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Found 7,151 Skills
Drafts initial ticket requirements by asking targeted clarifying questions and producing a structured ticket with Title, Body, and Acceptance Criteria. Use when a user has a request that needs to be converted into a clear tracker-ready ticket without codebase or architecture analysis.
[MCP WRAPPER] Programmatically create/modify Godot scenes using Godot MCP tools. Orchestrates mcp_godot_create_scene, mcp_godot_add_node, mcp_godot_load_sprite into agentic workflows. Use when user requests scene generation/automation via MCP. Keywords MCP, scene automation, programmatic scene building, node hierarchy.
Create, validate, and convert skills for the agent ecosystem. Enforces standardized structure for consistency. Enables self-evolution by creating new skills on demand, converting MCP servers and codebases to skills.
AI agents as force multipliers for quality work. Core skill for all 19 QE agents using PACT principles.
Dispatches one subagent per independent domain to parallelize investigation/fixes. Use when you have 2+ unrelated failures (e.g., separate failing test files, subsystems, bugs) with no shared state or ordering dependencies.
Guides implementation of agent memory systems, compares production frameworks (Mem0, Zep/Graphiti, Letta, LangMem), and designs persistence architectures for cross-session knowledge retention. Use when the user asks to "implement agent memory", "persist state across sessions", "build knowledge graph for agents", "track entities over time", "add long-term memory", "choose a memory framework", or mentions temporal knowledge graphs, vector stores, entity memory, or memory benchmarks (LoCoMo, LongMemEval).
Build autonomous RAG agents that reason, plan, and use tools for complex retrieval tasks. Use this skill when simple retrieve-and-generate isn't enough. Activate when: agentic RAG, RAG agent, multi-step retrieval, tool-using RAG, autonomous retrieval, query decomposition.
Frames coding-agent work sessions with explicit intent capture and drift monitoring. Use when a session transitions from planning/Q&A to implementation for coding tasks, refactors, feature builds, bug fixes, or other multi-step execution where scope drift is a risk.
Implements tracker subtasks tagged `implement`, publishes/updates the PR, and routes review using handoff-first context loading, lazy artifact reads, and rework_mode support.
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Structured checkpoint format for requesting human input. When an agent needs a decision, it must stop, present context, show options, and wait. Activate when delegating to subagents, running background tasks, or hitting any decision point that requires human judgment.
Set up a full AI ensemble/mob programming team for any software project. Creates team member profiles (.team/), coordinator instructions (.team/coordinator-instructions.md), project owner constraints (PROJECT.md), team conventions (AGENTS.md), architectural decisions (docs/ARCHITECTURE.md), domain glossary, and supporting docs. Use when: (1) starting a new project and wanting a full expert agent team, (2) the user asks to "set up a team", "create a mob team", "set up ensemble programming", or "create agent profiles", (3) converting an existing project to the driver-reviewer mob model, (4) the user wants AI agents to work as a coordinated product team with retrospectives and consensus-based decisions.