Total 56,878 skills, AI & Machine Learning has 9458 skills
Showing 12 of 9458 skills
Use when you have implemented an equivariant model and need to verify it correctly respects the intended symmetries. Invoke when user mentions testing model equivariance, debugging symmetry bugs, verifying implementation correctness, checking if model is actually equivariant, or diagnosing why equivariant model isn't working. Provides verification tests and debugging guidance.
Routes tasks to skills in skill-db and skill-library using semantic discovery. Triggers on specialized skill requirements, domain-specific tasks, or explicit skill requests. Uses skill-discovery, mcp-skillset, and skill-rag-router for semantic matching.
Use when making predictions or judgments under uncertainty and need to explicitly update beliefs with new evidence. Invoke when forecasting outcomes, evaluating probabilities, testing hypotheses, calibrating confidence, assessing risks with uncertain data, or avoiding overconfidence bias. Use when user mentions priors, likelihoods, Bayes theorem, probability updates, forecasting, calibration, or belief revision.
Use when writing instructions that guide Claude behavior - skills, CLAUDE.md files, agent prompts, system prompts. Covers token efficiency, compliance techniques, and discovery optimization.
Use when creating or updating CLAUDE.md files for projects or subdirectories - covers top-level vs domain-level organization, capturing architectural intent and contracts, and mandatory freshness dates
Persistent memory for Claude across conversations. Use when starting any task, before writing or editing code, before making decisions, when user mentions preferences or conventions, when user corrects your work, or when completing a task that overcame challenges. Ensures Claude never repeats mistakes and always applies learned patterns.
This skill should be used when analyzing video files. Claude cannot process video directly, so this skill extracts frames hierarchically - starting with a quick overview, then zooming into regions of interest with higher resolution and temporal density. Use when asked to watch, analyze, review, or understand video content.
Meta-prompting skill that creates well-structured, verifiable, low-hallucination prompts for any use case. Use when the user wants to create, refine, or improve a prompt — including system prompts, role prompts, task prompts, or any AI instruction set. Triggers on requests like "create a prompt for...", "help me write a prompt", "refine this prompt", "make a better prompt for...", or "generate a prompt that...".
Extract structured review themes from any input source: supports files (PDF/Word/Markdown/Tex), folders, images, natural language descriptions, web URLs, etc.; automatically identifies input types and extracts content; generates structured output of "Theme + Keywords + Core Questions" which can be directly used for systematic-literature-review and other literature review skills.
Use when designing multi-agent systems, implementing supervisor patterns, coordinating multiple agents, or asking about "multi-agent", "supervisor pattern", "swarm", "agent handoffs", "orchestration", "parallel agents"
Performs Technical Due Diligence on startups. Analyzes code (if available) or evaluates public signals (hiring, blogs) to assess technical risk and team maturity.
Community incident reporting for AI agents. Contribute to collective security by reporting threats.