Total 50,906 skills, AI & Machine Learning has 8525 skills
Showing 12 of 8525 skills
Comprehensive knowledge of Microsoft Agent Framework for building production AI agents and workflows. Auto-activates for agent building, workflow design, AutoGen migration, and enterprise AI tasks.
Guide for implementing Google Gemini API image generation - create high-quality images from text prompts using gemini-2.5-flash-image model. Use when generating images, creating visual content, or implementing text-to-image features. Supports text-to-image, image editing, multi-image composition, and iterative refinement.
This skill should be used when users request help optimizing, improving, or refining their prompts or instructions for AI models. Use this skill when users provide vague, unclear, or poorly structured prompts and need assistance transforming them into clear, effective, and well-structured instructions that AI models can better understand and execute. This skill applies comprehensive prompt engineering best practices to enhance prompt quality, clarity, and effectiveness.
Execute Groq secondary workflow: Core Workflow B. Use when implementing secondary use case, or complementing primary workflow. Trigger with phrases like "groq secondary workflow", "secondary task with groq".
Explore a codebase with parallel Haiku agents. Modes - --fast (1 agent), default (3), --deep (5). Use when user says "learn [repo]", "explore codebase", "study this repo".
Use when improving agent prompts, frontmatter, and tool restrictions.
Decompose research ideas into atomic, self-contained concepts with bidirectional math-code mapping. For each concept, extract the math formula from papers and find code implementations. Use for complex system papers requiring formal grounding.
通过 MiniMax MCP 进行图像理解，适用于 OpenClaw 平台。如果你是 Claude Code 用户，请忽略此技能。
Setup Spanora AI observability in any project (JavaScript/TypeScript or Python). Use when user asks to "add spanora", "setup spanora", "integrate spanora", "add AI observability", "monitor LLM calls with spanora", "track AI costs", or mentions spanora in the context of adding observability to their project. Detects the language and installed AI SDKs (Vercel AI, Anthropic, OpenAI, LangChain) and configures the optimal integration pattern.
C++ Reinforcement Learning best practices using libtorch (PyTorch C++ frontend) and modern C++17/20. Use when: - Implementing RL algorithms in C++ for performance-critical applications - Building production RL systems with libtorch - Creating replay buffers and experience storage - Optimizing RL training with GPU acceleration - Deploying RL models with ONNX Runtime
Creates and registers templates for agents, skills, workflows, hooks, and code patterns. Handles post-creation catalog updates, consuming skill integration, and README registration. Use when creating new template types or standardizing patterns.
Optimize token usage when delegating to Gemini CLI. Covers token caching, batch queries, model selection (Flash vs Pro), and cost tracking. Use when planning bulk Gemini operations.