Total 50,341 skills, AI & Machine Learning has 8461 skills
Showing 12 of 8461 skills
Build local-first executive assistant workflows with OpenClaw for data intake, operational memory, and communications triage
Expert in OpenClaw Studio - web dashboard for managing OpenClaw Gateway, agents, chat, approvals, and jobs
Official Lark/Feishu plugin for OpenClaw that enables AI agents to interact with Lark workspaces including messages, docs, bases, calendars, and tasks
Provision a zero-config, no-signup Upstash Redis database for an AI agent via a single POST to `https://upstash.com/start-redis`. Use when an agent needs scratch Redis for short-term memory, conversation history, sub-agent work queues, or ranked recall and the user has not provided credentials. The database lives 3 days unless the user claims it.
better-chatbot project conventions and standards. Use for contributing code, following three-tier tool system (MCP/Workflow/Default), or encountering server action validators, repository patterns, component design errors.
Vercel AI SDK v5 for backend AI (text generation, structured output, tools, agents). Multi-provider. Use for server-side AI or encountering AI_APICallError, AI_NoObjectGeneratedError, streaming failures.
Automate 7-phase feature development with specialized agents (code-explorer, code-architect, code-reviewer). Use for multi-file features, architectural decisions, or encountering ambiguous requirements, integration patterns, design approach errors.
Secure browser SSO and OAuth2 authentication proxy that lets AI agents access authenticated APIs without exposing credentials
MCP server for computer use & browser automation - screenshot, OCR, click, type, find_text, Chrome/Electron CDP, template matching on macOS, Windows & Android
Inspect Claude Code session logs, tool calls, token usage, subagents, and context window using claude-devtools visual UI
Iterate on RAG systems with structured evals instead of eyeballing. This skill should be used when the user is tuning a RAG pipeline — changing retrieval prompts, swapping models, adjusting chunking, or debugging poor answers — and wants a cheap, ranked set of experiments with cost tracking and structured feedback on the stack. Also use when the user asks "how do I know if my RAG is working?", "this RAG eval is burning money", or "what should I try next on retrieval?".
Use when the response requires complex reasoning, creativity, nuance, evaluation, decision-making, subjectivity, or communication-sensitive work. If the task is simple and the user has not manually invoked this skill, do not use it.