decision-tree-design
Systematic decision tree and epic generation through Socratic discovery
Systematic decision tree and epic generation through Socratic discovery
Creates new AI agent skills following the Agent Skills spec. Trigger: When user asks to create a new skill, add agent instructions, or document patterns for AI.
Add speech-to-text transcription widget to web pages using Whisper server. Creates WhisperWidget JS component that records audio, sends to whisper server, and returns transcribed text. Use when adding voice input to web applications.
ユーザーメモリ、プロジェクトメモリ、READMEを再読み込み。「コンテキストを再読み込み」「設定をリロード」「CLAUDE.mdを読み直して」と言われた時、またはclear後にコンテキストを復元したい時に使用
Provides real-time weather forecasts using OpenWeatherMap API. Use when users ask about current or future weather conditions, temperature, precipitation, or weather-dependent planning for any location (e.g., 'What's the weather in Paris tomorrow?', 'Will it rain in Seattle this weekend?', 'Should I bring a jacket to Denver?').
Natural language understanding, intent classification, context management, reference resolution, and conversation history analysis for agentful
Use when implementing different agent types in Microsoft Agent Framework. Triggers: "ChatAgent", "BaseAgent", "WorkflowAgent", "A2A agent", "custom agent class". NOT for: Non-Microsoft agent frameworks or simple single-agent scenarios.
Phase 2 of Ontology Builder Pipeline. AI acts as domain SME to analyze raw inputs, extract entities/workflows/rules, fill knowledge gaps using market expertise. Use after Phase 1 ingestion is complete.
Build and run AI agents using Claude Code CLI. Use when developing autonomous agents, multi-agent systems, CI/CD automation, or scripting Claude for programmatic tasks. Covers authentication, headless mode (-p), JSON output parsing, tool restrictions, subagents, and orchestration patterns.
Build production-grade applications with OpenAI APIs (direct or via Azure OpenAI). Covers structured outputs, function calling, streaming, Assistants API, Responses API, Agents SDK, cost optimization, rate limiting, error handling, and batch processing. IMPORTANT: Before implementing, gather context about the target environment including: - API provider (OpenAI direct vs Azure OpenAI) - Primary use cases (chat, function calling, structured outputs, assistants, agents) - Cost constraints and optimization requirements - Error handling and retry strategy needs
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
First step for any task. Uses HelixDB to map code relationships, then loads precise context into Repo Prompt. Trigger when starting work, feeling lost, or context seems stale.
MANDATORY protocol for parallel agent execution when facing 3+ independent failures or tasks that can be investigated without shared state or dependencies - dispatches multiple Claude agents to investigate and fix independent problems concurrently
Azure PostgreSQL AI Integration - Vector search, RAG pipelines, and agent memory with pgvector and azure_ai extensions
Enforce honest, evidence-bound answers. Use when the user asks to be honest/truthful, expresses frustration or urgency, or when confidence is low and errors are costly.
Append conversation context to cumulative project history - never overwrites
Search and retrieve past decisions from Mother-Harness long-term memory
Retrieve relevant context from past sessions before starting implementation. Use when beginning work on a task, when the user describes what to build, when about to write significant code, or when stuck on a problem that may have been solved before.