Loading...
Loading...
Found 4,459 Skills
Expert in observing, benchmarking, and optimizing AI agents. Specializes in token usage tracking, latency analysis, and quality evaluation metrics. Use when optimizing agent costs, measuring performance, or implementing evals. Triggers include "agent performance", "token usage", "latency optimization", "eval", "agent metrics", "cost optimization", "agent benchmarking".
Run the mandatory verification stack when changes affect runtime code, tests, or build/test behavior in the OpenAI Agents Python repository.
Use when Elixir OTP patterns including GenServer, Supervisor, Agent, and Task. Use when building concurrent, fault-tolerant Elixir applications.
Guides architects on when and how to use goal-seeking agents as a design pattern. This skill helps evaluate whether autonomous agents are appropriate for a given problem, how to structure their objectives, integrate with goal_agent_generator, and reference real amplihack examples like AKS SRE automation, CI diagnostics, pre-commit workflows, and fix-agent pattern matching.
PocketFlow framework for building LLM applications with graph-based abstractions, design patterns, and agentic coding workflows
Spawn a Team Leader agent that manages multiple sub-agents working toward a common goal. Team Leader reads requirements, decomposes work, assigns personalities and tasks, manages communication between team members, tracks progress, and reports results following ogt-docs task workflow. Integrates fully with docs-first system via task signals and status tracking.
Amazon Bedrock AgentCore platform for building, deploying, and operating production AI agents. Covers Runtime, Gateway, Browser, Code Interpreter, and Identity services. Use when building Bedrock agents, deploying AI agents to production, or integrating with AgentCore services.
Amazon Bedrock AgentCore multi-agent orchestration with Agent-to-Agent (A2A) protocol. Supervisor-worker patterns, agent collaboration, and hierarchical delegation. Use when building multi-agent systems, orchestrating specialized agents, or implementing complex workflows.
Guide for creating AI subagents with isolated context for complex multi-step workflows. Use when users want to create a subagent, specialized agent, verifier, debugger, or orchestrator that requires isolated context and deep specialization. Works with any agent that supports subagent delegation. Triggers on "create subagent", "new agent", "specialized assistant", "create verifier".
Chain agents together in sequential or branching workflows with data passing
Expert knowledge of GitHub Copilot CLI - installation, configuration, usage, custom agents, MCP servers, and version management. Use when asking about copilot cli, copilot commands, installing copilot, updating copilot, copilot features.
Create or update CLAUDE.md and AGENTS.md files following official best practices. Use when asked to create, update, audit, or improve project configuration files for AI agents, or when users mention "CLAUDE.md", "AGENTS.md", "agent config", or "agent instructions".