agent-developing-agents
AI agent development standards including frontmatter structure, naming conventions, tool access patterns, model selection, and Bash-only file operations for .claude/ folders
AI agent development standards including frontmatter structure, naming conventions, tool access patterns, model selection, and Bash-only file operations for .claude/ folders
ChatGPT-style deep research strategy with problem decomposition, multi-query generation (3-5 variations per sub-question), evidence synthesis with source ranking, numbered citations, and iterative refinement. Use for complex architecture decisions, multi-domain synthesis, strategic comparisons, technology selection. Keywords: architecture, integration, best practices, strategy, recommendations, comparison.
AIエージェント・システムロール・ペルソナのための役割プロンプト設計スキル。 責務分離、専門家思考様式の適用、効果的なプロンプト構造化の指針を提供する。 Anchors: • The Pragmatic Programmer (Andrew Hunt, David Thomas) / 適用: DRY原則・責務分離 / 目的: ロール設計の品質向上 • Domain-Driven Design (Eric Evans) / 適用: ユビキタス言語・境界づけられたコンテキスト / 目的: 責務境界の明確化 • Thinking, Fast and Slow (Daniel Kahneman) / 適用: 専門家思考様式の理解 / 目的: 適切な思考モード設計 Trigger: Use when designing AI agent roles, system prompts, persona definitions, or separating responsibilities between agents. role prompting, persona design, agent role, system prompt, responsibility separation, ロール設計, ペルソナ
This skill should be used when the user asks to "/gobby-plan", "create plan", "plan feature", "write specification". Guide users through structured specification planning and task creation.
Add a new AI provider or model for recipe generation. Use when adding support for a new LLM provider (Anthropic, Google, etc.) or adding models to an existing provider.
Claude Codeの拡張機能(スキル、サブエージェント、プロジェクト設定)をユーザーの自然言語指示から自動生成するスキル。「○○を作るスキルを作って」「○○エージェントを作って」「CLAUDE.mdを設定して」などのリクエスト時に使用。v2.1.1以降、スラッシュコマンドとスキルは統合されたため、すべてスキルとして作成。
Headless browser automation CLI for AI agents. Use when you need to interact with web pages, fill forms, click buttons, scrape content, take screenshots, or automate any browser-based workflow. Provides snapshot-based element selection optimized for AI agents, session management for parallel browsing, and full Playwright capabilities via CLI.
Comprehensive Three.js and React Three Fiber skill for creating 3D scenes, characters, NPCs, procedural generation, animation retargeting, and interactive experiences. Use when user asks to "create Three.js scene", "setup React Three Fiber", "add 3D character", "create NPC AI", "procedural 3D generation", "retarget animation", "setup avatar system", or "create 3D game".
Patterns for launching and managing parallel subagents efficiently.
Complete llama.cpp C/C++ API reference covering model loading, inference, text generation, embeddings, chat, tokenization, sampling, batching, KV cache, LoRA adapters, and state management. Triggers on: llama.cpp questions, LLM inference code, GGUF models, local AI/ML inference, C/C++ LLM integration, "how do I use llama.cpp", API function lookups, implementation questions, troubleshooting llama.cpp issues, and any llama-cpp or ggerganov/llama.cpp mentions.
Create new AI agent skills following the SKILL.md guidelines. Generates complete skill structure including SKILL.md with YAML frontmatter, optional bundled resources (scripts/, references/, assets/), and updates the repository README.md. Validates user input and requests clarification when information is insufficient.
Designs and optimizes prompts for large language models to achieve better, more consistent outputs. Trigger keywords: prompt, LLM, GPT, Claude, prompt engineering, AI prompts, few-shot, chain of thought.
プロンプトのテスト、評価、反復改善を専門とするスキル。A/Bテスト、評価メトリクス、自動化されたプロンプト品質保証により、本番環境で信頼性の高いプロンプトを実現します。 Anchors: • Test-Driven Development: By Example (Kent Beck) / 適用: Red-Green-Refactorサイクル / 目的: 反復的な品質改善 • LLM-as-a-Judge pattern / 適用: 自動評価とスコアリング / 目的: スケーラブルな品質評価 • A/B Testing for AI Systems / 適用: プロンプト比較実験設計 / 目的: データドリブンな改善 Trigger: Use when testing prompts, evaluating prompt quality, running A/B tests on prompts, implementing automated prompt evaluation, or establishing continuous prompt improvement cycles. Keywords: prompt testing, A/B testing, evaluation metrics, LLM-as-a-judge, prompt quality, automated evaluation, regression testing
Claude Agent SDK(@anthropic-ai/claude-agent-sdk)を使用したエージェント統合の実装を専門とするスキル。 query() API、Hooksシステム、Permission Control、Electron統合、ストリーミング処理を支援します。 Anchors: • Claude Agent SDK Official Docs / 適用: SDK API、Hooks、Permissions / 目的: 公式パターンに準拠した実装 • Electron IPC Best Practices / 適用: Main-Renderer間通信 / 目的: セキュアなプロセス間通信 • TypeScript Handbook / 適用: 型定義、ジェネリクス / 目的: 型安全なSDK統合 Trigger: Claude Agent SDKを使用したエージェント機能実装、query() APIストリーミング処理、Hooksシステム実装、Electron統合、Permission Control設計、MCP統合を行う場合に使用。 claude-agent-sdk, query API, PreToolUse, PostToolUse, PermissionRequest, Electron IPC, MCP, ストリーミング, 権限制御
Systematic prompt evaluation framework with MATH, GSM8K, and Game of 24 benchmarks. Use when evaluating prompt effectiveness on standard benchmarks, comparing meta-prompting strategies quantitatively, measuring prompt quality improvements, or validating categorical prompt optimizations against ground truth datasets.
This skill should be used when the user asks to "add AI chat", "implement streaming UI", "use useChat hook", "add AI completion", "implement useCompletion", "create conversational interface", "add streaming text", "implement tool calling UI", "create generative UI", "add AI-powered features", "implement StreamableUI", or mentions AI SDK, streaming responses, chat interfaces, or AI-generated content in React/Next.js applications.