

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Put an inline firewall and containment layer in front of agent network traffic, tool calls, and MCP traffic before you trust an agent with local secrets.

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Add hard pre-execution guardrails to Claude Code so destructive shell commands are blocked before an agent can run them.

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Scan staged changes, commits, or repositories for secrets before they leave the workstation or CI job, instead of relying on a later platform-side catch.

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。