Total 57,845 skills, AI & Machine Learning has 9619 skills
Showing 12 of 9619 skills
Define an entire Cargo workspace in code — connectors, models, plays, tools, agents, MCP servers, context, capacities, territories, segments, folders, files, workers, apps — and deploy it declaratively with `cargo-ai cdk` (init → types → plan → deploy), the way you'd manage cloud infra with Pulumi or the AWS CDK. Use when the user wants to manage Cargo resources as code: reproducibly, version-controlled, in git, from a template, or across environments. Routes to authoring/deploy/typing guides (Level 2), recipes (Level 2.5), and references. For one-off imperative operations (create one connector, read a model, run a workflow), use the matching capability skill instead.
Put the USER into a ~45s Japanese-anime football short as the hero — from their photo + their favorite team + the opponent. Anime-fies the user's face into a consistent character sheet, designs a dramatic 3-act match (come on → equalizer → winner) with the user scoring, writes three 15s cel-shaded scene prompts (CAPS names + Japanese dialogue), generates them on Seedance off the sheets, and stitches them. A Japanese-anime football short built on the Pika MCP, but the star is YOU. Triggers: "/anime-soccer", "put me in an anime soccer video", "make me the anime football hero", "anime match with my photo". Requires the Pika MCP.
Add AI navigation in Unity 6: bake a NavMesh with the AI Navigation package (NavMeshSurface), move agents with NavMeshAgent.SetDestination, and handle dynamic obstacles. Use when setting up pathfinding, making an enemy chase the player, baking navigation, or when the user mentions NavMesh, NavMeshAgent, NavMeshSurface, NavMeshObstacle, or Unity pathfinding.
Selects bounded, graph-informed source slices with Trailmark and delegates focused code analysis or patch-proposal work to a smaller subagent. Use when offloading function-, class-, caller-, callee-, call-path-, entrypoint-, or line-focused code tasks to constrained or locally hosted models without exposing the full repository.
Drafts copy-paste-ready /goal commands for goal mode in Claude Code and Codex. Use when the user asks to create, write, rewrite, improve, compress, clean up, or prepare a goal prompt, goal condition, /goal command, goal-mode objective, or copy-ready long-running task objective.
Turn a completed experiment iteration into an honest, evidence-backed analysis — a markdown report and a portable data dump. Pulls run data via the tpc CLI, scores each task, clusters friction by root cause (with a transcript example per claim), compares arms, and closes on agent-readiness gaps. The natural companion to setup-experiment: setup → run → analyze. Trigger when users say: "analyze my experiment", "write the report", "experiment report", "analyze the results", "summarize the runs", "what happened in this iteration", "friction report", or "report gen".
Best Practice Advisor for AGENTS.md / CLAUDE.md. It is used when users ask about the format, structure, and best practices of agents markdown, AGENTS.md, CLAUDE.md, Claude Code memory, and AI coding agent instruction files; it also supports reviewing, diagnosing, rewriting, optimizing, or creating agent instruction files such as AGENTS.md, CLAUDE.md, CLAUDE.local.md, and .claude/rules from scratch. It is not suitable for general README writing unless the goal is to provide project context for AI coding agents.
Designs, builds, debugs, and documents OpenClaw workflows, skills, and AI assistant configurations. Use when the user mentions "OpenClaw," "personal AI assistant," "local AI," "ClawdHub," "openclaw skills," "chat platform AI," or wants to set up AI assistants across WhatsApp, Telegram, Discord, or Slack.
Knowledge Base RAG implements the complete Retrieval-Augmented Generation pipeline: document ingestion, intelligent chunking, embedding generation, vector store indexing, semantic retrieval, and grounded response generation.
Enhanced skill template with progressive disclosure, bundled resources, and quality rubrics. Use when creating new skills that need structured tiers, reference files, validation rubrics, or advanced bundling patterns beyond the basic template.
Hamilton Helmer's 7 Powers framework applied to a business. Spawns a team of specialist agents — Power Cartographer, Lifecycle Timer, Counter-Positioning Scout, and Moat Devil's Advocate — who each apply a distinct lens from Helmer's taxonomy. The lead synthesizes into a Power Inventory (what you have), Power Pipeline (what's achievable given your stage), and the honest Helmer Verdict. Use when the user says "helmer this", "apply 7 powers", "what power does this company have", "is this a moat", "diagnose my competitive position", or proposes a business and wants strategic analysis. Works standalone or after /thiel (which confirms you need a monopoly) or /munger (which asks if the economics are durable).
Meta-skill for understanding and customizing Mindfold Trellis — the all-in-one AI workflow system for 11 AI coding platforms (Claude Code, Cursor, OpenCode, iFlow, Codex, Kilo, Kiro, Gemini CLI, Antigravity, Qoder, CodeBuddy). Documents the original Trellis system design including architecture, commands, hooks, multi-agent pipelines, monorepo support, and task lifecycle hooks. Use when understanding Trellis architecture, customizing workflows, adding commands or agents, troubleshooting issues, or adapting Trellis to specific projects. Modifications should be recorded in a project-local trellis-local skill, not here.