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Found 11,818 Skills
macOS native app automation CLI for AI agents. Use when the user needs to interact with macOS desktop applications, including opening apps, clicking buttons, toggling settings, filling forms, reading UI state, automating System Settings, controlling Finder, Safari, or any native app.
Scoring formulas and analytical frameworks for GitHub workflow agents. Covers repository health scoring (0-100, A-F grades), priority scoring for issues/PRs/discussions, confidence levels for analytics findings, delta tracking (Fixed/New/Persistent/Regressed), velocity metrics, contributor metrics, bottleneck detection, and trend classification. Use when computing scores, tracking remediation progress, building prioritized dashboards, or detecting workflow bottlenecks.
Use when setting up or configuring Laravel Boost for AI-assisted development — package installation, MCP server configuration, guideline customization, skill authoring, documentation API integration. Trigger conditions: install Laravel Boost, configure MCP for IDE, create custom AI guidelines, write project-specific skills, verify MCP tool connectivity, update Boost after dependency changes, extend Boost for custom agents.
Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.
Create and configure configs in LaunchDarkly. Helps you choose between agent vs completion mode, create the config, add variations with models and prompts, and verify the setup.
List recent git commits that are linked to agent sessions, optionally filtered by branch or repo. Use when the user asks "show agent commits", "what has the agent shipped", or wants a list of commits with their session context.
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-management`).
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.
Guide for creating effective skills following best practices. Use when creating or updating skills that extend agent capabilities.
AI agent patterns with Trigger.dev - orchestration, parallelization, routing, evaluator-optimizer, and human-in-the-loop. Use when building LLM-powered tasks that need parallel workers, approval gates, tool calling, or multi-step agent workflows.
Guides architectural decisions for Deep Agents applications. Use when deciding between Deep Agents vs alternatives, choosing backend strategies, designing subagent systems, or selecting middleware approaches.