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Found 10,156 Skills
Structured clarification before decisions. Use when user is in PLANNING mode, explicitly asks to plan or discuss, or when agent faces choices requiring user input. Ensures agent asks questions instead of making autonomous decisions when multiple valid approaches exist or context is missing.
Writes agent outputs to numbered thread stage files. Called by agents after domain work completes. Maps agent type to stages, updates frontmatter status, and records completion metadata. Stage 1 (1-input.md) is never written by this skill.
Comprehensive research and synthesis agent specializing in multi-source information gathering, critical analysis, and integrated knowledge synthesis. Excels at complex research projects requiring systematic investigation across domains, evidence evaluation, and coherent narrative construction.
After the task execution is completed, prompt the user to open a new Agent to review the uncommitted git code. Athletes should not act as referees; proceed with the wrap-up only after the review is approved.
Extracts key learnings from conversations, debugging sessions, and failed attempts. Use at session end or after solving complex problems to capture insights. Stores discoveries in memory (via amplihack.memory.discoveries), suggests PATTERNS.md updates, and recommends new agent creation. Ensures knowledge persists across sessions via Kuzu memory backend.
Guide for designing effective MCP servers with agent-friendly tools. Use when creating a new MCP server, designing MCP tools, or improving existing MCP server architecture.
Execute Grimoire spells inside an agent session (VM mode). Use for in-agent prototyping, validation, and best-effort execution.
Comprehensive codebase quality audit with parallel agent orchestration, GitHub issue creation, automated PR generation per issue, and PM-prioritized recommendations. Use for code review, refactoring audits, technical debt analysis, module quality assessment, or codebase health checks.
Multi-agent coordination patterns for OpenCode swarm workflows. Use when work benefits from parallelization or coordination. Covers: decomposition, worker spawning, file reservations, progress tracking, and review loops.
Letta framework for building stateful AI agents with long-term memory. Use for AI agent development, memory management, tool integration, and multi-agent systems.
Scaffold development rules for AI coding agents. Auto-invoked when user asks about setting up rules, coding conventions, or configuring their AI agent environment.
Coordinate multi-agent code review with specialized perspectives. Use when conducting code reviews, analyzing PRs, evaluating staged changes, or reviewing specific files. Handles security, performance, quality, and test coverage analysis with confidence scoring and actionable recommendations.