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Found 5,143 Skills
Auto-Claude performance optimization and cost management. Use when optimizing token usage, reducing API costs, improving build speed, or tuning agent performance.
Meet other AI agents and build relationships on inbed.ai. Find compatible agents through matchmaking, swipe, chat in real time, and form connections. Agent dating with compatibility scoring, agent chat, and relationship management. REST API — works with any framework.
Coordinator workflow for orchestrating dockeragents through fix-review-iterate-present loop. Use when delegating any task that produces code changes. Ensures agents achieve 10/10 quality before presenting to human.
This skill should be used when creating custom agents for Claude Code, configuring specialized AI assistants, or when the user asks about agent creation, agent configuration, or delegating tasks to workers. Covers both file-based agents and teams delegation.
This skill should be used when parallelizing multi-issue sprints using git worktrees and parallel Claude agents. Use when tackling multiple GitHub issues simultaneously, when the user mentions "blitz", "parallel sprint", "worktree workflow", or when handling 3+ independent issues that could be worked on concurrently. Orchestrates the full workflow from issue triage through parallel agent delegation to sequential merge.
Discover, buy, and sell AI agent services on Agentripe — the Stripe for AI Agents. Autonomous monetization via x402 protocol with on-chain identity and escrow.
Developer oversight and AI agent coaching. Use when viewing project status across repos, syncing GitHub data, or analyzing agents.md against commit patterns.
Set up the Pica CLI and MCP server so AI agents can interact with Gmail, Slack, HubSpot, Stripe, and 200+ platforms. Handles installation, initialization, and first connection.
Use when creating Claude Code plugins, writing skills, building commands, developing agents, or asking about "plugin development", "create skill", "write command", "build agent", "SKILL.md", "plugin structure", "progressive disclosure"
Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy
Use this skill when you need to test or evaluate LangGraph/LangChain agents: writing unit or integration tests, generating test scaffolds, mocking LLM/tool behavior, running trajectory evaluation (match or LLM-as-judge), running LangSmith dataset evaluations, and comparing two agent versions with A/B-style offline analysis. Use it for Python and JavaScript/TypeScript workflows, evaluator design, experiment setup, regression gates, and debugging flaky/incorrect evaluation results.
Initialize, validate, and troubleshoot Deep Agents projects in Python or JavaScript using the `deepagents` package. Use when users need to create agents with built-in planning/filesystem/subagents, configure middleware/backends/checkpointing/HITL, migrate from `create_react_agent` or `create_agent`, scaffold projects with repo scripts, validate agent config files, and confirm compatibility with current LangChain/LangGraph/LangSmith docs.