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Found 13,200 Skills
Teach AI agents how to query data warehouses accurately using ktx - an executable context layer with skills, memory, and a semantic layer
Add policy enforcement, zero-trust identity, and execution sandboxing to AI agents with Microsoft's Agent Governance Toolkit
The agentmemory plugin hooks that capture observations automatically across the agent session lifecycle. Use when explaining how memory gets captured without manual saves, when debugging missing observations, or when tuning what gets recorded.
Ingest raw context the user pastes or points at — a ticket, a design doc, meeting notes, a spec, a URL, referenced files/paths — and have an agent READ and UNDERSTAND all of it, then synthesize a well-formed feature brief (goal, scope, constraints, and load-bearing unknowns) that feeds sdd-clarify and the sdd-feature-flow harness. Use at the very start of a feature when you have source material instead of a one-line goal, or whenever the user says "here's the context" / "read this" / dumps a ticket or doc.
Runs a doer -> verifier-panel -> consensus loop to verify a deliverable before it ships. An orchestrator freezes acceptance criteria before implementation, dispatches a doer, then convenes a context-walled panel of independent verifiers - including an adversary with an explicit must-oppose mandate - for evidence-anchored review adjudicated to a SHIP / SHIP_WITH_CAVEATS / ITERATE / BLOCK / ESCALATE verdict logged to a ledger. Use for multi-agent verification of any artifact - code slices, plans, documents, audits - whenever asked to verify a deliverable, vet a plan, run a consensus review or independent review, set up a doer-verifier loop, or gate a ship decision. Works on any platform with parallel subagents; degrades to sequential fresh-context sessions without them. Not for trivial single-file edits or ordinary code review.
Drive comprehensive test creation for a feature, spec, module, or PR. Decide what must be tested, pick the testing strategy at the lowest sufficient cost, drive the test producers to author the tests, run them, and record the session — what was tested, by what test type, and what passed — in specs/<feature>/test-spec.md and test-report.md. Drives the producers create-yaml-tests and create-agent-tests.
Build autonomous AI agents with Claude Agent SDK. Structured outputs guarantee JSON schema validation, with plugins system and hooks for event-driven workflows. Prevents 14 documented errors. Use when: building coding agents, SRE systems, security auditors, or troubleshooting CLI not found, structured output validation, session forking errors, MCP config issues, subagent cleanup.
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
Configure AI coding agents like Cursor, GitHub Copilot, or Claude Code with project-specific patterns, coding guidelines, and MCP servers for consistent AI-assisted development.
Q-learning, DQN, PPO, A3C, policy gradient methods, multi-agent systems, and Gym environments. Use for training agents, game AI, robotics, or decision-making systems.
Register and manage AI agent identities on Avalanche C-Chain using ERC-8004 (Trustless Agents). Use this skill when the user wants to register an AI agent on-chain, give or read reputation feedback, request validation, or interact with ERC-8004 identity/reputation/validation registries on Avalanche mainnet or Fuji testnet.
Manage concentrated liquidity (CLMM) positions on DEXs like Meteora and Raydium. Create, monitor, and rebalance LP positions automatically.