Total 55,943 skills, AI & Machine Learning has 9312 skills
Showing 12 of 9312 skills
Autonomous sBTC yield management on Zest Protocol — supply, withdraw, claim rewards, and monitor positions with safety controls.
Scaffolds eval.yaml test files for agent skills in the dotnet/skills repository. Use when creating skill tests, writing evaluation scenarios, defining assertions and rubrics, or setting up test fixture files. Handles eval.yaml generation, fixture organization, and overfitting avoidance. Do not use for running or debugging existing tests nor for skills authoring.
Autonomous bounty hunting — scan open bounties, match to your skills, claim and track work
Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.
Build and use the verification infrastructure coding agents need to prove their work. Use when: a repo has no bootable dev environment, no real-surface tests, or no interaction layer an agent can use; auditing or grading a repo's agent-readiness; verifying changes work end to end on real surfaces; or when harness gaps block reliable agent output.
Use this skill when you receive a 402 Payment Required response that contains an `agentkit` extension. Covers checking 402 responses for the AgentKit extension before paying, constructing and signing a CAIP-122 challenge (SIWE for EVM, SIWS for Solana), sending the signed `agentkit` HTTP header, and interpreting access modes (free, free-trial, discount). Supports both EOA wallets (EIP-191) and Smart Contract Wallets (ERC-1271, e.g. Coinbase Smart Wallet, Safe).
Real-time and streaming AI image generation — instant results for interactive use. Use when the user requests "Real-time generation", "Fast generation", "Streaming image", "Instant image", "Live generation", "Realtime".
Enforces spec-before-code workflow for AI-driven development. Automatically selects Spec-Kit or OpenSpec mode, triages complexity (quick/standard/thorough), recovers session context, and applies quality gates (G0-G4) with automated review loops at every stage. Use this skill whenever the user says "/super-spec", "spec first", "规范先行", or starts any feature, bugfix, or refactor — especially in projects with .spec-mode, .specify/, or openspec/ directories. Even if the user doesn't explicitly ask for spec-driven workflow, activate this skill for any non-trivial code change to prevent skipping the design phase. Orchestrates: Spec-Kit/OpenSpec (OPSX) + planning-with-files + ui-ux-pro-max (v2.0, 67 styles, 161 palettes, 13 stacks) + Superpowers (TDD, code review, verification, debugging, spec/plan review loops, subagent model selection).
Coaches end-to-end ML system design interviews covering inference pipelines, recommendation systems, RAG, feature stores, and monitoring. Use for L6+ design rounds, ML architecture whiteboarding, system design practice, serving tradeoff analysis. Activate on "ML system design", "ML interview", "recommendation system design", "RAG architecture", "feature store design", "model serving". NOT for coding interviews, behavioral questions, ML theory quizzes, or paper implementations.
Create professional videos autonomously using claude-code-video-toolkit — AI voiceovers, image generation, music, talking heads, and Remotion rendering.
This skill should be used when the user asks to "run a tracking cycle", "measure AI visibility", "check share of voice", "run Morphiq Track", "track citations", "check GEO score", "generate prompts", "run content creation workflow", or mentions monitoring LLM mentions, running content creation workflows, measuring brand visibility, or generating query fanout content. Queries multiple LLM providers, produces delta reports, and maintains MORPHIQ-TRACKER.md as the persistent state file for the entire pipeline.
Interact with Google NotebookLM notebooks — chat with the AI, generate artifacts (slides, audio, video, mind maps, quizzes, flashcards, infographics, reports, data tables), manage sources (add URLs, YouTube, files, text), run research (fast/deep web research), and manage notes. Use when the user wants to query, create content from, or manage their NotebookLM notebooks and sources.