Total 32,819 skills, AI & Machine Learning has 5302 skills
Showing 12 of 5302 skills
Self-evolving AI agent system with 26 tools, three-layer memory, MCP plugins, and 24/7 self-repair in pure Python.
A team of 10 AI agents that manage your Obsidian vault for knowledge, nutrition, and mental wellness using Claude Code
Enable multiple Claude Code instances to discover each other and exchange messages in real-time via a local broker daemon and MCP server.
Documentation reference for writing Python code using the browser-use open-source library. Use this skill whenever the user needs help with Agent, Browser, or Tools configuration, is writing code that imports from browser_use, asks about @sandbox deployment, supported LLM models, Actor API, custom tools, lifecycle hooks, MCP server setup, or monitoring/observability with Laminar or OpenLIT. Also trigger for questions about browser-use installation, prompting strategies, or sensitive data handling. Do NOT use this for Cloud API/SDK usage or pricing — use the cloud skill instead. Do NOT use this for directly automating a browser via CLI commands — use the browser-use skill instead.
Delegate coding tasks to Google Jules AI agent for asynchronous execution. Use when user says: 'have Jules fix', 'delegate to Jules', 'send to Jules', 'ask Jules to', 'check Jules sessions', 'pull Jules results', 'jules add tests', 'jules add docs', 'jules review pr'. Handles: bug fixes, documentation, features, tests, refactoring, code reviews. Works with GitHub repos, creates PRs.
Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.
Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships.
Skill Map Viewer. Scans all installed skills and renders a visual overview — you can check the name, version, description, and category at a glance. This tool is triggered when the user says 'skills', '技能', '技能地图', 'skill map', '我有哪些技能', '看看技能', '列出技能', 'list skills'. It also activates when the user asks about available or installed skills.
Convert raster images (photos, illustrations, AI-generated art) into high-quality SVG recreations. Breaks the image into isolated features, builds each as a standalone SVG layer, then composites them. Use when the user wants to recreate an image as SVG, create vector versions of artwork, or extract specific elements from images as scalable graphics.
Paper Workflow: Read papers and create reading cards in one go. Accepts one or more arXiv links, paper URLs, PDFs, or paper titles. For each paper, it runs ljg-paper (generates org-format analysis) followed by ljg-card -l (generates long-form reading card PNG). Trigger this workflow when the user says '论文流', 'paper flow', '读论文并做卡片', '论文卡片', or provides multiple papers and requires both analysis and reading cards.
Investment Analysis: Generate an in-depth investment analysis report. We do not conduct traditional investment analysis—the core judgment is whether the project is an "Order-Creating Machine". Activate this when the user says "investment report", "investment analysis", "analyze this project", "write an investment report", "investment report", "invest analysis", or provides entrepreneur conversation records for investment evaluation. Also activate when the user pastes or references meeting notes, pitch decks, or founder interviews and requests analysis.
Deep concept anatomist that deconstructs any concept through 8 exploration dimensions (history, dialectics, phenomenology, linguistics, formalization, existentialism, aesthetics, meta-philosophy) and compresses insights into an epiphany. Use when user asks to explain, dissect, or deeply understand a concept, term, or idea. Triggers on '解剖概念', '概念解剖', 'explain concept', 'learn concept', '/ljg-learn'. Produces org-mode output.