Total 30,607 skills, AI & Machine Learning has 4942 skills
Showing 12 of 4942 skills
' Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems'
Manage git-backed memory repos. Load this skill when working with git-backed agent memory, setting up remote memory repos, resolving sync conflicts, or managing memory via git workflows.
Senior Architect for @google/genai v1.35.0+. Specialist in Structured Intelligence, Context Caching, and Agentic Orchestration in 2026.
Shows the Wasp plugin's available features, commands, and skills.
Researches topics and trends for blog content with parallel multi-agent execution. USE WHEN orchestrator invokes research phase OR user says 'research topic', 'find trends', 'gather information for blog'.
Implementing safety filters, content moderation, and guardrails for AI system inputs and outputs
Comprehensive guide for building Model Context Protocol (MCP) servers with support for tools, resources, prompts, and authentication. Use when: (1) Creating custom MCP servers, (2) Integrating external APIs with Claude, (3) Building tool servers for specialized domains, (4) Creating resource providers for documentation, (5) Implementing authentication and security
Use this agent for audits, debugging nasty bugs, deep research, getting second opinions on approaches, reviewing commits for correctness, or analyzing complex problems. Invoke when you need advanced reasoning about difficult issues. Use PROACTIVELY when encountering complex bugs, architectural decisions, or when a thorough review would prevent future issues.
Implement Mistral AI reference architecture with best-practice project layout. Use when designing new Mistral AI integrations, reviewing project structure, or establishing architecture standards for Mistral AI applications. Trigger with phrases like "mistral architecture", "mistral best practices", "mistral project structure", "how to organize mistral", "mistral layout".
Build specialized openclaw agents with proper workspace structure, identity, and skills
Data engineering, machine learning, AI, and MLOps. From data pipelines to production ML systems and LLM applications.
Multi-agent PR and code review workflow for projects using multiple AI assistants (Claude, GitHub Copilot/Codex, Gemini Code Assist). Use when working with pull requests, code reviews, commits, or addressing review feedback. Teaches how to check all feedback sources (conversation, inline, reviews), respond to inline bot comments, create Fix Reports, and coordinate between agents that use different comment formats. Critical for ensuring no feedback is missed from external review bots.