Total 50,523 skills, AI & Machine Learning has 8481 skills
Showing 12 of 8481 skills
Execute Python code in isolated rootless containers with MCP server proxying for token-efficient agent workflows
Expert in using ktx, the executable context layer for data and analytics agents that enables accurate querying through MCP with skills, memory and a semantic layer
Integrate Anki spaced repetition flashcards with AI assistants through Model Context Protocol for study sessions, deck management, and card creation
Local MCP memory server for AI coding assistants with verbatim recall, semantic search, and automatic session capture
Use OpenAI Codex CLI through MCP to get AI-powered code analysis, generation, review, and web search directly in your editor
Context layer for data and analytics AI agents with semantic layer, skills, and memory via MCP
Comprehensive memory quality review across 6 dimensions: purity, freshness, coverage, clarity, relevance, and structure. Generates prioritized findings with specific memory references and actionable recommendations.
Delegate tasks to AI agents via Box0. Use when the user asks to review code, check security, run tests, compare tools, get multiple perspectives, research a topic, analyze data, write docs, or any task that could benefit from specialized or parallel execution. Also use when the user mentions agent names or says "ask", "delegate", "get opinions from", or "have someone".
Use when the user explicitly asks to invoke another coding agent CLI as a subagent. Triggers include phrases like 'get a second opinion from Codex', 'have Gemini review this', 'run this through Claude Code', 'ask another agent', or 'use a different model for this'. Supports Claude Code, Codex CLI, and Gemini CLI. Never invoke autonomously.
Build and use the verification infrastructure coding agents need to prove their work. Use when: a repo has no bootable dev environment, no real-surface tests, or no interaction layer an agent can use; auditing or grading a repo's agent-readiness; verifying changes work end to end on real surfaces; or when harness gaps block reliable agent output.
Convert structured UX specs and product context into a sequenced prompts.md file for Claude Code. Use when a user has completed upstream design thinking (problem framing, PRD, UX spec) and needs to translate that into step-by-step prompts that coding agents can execute incrementally. This skill bridges design artifacts to code generation.
Python port of Claude Code agent harness — tools, commands, task orchestration, and CLI entrypoint via oh-my-codex