Total 56,939 skills, AI & Machine Learning has 9468 skills
Showing 12 of 9468 skills
Development & Design: Automatically inventory ECC resources, build a complete implementation plan through planner + architect, output plan.md for user confirmation before proceeding to implementation.
(Industry standard: Sequential Agent / Agent as a Tool) Primary Use Case: Delegating a well-defined task to a worker agent, verifying its execution, and repeating if necessary. Inner/outer agent delegation pattern. Use when: work needs to be delegated from a strategic controller (Outer Loop) to a tactical executor (Inner Loop) via strategy packets, with verification and correction loops.
Expert knowledge for AI deep research — methodology, source evaluation, search optimization, cross-referencing, synthesis, and citation formats
This skill should be used for multi-session autonomous agent work requiring progress checkpointing, failure recovery, and task dependency management. Triggers on '/harness' command, or when a task involves many subtasks needing progress persistence, sleep/resume cycles across context windows, recovery from mid-task failures with partial state, or distributed work across multiple agent sessions. Synthesized from Anthropic and OpenAI engineering practices for long-running agents.
Tiered memory system for cognitive continuity across agent sessions. Manages hot cache (session context loaded at boot) and deep storage (loaded on demand). Use when: (1) starting a session and loading context, (2) deciding what to remember vs forget, (3) promoting/demoting knowledge between tiers, (4) user says 'remember this' or asks about project history.
Why build on Ethereum specifically. The AI agent angle — ERC-8004, x402, composability, permissionless deployment. Use when a user asks "should I build on Ethereum?", "why blockchain?", or when an agent needs to recommend a chain for a project.
Ingests and prepares codebase documentation, builds internal knowledge graphs, and creates optimized context representations for downstream analysis tasks.
LLM fine-tuning expert for LoRA, QLoRA, dataset preparation, and training optimization
Proactively discover, install, and create Claude skills for specialized tasks. AUTOMATICALLY activates when encountering niche domains, unfamiliar APIs, specialized knowledge areas, or when user expresses wanting to learn/know something new. Searches the community registry at claude-plugins.dev and auto-installs matching skills. Creates new skills when no match exists. Use when user says "I want to know...", "help me with [specialized domain]", or when you recognize a task requires specialized expertise you don't have.
[WHAT] Universal content intake system for URLs (GitHub repos, YouTube videos, articles, PDFs) and skill packages (skills.sh, skill:// protocol) [HOW] Phase 1: Clone repos/fetch transcripts/scrape content/resolve skills to ~/lev/workshop/intake/. Phase 2-3: Load workshop/intake.md for full analysis [WHEN] Use when user provides a URL to analyze, says "intake/download", wants to evaluate external content, or references a skill package [WHY] Systematically evaluates external content and skill packages for adoption/adaptation with tier classification and ADR creation Triggers: "intake", "download", "analyze this url", "check out this repo", "review this video", "evaluate content", "install skill", "skill://"
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for...
Bootstrap, install, and operate an external task-management CLI as the source of truth for agent execution tracking (instead of built-in todos). Provides the abstraction layer between spec-management intent (implementation plans and tasks) and concrete CLI commands. MUST be invoked when any implementation-tier artifact (SPEC, STORY, BUG) comes up for implementation — create a tracked plan before writing code. Optional but recommended for complex SPIKEs. For coordination-tier artifacts (EPIC, VISION, JOURNEY), spec-management must decompose into implementable children first — this skill tracks the children, not the container. Also use for standalone tasks that require backend portability, persistent progress across agent runtimes, or external supervision. Use this skill whenever the user asks to track tasks, create an implementation plan, check what to work on next, see task status, manage dependencies between work items, or close/abandon tasks — even if they don't mention "execution tracking" explicitly.