Skills de Claude Code · página 151

Use this skill for any Python backend work in this project: building FastAPI endpoints, writing service functions, defining Pydantic/SQLModel schemas, running Alembic migrations, or debugging 422 errors. Essential for authentication and authorization patterns — setting up get_current_user, is_superuser checks, admin-only guards, role-based access, and dependency injection chains like Depends(). Also covers middleware, background tasks, async SQLAlchemy sessions, ORM relationship loading, and request/response design. Activate whenever the question involves Python API code, FastAPI patterns, or backend architecture in this codebase. Not for frontend, Docker, CI/CD, or infrastructure.

ALWAYS activate when the user's query involves Docker in any way — even if it also matches other skills. If the words docker, Dockerfile, docker-compose, compose.yml, container, or image appear in the query, this skill MUST be used. Covers: writing or editing Dockerfiles and compose files, adding services (postgres, redis, etc.) to compose, volume mounts and data persistence, docker build failures (layer caching, npm install issues), healthchecks and service startup ordering (depends_on), environment variables in containers, port mapping, container crashes and exit codes (OOM/137), non-root users, multi-stage builds, image optimization, .dockerignore, and deploying to container runtimes. Takes priority over general implementation or debugging skills when Docker infrastructure is the subject.

Use this skill for ANY work involving React, Next.js, TypeScript, or Tailwind in the browser layer. This includes building components and pages, but equally covers debugging and fixing frontend issues: CSS/Tailwind classes not applying, form validation behavior, hydration mismatches between server and client renders, styling bugs, layout shifts, and rendering problems. Also use for refactoring components (e.g., splitting Server vs Client Components), data fetching patterns, state management, bundle optimization, and frontend tooling. If the problem involves what users see or interact with in a web browser — whether building, fixing, or refactoring — use this skill. Not for backend APIs, databases, infrastructure, or DevOps.

Rewrite existing text so it stops sounding AI-generated. Triggers on "humanize this", "un-ChatGPT it", "doesn't scream LLM", "strip the AI voice", or naming specific AI tells (em dashes, "it's not just X it's Y", "stands as a testament", "thrilled to announce", emoji-headed bullets, LinkedIn cringe). Works on any draft — blog posts, memos, READMEs, essays, PR descriptions — inline or in files. Keeps facts; changes voice. Do NOT use for writing new content, summarizing, translating, or pure-explanation requests with no draft to rewrite.

MUST use for ANY query mentioning packages, monorepo, workspace, catalog, turbo, turborepo, or pnpm in a multi-package context. MUST use when sharing config (ESLint, tsconfig, prettier) across packages, fixing build order between packages, adding new packages, scoping CI installs/caching to changed packages, or debugging pnpm catalog version resolution. This skill OWNS all cross-package coordination problems — even when they look like build, CI, config, or dependency issues. If two or more packages interact in the query, this skill applies. Takes priority over other skills when the problem spans package boundaries.

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Show all your installed skills. Deprecated alias — use /janitor-report --brief.

Find new skills on GitHub or check a specific skill before installing. Use when the user wants to search for skills, evaluate a skill URL, check overlap with existing skills before installing, or compare a local skill against alternatives.

Automatically fix skill problems (safe preview first). Also use with --prune to find and remove broken symlinks, empty directories, and orphaned skills.

Check if a new skill overlaps with existing ones before installing. Deprecated alias — use /janitor-discover with a URL or path.

Full health check of all your skills in one report. Use when the user wants to check for errors, find duplicates, detect broken skills, or get a complete overview of skill health. Pass --brief for inventory only.

Search GitHub for new skills to install. Deprecated alias — use /janitor-discover (combines search + pre-install check).

Show how many context window tokens each skill consumes. Deprecated alias — use /janitor-value (which combines tokens with usage).

Show which skills you use. Deprecated alias — use /janitor-value (which now combines usage + token cost).

Show whether each skill is earning its context-window cost — combined tokens-used view sorted by waste. Use when the user asks 'are my skills worth it', 'what's my context budget', 'which skills are dead weight', or anything about skill value, token cost, or usage.

