Total 57,028 skills, AI & Machine Learning has 9482 skills
Showing 12 of 9482 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.
Use when combining information from multiple Glean sources or when needing to synthesize results across documents, meetings, code, and people searches. Triggers on complex queries that span multiple data types, when results seem contradictory, when building comprehensive answers from partial information, or when the user asks for a complete picture of something that requires multiple queries.
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.
Package a agent skill into a complete GitHub repository ready for distribution via skills.sh. Generates README, LICENSE, plugin.json, marketplace.json, .gitignore, and the proper directory structure. Optionally initializes a git repo and creates a GitHub repository. This skill should be used when publishing a skill, packaging a skill for distribution, preparing a skill repo, or when the user says 'publish skill', 'package skill', 'release skill', '发布技能', '打包 skill'.
Guide for Claude Code skills installation, usage, and development. Use when working with Claude Code specific features, paths, and conventions.
Searches and retrieves MLflow documentation from the official docs site. Use when the user asks about MLflow features, APIs, integrations (LangGraph, LangChain, OpenAI, etc.), tracing, tracking, or requests to look up MLflow documentation. Triggers on "how do I use MLflow with X", "find MLflow docs for Y", "MLflow API for Z".
Determine the escalation path (Researcher vs. Human) and format the appropriate handoff. Used when the fix-engine has exhausted retry attempts.
Domain-agnostic strategic decision analysis and wargaming. Auto-classifies scenario complexity: simple decisions get structured analysis (pre-mortem, ACH, decision trees); complex or adversarial scenarios get full multi-turn interactive wargames with AI-controlled actors, Monte Carlo outcome exploration, and structured adjudication. Generates visual dashboards and saves markdown decision journals. Use for business strategy, crisis management, competitive analysis, geopolitical scenarios, personal decisions, or any consequential choice under uncertainty. NOT for simple pros/cons lists, non-strategic decisions, or academic debate.
Conduct deep research on any topic through structured investigation design. Use when the user needs comprehensive, multi-source analysis -- not a quick lookup. Triggers: deep research, comprehensive analysis, research report, compare X vs Y, analyze trends, investigate, or any request requiring synthesis across multiple perspectives. Do NOT use for simple questions answerable with 1-2 searches or for debugging.
Use when experiments complete to judge what claims the results support, what they don't, and what evidence is still missing. Codex MCP evaluates results against intended claims and routes to next action (pivot, supplement, or confirm). Use after experiments finish — before writing the paper or running ablations.
Autonomous LLM training optimization with GPU support. Runs 5-minute training experiments, measures val_bpb, keeps improvements or reverts — repeat forever. Use this skill when the user asks to "train a model autonomously", "optimize LLM training", "run ML experiments", "autoresearch with GPU", "optimize val_bpb", "autonomous ML training", "LLM pretraining loop", "setup ML autoresearch", "GPU training experiments", "pretrain from scratch", "speed up training", "lower my loss", "GPU optimization", "CUDA training", or mentions "train.py", "prepare.py", "bits per byte", "val_bpb", "NVIDIA GPU training", "RTX training", "H100 training", "autonomous model training", "consumer GPU training", "low VRAM training". Always use this skill when the user wants to autonomously optimize any ML training metric.
Retrieve a GitHub issue using the `gh` CLI, analyze it, and spawn a PM + developer team to address it. Accepts an issue URL, issue number, or `owner/repo#number`.