Total 50,891 skills, AI & Machine Learning has 8520 skills
Showing 12 of 8520 skills
Execute complex tasks with intelligent workflow management and cross-session persistence. Use when managing large projects, tracking progress across sessions, or orchestrating multi-phase work.
General Architecture Specification for CS-RAG Project, unifies global architecture cognition and architecture design constraints, provides entry points for layered inspection, impact analysis, interface contracts, dependency injection and pluggable governance.
This skill should be used when loading tasks from a project directory into the current Claude Code session. It reads task JSON files from session subdirectories, recreates them in the current session, and sets the active project marker.
Quick answers to questions without heavy note-taking overhead
Design effective MCP tools and Claude Code integrations using the consolidation principle. Fewer, better-designed tools dramatically improve agent success rates. Use when creating MCP servers, designing tool interfaces, optimizing tool sets, or when user mentions 'tool design', 'MCP', 'fewer tools', 'tool consolidation', 'tool architecture', or 'tool optimization'.
Add new behavior options without changing core roles.
Shows the Wasp plugin's available features, commands, and skills.
Build specialized openclaw agents with proper workspace structure, identity, and skills
Autonomous p5.js visualization agent. It implements, inspects, critiques design/UX, fixes, and launches the result.
This skill should be used when checking for naming conflicts between local skills (~/.claude/skills) and plugin-provided skills (~/.claude/plugins). Use to identify duplicate or similarly named skills that may cause inconsistent agent behavior.
Proactive token budget assessment and task chunking strategy. Use this skill when queries involve multiple large file uploads, requests for comprehensive multi-document analysis, complex multi-step workflows with heavy research (10+ tool calls), phrases like "complete analysis", "full audit", "thorough review", "deep dive", or tasks combining extensive research with large output artifacts. This skill helps assess token consumption risk early and recommend chunking strategies before beginning work.
Implement machine learning experiment tracking using MLflow or Weights & Biases. Configures environment and provides code for logging parameters, metrics, and artifacts. Use when asked to "setup experiment tracking" or "initialize MLflow". Trigger with relevant phrases based on skill purpose.