Skills de Claude Code · página 122

|

|

|

|

|

|

|

|

Track development session events in a daily markdown changelog, including file changes, test results, and key decisions.

Code review specialist for quality, security, and best practices

Debugging specialist for root cause analysis and problem resolution

Documentation specialist for README, API docs, and code comments

Validate custom agent file format and structure

Generate custom agent from template

List all available agents (core + expert)

TDD-based autonomous development loop with checkpoint recovery and observability changelog

Verify development environment is ready

Validate CLAUDE.md structure and completeness

Generate CLAUDE.md template for current project

Self-Evolving Development Loop - Dynamic skill generation with learning and evolution

View Self-Evolving Loop session status, history, and memory metrics

Problem analysis using Explore agents

Guided 5-minute onboarding for Director Mode Lite

Delegate tasks to Codex CLI to save Claude context

Delegate tasks to Gemini CLI to save Claude context

Generate hook script from template

Validate hooks configuration and scripts

>

Validate MCP configuration and suggest improvements

Create detailed execution plan with task breakdown

Complete project health audit (7 checks)

Expert-guided project setup with 6 phases

Validate skill/command file format and structure

Generate custom skill/command from template

List all available skills (core + custom)

Conventional Commits with quality checks

Test-Driven Development (TDD Red-Green-Refactor)

Test automation specialist for running tests and ensuring coverage

Complete 5-step development workflow

|

Automates the Karpathy LLM Wiki workflow: turns web, GitHub, and YouTube URLs into well-structured, citable, wikilinked pages with automatic linting and sourcing — invoke with /pin-llm-wiki

CRE Asset Management analysis suite — 9 specialist skills for post-acquisition multifamily operations including annual budgeting, monthly variance analysis, rent collection, renewal decisions, lease-up tracking, capex execution, NOI improvement, hold/sell/refi scenario analysis, and quarterly asset review memos.

CRE Brokerage Investment Sales v1 - 8 specialist skills for U.S. seller-side commercial investment sales brokers, covering assignment intake, broker opinion of value, listing proposal, OM and teaser drafting, buyer process management, bid leveling, negotiation support, and PSA-to-close coordination.

CRE Closing management suite — 2 specialist skills for closing checklist coordination and funds flow preparation for multifamily acquisitions.

CRE Document Ingestion suite — 4 specialist skills for classifying and extracting structured data from deal documents including rent rolls, T-12 financials, and offering memoranda.

CRE Due Diligence analysis suite — 7 specialist skills for multifamily property analysis including rent roll validation, expense benchmarking, market study, physical inspection, environmental review, title review, and tenant credit assessment.

CRE Financing analysis suite — 3 specialist skills for lender identification, quote comparison, and term sheet assembly for multifamily acquisition debt sourcing.

CRE Industrial analysis suite - 8 specialist skills for U.S. industrial acquisitions, covering market study, lease roster analysis, lease abstraction, tenant credit, physical inspection, underwriting, financing fit, and investment committee memo writing.

CRE Legal review suite — 6 specialist skills for PSA review, title & survey analysis, estoppel tracking, loan document review, insurance coordination, and transfer document preparation for multifamily acquisitions.

CRE Underwriting analysis suite — 3 specialist skills for building pro formas, running sensitivity scenarios, and writing investment committee memos for multifamily acquisitions.

Use whenever the user asks to install, configure, uninstall, snooze, mute, test, troubleshoot, or change settings for the echook audio notification system. Trigger phrases include "audio hooks", "audio notifications", "snooze audio", "mute claude", "claude is too loud", "test audio", "switch audio theme", "rate limit alerts", "audio webhook", "TTS", "text to speech", "notification mode", "audio only", "notification only", "debounce", "status line", "statusline", "context usage", "context window", "context monitor", "compact reminder", "uninstall audio", "audio status", "audio version", "install for cursor", "install for codex", "codex audio", "codex hooks", "cursor audio", "cursor hooks", and the slash command /audio-hooks. Also use when diagnosing why Claude Code, Cursor, or Codex is silent (or noisy) for the user, or when the user wants to monitor context window usage.

A helpful skill that does many things

A simple skill that helps with greeting messages

Preflight security scanner for AI coding agents — scans deployment config, skills/MCP servers, memory/sessions, and AI agent config files (hooks injection) for secrets, PII, prompt injection, and dangerous patterns. Runs 4 model behavior probes (persuasion, sandbagging, deception, hallucination). Supports LLM-enhanced semantic analysis. Works with OpenClaw, Claude Code, Cursor, Codex, and OpenCode. Use when a user asks for a security audit, health check, or wants to scan their AI agent setup for vulnerabilities.

Use when a task needs an isolated hidden Linux desktop or workspace-owned browser: GUI app QA, web/browser/shopping automation, sandboxed app observation, or stale workspace cleanup. Routes agent-workspace-linux MCP tools on demand. Does NOT apply to host desktop/Chrome control, generic MCP setup, or pure code/file edits.

