Total 50,614 skills, AI & Machine Learning has 8484 skills
Showing 12 of 8484 skills
Official skill for generating high-quality images from text prompts using ZhiPu GLM-Image API. Excellent at scientific illustrations, high-quality portraits, social media graphics, and commercial posters. Supports multiple aspect ratios, HD quality, and watermark control. Use this skill when the user wants to generate images, create AI art, text-to-image, or convert text descriptions into visual content.
Predexon Agent — README Unified prediction market data API for Polymarket, Kalshi, Dflow, Binance, and cross-platform matching. Price: 0.001 USDC per call — flat rate for all endpoints. This agent
Perform real-time web searches with Google/Serper results.
How to create and structure skills for this repository. Covers the type system (guideline vs pattern), frontmatter conventions, and behavioral instructions. Follow when creating or updating any skill.
Guide pour la création de serveurs MCP (Model Context Protocol) de qualité permettant aux LLM d'interagir avec des services externes via des outils bien conçus. À utiliser pour construire des serveurs MCP intégrant des API ou services externes, en Python (FastMCP) ou Node/TypeScript (MCP SDK).
Query the official CrewAI documentation for answers. Use when the user has a CrewAI question that isn't fully covered by the getting-started, design-agent, design-task skills — e.g., specific API details, configuration options, advanced features, troubleshooting errors, enterprise features, tool references, or anything where the latest docs are the best source of truth.
Persistent memory and context management for AI agents using OpenContext. Keep context across sessions/repos/dates, store conclusions, and provide document search workflows.
Autonomously optimize an existing AI skill by running it repeatedly against binary evals, mutating one instruction at a time, and keeping only changes that improve pass rate. Based on Karpathy-style autoresearch, but applied to SKILL.md iteration instead of ML training. Use when optimizing a skill, benchmarking prompt quality, building evals for a skill, or running self-improvement loops on reusable agent instructions. Triggers on: skill-autoresearch, optimize this skill, improve this skill, benchmark this skill, eval my skill, run autoresearch on this skill, self-improve skill.
Cross-platform landscape scan before planning or implementation. Researches context, workarounds, existing solutions, and structural gaps, then writes reusable survey artifacts for OMC, OMX, OHMG, and general agent workflows.
Integrated AI agent orchestration skill that combines plannotator, ralphmode, team or bmad execution, agent-browser verification, and agentation feedback loops, while maintaining a project-local `.jeo` ledger for planning, development, and QA. Use when the user wants an end-to-end multi-agent workflow with plan approval, implementation, UI review, cleanup, and durable task history. Triggers on: jeo, annotate, ui-review, multi-agent orchestration.
Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".
Dialogue between Wang Yangming × Laozi × Alan Watts. A multi-role discussion from the perspectives of Heart-Mind Philosophy and Taoism, with Claude as the judge. Triggers: /dada-chatroom-heart, /心学聊天室, "心学聊天室" Heart-mind philosophy chatroom. Wang Yangming × Laozi × Alan Watts debate, Claude as judge. Trigger: /dada-chatroom-heart, /心学聊天室, "心学聊天室"