Total 50,615 skills, AI & Machine Learning has 8484 skills
Showing 12 of 8484 skills
Advanced AI agent benchmark scenarios that push Vercel's cutting-edge platform features — Workflow DevKit, AI Gateway, MCP, Chat SDK, Queues, Flags, Sandbox, and multi-agent orchestration. Designed to stress-test skill injection for complex, multi-system builds.
Post-session retrospective: audits efficiency, proposes skill/memory/CLAUDE.md updates, and generates coaching feedback
Core patterns for AI coding agents based on analysis of Claude Code, Codex, Cline, Aider, OpenCode. Triggers when: Building an AI coding agent or assistant, implementing tool-calling loops, managing context windows for LLMs, setting up agent memory or skill systems, or designing multi-provider LLM abstraction. Capabilities: Core agent loop with while(true) and tool execution, context management with pruning and compression and repo maps, tool safety with sandboxing and approval flows and doom loop detection, multi-provider abstraction with unified API for different LLMs, memory systems with project rules and auto-memory and skill loading, session persistence with SQLite vs JSONL patterns.
Use when you need Teams-first multi-agent orchestration in Claude Code. Triggers on: omc, autopilot, ralph, ulw, ccg, team. 29+ specialized agents, smart model routing (Haiku→Opus), persistent execution loops, skill layers, real-time HUD.
Standardize and validate SKILL.md files against the Agent Skills specification (agentskills.io). Use when creating new skills, auditing existing skills for spec compliance, converting legacy skill formats to standard structure, or improving descriptions for reliable triggering. Triggers on: "validate skill", "create SKILL.md", "standardize skill format", "check skill spec", "skill frontmatter", "improve skill description", "add evals to skill".
Ouroboros specification-first AI development — the complete system. Socratic interviewing crystallizes vague ideas into immutable specs (Ambiguity ≤ 0.2) before any code is written. Nine Minds agents (socratic-interviewer, ontologist, seed-architect, evaluator, contrarian, hacker, simplifier, researcher, architect) execute the Double Diamond. Ralph mode loops with state persistence until verification passes — the boulder never stops. Use when user says "ralph", "ooo", "ooo interview", "ooo seed", "ooo run", "ooo evaluate", "ooo evolve", "ooo unstuck", "ooo status", "ooo ralph", "stop prompting", "start specifying", "specification first", "socratic interview", "don't stop", "must complete", "keep going", or "the boulder never stops".
Multi-agent swarm orchestration where AI agents spawn, coordinate, and self-organize into collaborative teams. Use when running parallel AI agent tasks, orchestrating multi-agent workflows across Claude Code / Codex / Cursor / custom agents, isolating agent workspaces via git worktrees, tracking task dependencies across agents, or running autonomous experiments. Triggers on: clawteam, agent swarm, spawn agents, multi-agent team, agent orchestration, parallel agents, agent coordination, swarm intelligence, agent spawn, clawteam spawn, agent worktree, agentic team, ml agent experiments, autonomous agents, agent team.
Interact with the YouThumb.ai API to generate AI-powered YouTube thumbnails. Upload assets, create persons, create projects, launch generation, and retrieve results. Use when the user says "YouThumb API", "generate thumbnail via API", "upload asset to YouThumb", "create YouThumb project", "thumbnail generation", "YouThumb person", or when automating YouTube thumbnail creation programmatically.
Interactively onboard a project to OpenSpec by running a structured interview and generating a complete QRSPI-configured openspec/config.yaml. Use this skill whenever a user mentions "openspec config", "config.yaml for openspec", "set up openspec", "onboard to openspec", "generate openspec config", "QRSPI config", or asks how to configure OpenSpec for their project — even if they just say "help me set up openspec" or "I want to use openspec". Always prefer this skill over ad-hoc config generation.
Transform code, issues, or context into a detailed prompt/context for another LLM to fix or implement. Use when preparing comprehensive context for external LLM assistance, bug fixes, improvements, or feature implementations. Provides detailed context without implementation suggestions, letting the receiving LLM decide how to implement solutions. Focuses on "what" (problem, requirements, current state) not "how" (implementation approach).
Find prompt and model quality issues using real conversation data, with specific optimization recommendations. Can implement prompt fixes and model switches directly in your codebase.
Build AI agents with x402 payments on SKALE. Covers facilitator setup, payment middleware, and agent client. Use for monetized AI services, agent-to-agent payments.