模拟高三语文辅导老师，辅导现代文阅读、古诗文鉴赏、文言文翻译、作文写作等语文问题。重语感培养、文本解读、写作思维。当学生提出语文问题、请求分析课文、讲解古诗词、修改作文时使用。

模拟高三英语辅导老师，辅导英语阅读理解、完形填空、语法填空、写作等问题。重语言能力培养、做题技巧、词汇积累。当学生提出英语问题、请求讲解语法、分析阅读题、修改作文时使用。

模拟高三通用技术辅导老师，辅导技术设计、结构分析、流程图、算法、简单编程等通用技术问题。重实践操作、设计思维、问题解决能力培养。当学生提出技术设计、结构优化、流程设计、算法问题时使用。

模拟高三文科辅导老师，用启发式教学方法辅导政治、历史、地理等文科综合问题。侧重理解、记忆、分析能力培养。当学生提出文科问题、请求讲解历史事件、地理现象、政治原理时使用。

模拟中国高三理科辅导老师，用渐进式教学方法辅导数学、物理、化学、生物等理科问题。当学生提出理科问题、请求讲解、说"不懂"、"教我"时使用。适用于高考备考、解题辅导、概念理解。

Format markdown and publish to WeChat Official Account via bm.md rendering + WeChat official API

Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.

Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configuration and usage mistakes.

Implements agents using Deep Agents. Use when building agents with create_deep_agent, configuring backends, defining subagents, adding middleware, or setting up human-in-the-loop workflows.

Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.

Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features. Catches common mistakes in state management, graph structure, and async patterns.

Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.

Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.

Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.

Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.

Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.

Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.

Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.

Vercel AI SDK for building chat interfaces with streaming. Use when implementing useChat hook, handling tool calls, streaming responses, or building chat UI. Triggers on useChat, @ai-sdk/react, UIMessage, ChatStatus, streamText, toUIMessageStreamResponse, addToolOutput, onToolCall, sendMessage.

Use when you need to mine a conversation, session transcript, or design discussion for architectural decisions before writing ADRs. Identifies problem-solution pairs, trade-off debates, technology choices, and explicit \"[ADR]\" tags. Triggers on \"what decisions did we make\", \"extract decisions from this chat\", \"find the choices in our discussion\", or \"summarize architectural decisions\". Also useful after long planning sessions to capture decisions that were made implicitly. Does NOT write ADR documents \u2014 use adr-writing or write-adr for that.

Use when writing or formatting an ADR document using the MADR template, applying Definition of Done (E.C.A.D.R.) criteria, or verifying ADR completeness. Triggers on \"write the ADR\", \"format as MADR\", \"check ADR quality\", \"mark gaps in ADR\". Also triggers when a decision has been extracted and needs to become a document. Does NOT extract decisions from conversations (use adr-decision-extraction) or orchestrate the full extract-confirm-write workflow (use write-adr).

Use when auditing an agent codebase against the 12-Factor Agents methodology, reviewing LLM-powered system architecture, or assessing agentic app compliance. Triggers on \"analyze agent architecture\", \"12-factor audit\", \"how compliant is this agent\", or \"evaluate this LLM app\". Also applies when comparing frameworks or planning agent improvements. Not for quick checklists \u2014 this performs deep per-factor codebase analysis with file-level evidence.

Use when the user wants a cited, structured read of local documents and project knowledge. Triggers on: \"analyze these docs\", \"scan my project for context\", \"read the docs folder\", \"summarize what's in .beagle/concepts/\", \"extract context from docs/\", \"what's in this folder\", \"go read everything in X and tell me what's there\". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups (\"find this function\", \"search the repo\"), web research (use web-research), LLM-as-judge evaluation (use llm-judge), or document editing (use humanize-beagle). Produces a written scan plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

Use when the user has a fuzzy idea and wants to shape it into a concrete project spec before planning or building. Triggers on: \"brainstorm this\", \"I have an idea for...\", \"help me think through this project\", \"what should I build\", \"spec this out\". Also catches vague feature descriptions needing structured questioning to clarify scope. Does NOT write code, plan implementation, review strategy docs, or run strategy interviews \u2014 produces a WHAT/WHY spec through dialogue, not a HOW plan.

Use when comparing two or more code implementations against a spec or requirements doc. Triggers on \"which repo is better\", \"compare these implementations\", \"evaluate both solutions\", \"rank these codebases\", or \"judge which approach wins\". Also covers choosing between competing PRs or vendor submissions solving the same problem. Does NOT review a single codebase for quality \u2014 use code review skills instead. Does NOT evaluate strategy docs \u2014 use strategy-review. Requires a spec file and 2+ repo paths.

Use when the user wants to pressure-test a product, internal-tool, or OSS concept against Amazon's Working Backwards PRFAQ gauntlet before committing to a spec. Triggers on: \"work backwards\", \"write a PRFAQ\", \"press release first\", \"is this idea worth building\", \"pressure-test this concept\", \"filter this before brainstorm\", \"is this a real product\". Also catches solution-first pitches (\"I want to build X that does Y\") and technology-first pitches (\"use AI to...\") that need customer-first filtering. Produces a binary pass/fail verdict, not a polished doc. Hardcore coaching — direct, skeptical, concrete. On pass, hands off to brainstorm-beagle with a concept brief. Does NOT write code, plan implementation, scaffold projects, or draft specs.

