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Found 1,677 Skills
Agent-based declarative testing with YAML test specs. Tests run in sub-agents to preserve main context while executing many tests. Supports MCP servers, APIs, and browser automation. Use when: testing MCP servers, running integration tests, validating tool behavior after changes, or creating regression test suites. Keywords: yaml tests, agent testing, mcp test, integration tests.
Debug MCP server communication. Use for troubleshooting MCP integrations, viewing traffic, and analyzing latency.
Self-modifying AI agent configuration via ruler + MCP + DuckDB. All behavior mods become one-liners.
Automate Segment tasks via Rube MCP (Composio): track events, identify users, manage groups, page views, aliases, batch operations. Always search tools first for current schemas.
Automate Mixpanel tasks via Rube MCP (Composio): events, segmentation, funnels, cohorts, user profiles, JQL queries. Always search tools first for current schemas.
Automate Stripe tasks via Rube MCP (Composio): customers, charges, subscriptions, invoices, products, refunds. Always search tools first for current schemas.
Build stateless MCP servers with TypeScript on Cloudflare Workers using @modelcontextprotocol/sdk. Provides patterns for tools, resources, prompts, and authentication (API keys, OAuth, Zero Trust). Use when exposing APIs to LLMs, integrating Cloudflare services (D1, KV, R2, Vectorize), or troubleshooting export syntax errors, unclosed transport leaks, or CORS misconfigurations.
Guidelines for integrating Figma designs with development workflows using MCP servers and best practices
Claude Code: skills, agents, hooks, commands, MCP servers, IDE integrations.
Use when tasks require current, source-backed technical information from MCP tools. Apply for library/API questions, dependency version checks, third-party integration work, framework- or SDK-specific debugging, and any case where stale model knowledge could cause incorrect guidance.
Automatically intercepts and optimizes prompts using the prompt-learning MCP server. Learns from performance over time via embedding-indexed history. Uses APE, OPRO, DSPy patterns. Activate on "optimize prompt", "improve this prompt", "prompt engineering", or ANY complex task request. Requires prompt-learning MCP server. NOT for simple questions (just answer them), NOT for direct commands (just execute them), NOT for conversational responses (no optimization needed).
Find, install, and configure MCP servers. Use proactively for MCP discovery, OAuth setup, env vars, stdio vs SSE transport, or troubleshooting MCP connections. Examples: - user: "Add the filesystem MCP server" → read server file, add to mcpServers in opencode.json, verify transport type - user: "How do I use MCP with GitHub?" → check catalog, install @modelcontextprotocol/server-github, configure OAuth token - user: "MCP not connecting" → check transport type (stdio/SSE), verify args/command, check env vars are passed - user: "What MCPs are available?" → run list_mcps.py, show catalog with auth types and install commands