测试 use-persona 的角色扮演一致性。给定 persona + 10 个对话场景，生成回复并按 5 个维度评分，输出一致性报告。

测试 use-self 替身会议的辩论质量。给定 persona + 3 个决策场景，运行完整三阶段辩论并按 5 个维度评分，输出质量报告。

蒸馏一个你身边的人。通过聊天记录、朋友圈、描述等素材，生成 ta 的人格档案，让 ta 以自己的方式和你对话。

蒸馏你自己的数字替身。通过多轮对话和素材导入，生成你的人格底座，用于私人决策辅助。

以某个人的身份和你对话。用 ta 的语气、习惯、互动方式回应你。

召唤你的数字替身进行决策辅助。多个版本的你同时分析一个决定，帮你看清局中看不清的自己。

Guide for creating effective skills. This skill should be used when users want

This skill should be used when writing in the distinctive style of David Heinemeier Hansson (DHH). It applies when creating blog posts, technical articles, business content, manifestos, or any prose requiring a clear, punchy, opinionated style.

This skill should be used when reviewing or editing copy to ensure adherence to Every's style guide. It provides a systematic line-by-line review process for grammar, punctuation, mechanics, and style guide compliance.

This skill should be used when writing technical content in the style of Hunt/Thomas (The Pragmatic Programmer) and Joel Spolsky (Joel on Software). It applies when creating technical essays, documentation, tutorials, or explanatory content that needs to be clear, engaging, and actionable.

This skill should be used when extracting voice profiles from sample text, creating voice documentation, or matching a specific writing style. It applies when users provide sample text and want to capture the voice for future use.

This skill should be used when orchestrating complex writing workflows with multiple phases. It provides two-agent orchestration patterns, the two-gate content readiness assessment, 10 baseline writing strategies, 20+ situational strategies, and quality checkpoints. Inspired by the Spiral Writing System.

Collaborative brainstorming partner for multi-session ideation projects. Use

Expert documentation generator for coding projects. Analyzes codebases to

Develop ebook ideas into structured concepts ready for architecture. Use when

Surface ebook ideas you didn't know you had. Use when ready to discover what

Create a structured session handoff document for continuity across sessions.

Generate platform-specific, actionable growth playbooks for mobile apps. Use

Systematically analyze app reviews for competitor research or own-app

Optimize iOS App Store and Google Play Store listings for maximum

Full-pipeline iOS App Store opportunity research. Discovers underserved

Orchestrate iOS/macOS app scaffolding and optional skill adoption for existing projects. Use when users want a guided wizard that can scaffold with XcodeGen and optionally install xcode-makefiles and simple-tasks.

Install a fast local task workflow for single-project planning with `scripts/task.sh` (claim, done, status, reporting) backed by `tasks/TASKS.md` and optional `tasks/details/` notes. Use for lightweight in-progress task coordination, not full team issue tracking.

Install strict Xcode Makefile tooling for iOS/macOS projects, including build/run/test scripts with AGENT_NAME-based per-agent isolation under build/. Use when a project needs reproducible local CLI builds without full app scaffolding.

Comprehensive step-by-step launch checklist for shipping mobile apps to the

Design effective paywalls, structure subscription tiers, and optimize pricing

Design the structural and emotional architecture for nonfiction books. Use

Stress-test book concepts against existing research before committing to

Develop raw book ideas into structured nonfiction book concepts. Use when the

Assess commercial viability of book concepts for Amazon KDP self-publishing.

Plan, orchestrate, and validate deep research for nonfiction books. Use when

Plan and architect a single chapter at beat-level granularity. Use when you

Produce first drafts that match a writer's authentic voice using their Voice

Capture a writer's voice DNA through collaborative interview and sample

Refresh a repo's documentation for a live production system — audit stale docs, rebuild a focused doc set with diagrams, update AI-agent knowledge bases, and improve code-level docstrings

Detect common technical and organizational anti-patterns in proposals, architectures, and plans. Use when strategic-cto-mentor needs to identify red flags before they become problems.

Recommend architecture patterns (monolith, microservices, serverless, modular monolith) based on scale, team size, and constraints. Use when cto-architect needs to select the right architectural approach for a new system or migration.

Identify and challenge implicit assumptions in plans, proposals, and technical decisions. Use when strategic-cto-mentor needs to surface hidden assumptions and wishful thinking before they become costly mistakes.

Generate targeted clarifying questions (2-3 max) that challenge vague requirements and extract missing context. Use after request-analyzer identifies clarification needs, before routing to specialist agents. Helps cto-orchestrator avoid delegating unclear requirements.

Infrastructure and development cost estimation for technical projects. Use when planning budgets, evaluating build vs buy decisions, or projecting TCO for architecture choices.

Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing to specialist agents. Ensures agents receive complete context for effective work.

Deep expertise in ML/CV model selection, training pipelines, and inference architecture. Use when designing machine learning systems, computer vision pipelines, or AI-powered features.

