CLI Tools

Write pytest tests for FastAPI endpoints using TestClient including request/response testing, authentication, and error handling. Use when testing REST API endpoints.

This skill enables Claude to monitor and analyze application error rates to improve reliability. It is used when the user needs to track and understand errors occurring in their application, including HTTP errors, application exceptions, database errors, external API errors, background job errors, and client-side errors. Use this skill when the user asks to "monitor errors", "analyze error rates", "track application errors", or requests help with "error monitoring". It sets up comprehensive error tracking and alerting based on defined thresholds.

Multi-agent task orchestration using the spec CLI. Guides agents on claiming tasks, parallel execution, blocking, and coordination. Use when working with multiple agents, claiming tasks, or managing parallel work.

DevOps scripting with Bash, Python, and Go for automation, tooling, and infrastructure management

Particle lifecycle management—emission/spawning, death conditions, object pooling, trails, fade-in/out, and state transitions. Use when particles need birth/death cycles, continuous emission, trail effects, or memory-efficient recycling.

Deterministic wrapper for tonl CLI (npm package) for structured data operations in JSON/TONL formats. Use when converting JSON↔TONL, querying structured data, validating TONL schemas, or generating statistics. Maps directly to CLI subcommands (encode, decode, query, get, validate, stats).

This skill enables Claude to monitor and analyze application error rates to improve reliability. It is used when the user needs to track and understand errors occurring in their application, including HTTP errors, application exceptions, database errors, external API errors, background job errors, and client-side errors. Use this skill when the user asks to "monitor errors", "analyze error rates", "track application errors", or requests help with "error monitoring". It sets up comprehensive error tracking and alerting based on defined thresholds.

Search, filter, and retrieve Claude/Codex history indexed by the automem CLI. Use when the user wants to index history, run lexical/semantic/hybrid search, fetch full transcripts, or produce LLM-friendly JSON output for RAG.

Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing

Use when user provides YouTube URL and says "document this", "create notes", or "save this video". Automatically extracts transcript, determines folder placement, and generates Knowledge Framework documentation with MECE structure, Mermaid diagrams, and clickable timestamp citations.

Hypothesis-driven hunt plan for suspicious PowerShell, plus query snippets for common telemetry.

Use when the user asks to run Codex CLI (codex exec, codex resume) or references OpenAI Codex for code analysis, refactoring, or automated editing

Handle data filtering, searching, and manipulation operations on JSON data loaded in the static website. Use this skill when working with client-side data querying, filtering records, or transforming data for display purposes.

Generate ASCII art typography using pyfiglet. Use when user asks for ASCII art text, banners, headers, logo text, terminal art, or decorative typography. Curated whitelist of 24 display fonts available, agent can also use any of 500+ pyfiglet fonts.

Orchestrate parallel CLI agents (Claude Code, Codex, Gemini) for competitive evaluation. Use when user says "run multi-agent", "compare agents", "launch competitive evaluation", "use parallel agents", or complex tasks (>7/10) where multiple approaches exist and best solution matters.

How to use the file-factory CLI to create new React components, hooks, context providers, Next.js pages along with associated tests and storybook stories. ALWAYS use this tool instead of manually creating new components/hooks/contexts/pages to ensure consistent file structure and conventions.

React Server Components（RSC）の実装パターンとNext.js App Routerにおけるベストプラクティスを提供する専門スキル。サーバーコンポーネントとクライアントコンポーネントの責務分離、データフェッチの最適化、Suspenseとストリーミングの活用を支援します。Anchors:• 『Learning React Server Components』（Tejas Kumar）/ 適用: RSCアーキテクチャ / 目的: サーバーとクライアント間の責務分離• Next.js App Router公式ドキュメント / 適用: RSC実装パターン / 目的: Next.js固有の最適化手法• Dan Abramovのブログ / 適用: RSC設計思想 / 目的: 第一原理からの理解Trigger:Use when implementing Next.js App Router, designing Server Components, defining Client Component boundaries, optimizing data fetching, integrating Suspense, or implementing streaming SSR.rsc, server components, client components, next.js app router, use client, suspense, streaming, data fetching

Use this skill when creating, running, or managing database migrations for PostgreSQL or ClickHouse. This includes creating new migrations, running migrations, checking migration status, rollback operations, seeding data, or troubleshooting migration issues.

MANDATORY invocation for ALL LLM-related work. Invoke immediately when: - ANY mention of model names, IDs, or versions - ANY configuration of AI providers or APIs - ANY defaults/constants for LLM settings - ANY prompt engineering or modification - ANY discussion of model capabilities or features - ANY changes to AI-related dependencies - Reading/writing .env files with AI config - Modifying aiProviders.ts, prompts.ts, or similar - Reviewing AI-related pull requests - Debugging LLM integration issues CRITICAL: Training data lags reality by months. ALWAYS research first. Use WebSearch, Exa MCP, or Gemini CLI before making ANY LLM decisions.

Remote control tmux sessions for interactive CLIs by sending keystrokes and scraping output.