Total 50,611 skills, AI & Machine Learning has 8484 skills
Showing 12 of 8484 skills
Current Claude AI and Claude Code capabilities, features, and limits. Consult before making claims about what Claude can or cannot do, what features exist, model availability, pricing, or environment differences. Use when answering "can Claude do X?", comparing Claude AI vs Code, checking current model specs, or advising on which environment suits a task.
Self-improve AI Factory skills based on project context, accumulated patches, and codebase patterns. Analyzes what went wrong, what works, and enhances skills to prevent future issues. Use when you want to make AI smarter for your project.
Comprehensive operational knowledge for ZeroClaw, the fast, small, fully autonomous AI assistant infrastructure built in Rust. Covers CLI, 30 providers, 14 channels, config, hardware, deployment, and security.
FORGE Vector Memory — Diagnostic tool for the vector memory index. Operations: sync, search, status, reset. Usage: /forge-memory sync | /forge-memory search "query" | /forge-memory status | /forge-memory reset
[v3] Resolve all PR comments using parallel agents with full workflow and verification gate
Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".
VM0 API for running AI agents in secure sandboxes. Use this skill to execute agents, manage runs, and download outputs (artifacts) and inputs (volumes) via the VM0 platform API.
Branch off a conversation to handle tangents. Outputs context summary and ready-to-paste command for a new terminal session.
Tech Spec을 분석하여 Epic/Story 구조를 생성하고, Story 5개 이상일 때 병렬로 Story 문서를 작성한다. Distribute 조율 패턴.
Build a structured taxonomy of failure modes from open-coded trace annotations. Use this skill whenever the user has freeform annotations from reviewing LLM traces and wants to cluster them into a coherent, non-overlapping set of binary failure categories (axial coding). Also use when the user mentions "failure modes", "error taxonomy", "axial coding", "cluster annotations", "categorize errors", "failure analysis", or wants to go from raw observation notes to structured evaluation criteria. This skill covers the full pipeline: grouping open codes, defining failure modes, re-labeling traces, and quantifying error rates.
AI Native Camp Day 5 콘텐츠 소화 스킬 만들기. fetch-tweet, fetch-youtube, content-digest 3개 스킬을 직접 만들고 활용한다. "5일차", "Day 5", "fetch", "콘텐츠 스킬", "트윗 스킬", "유튜브 스킬", "다이제스트 스킬" 요청에 사용.
Consolidate agent output results into structured final output, supporting multiple formats. Suitable for consolidating analysis reports and generating structured reports