Analyze incoming user requests to detect intent, request type (design/validate/debug/document), complexity level, and identify vague requirements or buzzwords that need clarification. Use when cto-orchestrator receives new requests that need classification before routing to specialist agents.

Generate phased implementation roadmaps with Epic/Story/Task breakdown, effort estimates, and validation checkpoints. Use when cto-architect needs to create actionable technical roadmaps from architecture designs.

Guidance for scaling systems from startup to enterprise scale. Use when planning for growth, diagnosing bottlenecks, or designing systems that need to handle 10x-1000x current load.

Recommend technology stacks based on project requirements, team expertise, and constraints. Use when selecting frameworks, languages, databases, and infrastructure for new projects.

Generate structured 8-section validation reports with verdict (GOOD/BAD/NEEDS MAJOR WORK), strengths, critical flaws, blindspots, and concrete path forward. Use after strategic-cto-mentor has completed validation analysis and needs to produce final deliverable.

Reddit & X/Twitter auto-reply bot for ecommerce/SaaS growth. Finds relevant posts about AI customer service, Amazon FBA, Shopify — posts genuine AI-generated replies mentioning your product. Includes Reddit account warmup (karma building) and lead tracking. Triggers: social reply bot, reddit auto reply, twitter auto reply, x auto reply, social media bot, amazon seller engagement, ecommerce social engagement, reddit warmup, karma building, warmup reddit, social leads, potential customers

Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.

Manages Neo4j Aura Agents via the v2beta1 REST API — create, list, get, update, delete,

Serverless Aura Graph Analytics (AGA) GDS Sessions — covers GdsSessions,

Provisions and manages Neo4j Aura instances via CLI (aura-cli v1.7+) or REST API.

Use when working with Neo4j command-line tools — neo4j-cli (modern unified

Generates, optimizes, and validates Cypher 25 queries for Neo4j 2025.x and 2026.x.

Ingests unstructured and semi-structured documents into Neo4j as a knowledge graph.

Neo4j .NET Driver v6 — IDriver lifecycle, DI registration (singleton), ExecutableQuery

Covers the Neo4j Go Driver v6 — driver lifecycle, ExecuteQuery, managed and

Neo4j Java Driver v6 — driver lifecycle, Maven/Gradle setup, executableQuery,

Neo4j JavaScript/TypeScript Driver v6 — driver lifecycle, executeQuery,

Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit

Neo4j Graph Data Science (GDS) embedded plugin via Python client or Cypher —

Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher

Orchestrates zero-to-running-app in 8 stages — prerequisites → context →

Build and configure a GraphQL API backed by Neo4j using @neo4j/graphql v7 (current) or v5 (LTS).

Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python

Import structured data into Neo4j — LOAD CSV, CALL IN TRANSACTIONS, neo4j-admin

Configure and operate the Neo4j Connector for Kafka (sink + source) and the

Use when installing, configuring, or troubleshooting the official Neo4j MCP server

Migrates Neo4j driver code and Cypher queries from older versions (4.x, 5.x)

Design, review, and refactor Neo4j graph data models. Use when choosing node

Neo4j Visualization Library (NVL) — framework-agnostic graph rendering for the browser.

Diagnoses and fixes slow Neo4j Cypher queries by reading execution plans, identifying

Programmatic security management in Neo4j — RBAC/ABAC, user lifecycle (CREATE/ALTER/DROP USER),

Run Neo4j Graph Analytics algorithms (PageRank, Louvain, WCC, Dijkstra, KNN,

Use when reading from or writing to Neo4j with Apache Spark or Databricks using the

Use when building Spring Boot applications with Neo4j using Spring Data Neo4j (SDN 7.x/8.x):

Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN),

Stage-aware prompt coaching, prompt improvement, lookback analysis, prompting habit feedback, and local reports about prompt quality for AI coding agents such as Claude Code or Codex.

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PDF to Word converts PDF to editable Word/DOCX with AI-powered layout analysis and table recognition, built on ComPDF Conversion SDK to better preserve tables, multi-column layouts, lists, and images for downstream editing. It fits requests such as “pdf to word,” “convert pdf to docx,” “pdf to editable word,” “pdf to office,” “keep layout in word,” and “convert report to docx.” Example queries include “Convert this PDF contract to editable Word while keeping the tables intact,” “Turn this report into DOCX and preserve the multi-column layout,” and “Export this PDF to Word for further editing.”

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Read all atomic guidelines in wiki-twobatch/guidelines/ and propose themed clusters that group near-duplicates. Writes cluster pages and updates _config.yaml; originals are preserved with a `superseded_by:` backref.