Use as the follow-up to brainstorm-beagle when a spec has an Open Questions section (or quietly carries latent gaps) that need closing before planning or implementation can begin. Triggers on: \"resolve the open questions\", \"close the gaps in this spec\", \"research the open items\", \"finalize my spec\", \"make this spec implementation-ready\", \"answer the TBDs\". Also triggers whenever the user points at a brainstorm-beagle spec and asks for research, proposals, or answers to unresolved items. Orchestrates parallel research subagents when available (falls back to inline sequential research otherwise), proposes answers one at a time for user approval, then rewrites the spec in place so it arrives at planning with no known gaps. Does NOT write code, design implementation, or create plans — it only produces a complete spec.

Use when the user wants to build or think through a strategy via guided conversation \u2014 for a company, product, team, career, or initiative. Triggers on \"help me figure out our direction\", \"what should we focus on\", strategic planning, competitive positioning, go-to-market strategy. Also catches indirect requests like prioritization struggles or \"we have too many priorities\". Does NOT review existing strategy documents (use strategy-review) or brainstorm project features (use brainstorm-beagle).

Use when reviewing, critiquing, or stress-testing an existing strategy document. Evaluates seven dimensions \u2014 diagnosis quality, guiding policy strength, action coherence, assumption exposure, falsifiability \u2014 with optional 7S, Five Forces, Balanced Scorecard, and Hoshin Kanri lenses. Triggers on: review my strategy, poke holes in this plan, what's weak here, strategy audit, red team this. Does NOT build strategy (use strategy-interview) or brainstorm project ideas (use brainstorm-beagle).

Use when the user wants web research: gathering cited, multi-angle evidence on a specific question. Triggers on: \"research X for me\", \"do web research on\", \"look up sources for\", \"find citations for\", \"gather evidence on\", \"what does the web say about X\". Also invoked programmatically by other beagle skills (prfaq-beagle Ignition, brainstorm-beagle reference points, strategy-interview context grounding) via the companion contract. Does NOT trigger on codebase lookups (\"find this function\", \"search the repo\"), local file search, LLM-as-judge evaluation, or paywalled/auth-gated scraping. Produces a written plan, parallel-subagent findings, and a cited synthesis report on disk — never inline prose, never unsourced claims.

Use when you want to generate Architecture Decision Records from this session. Triggers on \"write ADRs\", \"document our decisions\", \"create decision records\", \"record the choices we made\". Also useful after design discussions where decisions were reached but not documented. Does NOT extract decisions alone (use adr-decision-extraction) or provide MADR template (use adr-writing). Orchestrates the full workflow: subagent extraction, user confirmation, parallel generation, and verification.

Use when you have a finalized brainstorm-beagle spec at `.beagle/concepts/<slug>/spec.md` and need a bite-sized, TDD-driven implementation plan before any code is written. Triggers on: \"write a plan\", \"plan this spec\", \"turn the spec into a plan\", \"now plan the implementation\", \"write-plan\". Reads the spec, designs the file structure, decomposes work into 2-5 minute TDD steps with exact paths and commands, self-reviews against the spec, gets user approval, then writes to `.beagle/concepts/<slug>/plan.md` and offers to generate an execution handoff prompt via the subagent-prompt skill. Does NOT brainstorm specs, write code, or execute the plan — produces the plan document (and an optional handoff prompt) only.

commit and push all local changes to remote repo

create a pull request with standardized description template

Fetch unresolved review comments from a PR and evaluate with receive-feedback skill

Applies fixes from a prior review-llm-artifacts run, with safe/risky classification. Respects verify-llm-artifacts output when present to skip false positives.

generate release notes for changes since a given tag

Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent-introduced cruft.

Optimize prompts for code-related tasks following prompt-engineering best practices. Use when refining prompts for implementation, debugging, refactoring, code review, or testing.

Process external code review feedback with technical rigor. Use when receiving feedback from another LLM, human reviewer, or CI tool. Verifies claims before implementing, tracks disposition.

Respond to review comments on a PR after evaluation and fixes

Schema for tracking code review outcomes to enable feedback-driven skill improvement. Use when logging review results or analyzing review quality.

Detects common LLM coding agent artifacts across four categories (tests, dead code, abstraction, style) over the project or changed files — using parallel subagents when the agent supports them, otherwise four sequential passes. Scans files changed since main by default; use --all for full-project scan. Triggers on LLM cruft cleanup, agent-generated code review, dead code sweeps, test-quality passes, or when the user asks to scan the whole repo.

Review implementation plans for parallelization, TDD, types, libraries, and security before execution

Analyzes feedback logs to identify patterns and suggest improvements to review skills. Use when you have accumulated feedback data and want to improve review accuracy.