Total 50,650 skills, AI & Machine Learning has 8490 skills
Showing 12 of 8490 skills
Create, audit, and maintain CLAUDE.md documentation files that configure Claude Code for projects. Use this skill when (1) initializing a new project with Claude Code configuration, (2) reviewing or improving existing CLAUDE.md files, (3) organizing project instructions using progressive disclosure patterns, (4) converting repeated instructions into permanent documentation, or (5) setting up agent_docs/ structures for larger codebases. Handles the WHAT/WHY/HOW framework, conciseness optimization, and file import patterns.
Super Ralph Wiggum - autonomous iteration loops with templates, PRD support, progress tracking, and browser testing. This skill should be used when running Claude Code in autonomous loops for test coverage improvement, PRD-based feature development, documentation generation, dataset creation, lint fixing, code cleanup, or framework migrations. Combines the plugin's in-session loop mechanism with specialized templates and best practices from Geoffrey Huntley, Ryan Carson, and AI Hero.
Use when coordinating multi-agent work with dependencies, parallel workstreams, or complex handoffs requiring milestone tracking
Creates visual concepts for album artwork and generates AI art prompts. Use during planning for concept discussion, or after all tracks are Final for actual artwork generation.
OpenSpec Spec-Driven Development Assistant - An AI-aided programming framework based on the OPSX workflow. Align requirements with AI before writing code, and manage changes using a Schema-driven artifact dependency system. Trigger Conditions: 1. User mentions "openspec", "opsx", or spec-driven development 2. User wants to start new feature development or refactoring 3. User needs to explore complex problems or clarify requirements 4. User complains about AI misunderstanding or frequent rework 5. User uses slash commands such as /opsx:new, /opsx:ff, /opsx:apply, etc. 6. During project initialization or preparation for major changes
AI Agent native API provider — no API keys, no signups, no subscriptions. Just pay with USDC per request via x402 to instantly access Twitter, Instagram, and more.
Design and refactor Agent Skills with concise, high-signal instructions and explicit trigger metadata. Use when creating a new skill, revising SKILL.md/README.md structure, or improving skill discoverability and portability.
Generate Claude Code permissions config from session history. Use when setting up autonomous mode, configuring .claude/settings.json, avoiding --dangerously-skip-permissions, or analyzing what permissions a project needs. Reads session logs to extract Bash commands and MCP tools actually used, then generates appropriate allow/deny rules.
Analyzes an MLflow session — a sequence of traces from a multi-turn chat conversation or interaction. Use when the user asks to debug a chat conversation, review session or chat history, find where a multi-turn chat went wrong, or analyze patterns across turns. Triggers on "analyze this session", "what happened in this conversation", "debug session", "review chat history", "where did this chat go wrong", "session traces", "analyze chat", "debug this chat".
Use this when you need to EVALUATE OR IMPROVE or OPTIMIZE an existing LLM agent's output quality - including improving tool selection accuracy, answer quality, reducing costs, or fixing issues where the agent gives wrong/incomplete responses. Evaluates agents systematically using MLflow evaluation with datasets, scorers, and tracing. Covers end-to-end evaluation workflow or individual components (tracing setup, dataset creation, scorer definition, evaluation execution).
Analyzes a single MLflow trace to answer a user query about it. Use when the user provides a trace ID and asks to debug, investigate, find issues, root-cause errors, understand behavior, or analyze quality. Triggers on "analyze this trace", "what went wrong with this trace", "debug trace", "investigate trace", "why did this trace fail", "root cause this trace".
File-based knowledge persistence patterns: when to store discoveries, when to recall past solutions, and how to organize project memory. Activate when starting tasks, encountering errors, making decisions, or when context may be lost between sessions.