Total 30,812 skills, AI & Machine Learning has 4972 skills
Showing 12 of 4972 skills
AI-powered X/Twitter research via xAI Grok. Returns AI SUMMARIES with analysis, not raw tweets. Use for "what's trending", "social sentiment", "summarize X discussion about", "analyze X conversation about", "research topic on X". For RAW tweet data, use x-user-timeline, x-tweet-search, x-tweet-fetch instead. Requires XAI_API_KEY.
Execute Gemini CLI for AI-powered code analysis and generation. Use when you need to leverage Google's Gemini models for complex reasoning tasks.
This skill should be used when the user asks to "create custom DSPy module", "design a DSPy module", "extend dspy.Module", "build reusable DSPy component", mentions "custom module patterns", "module serialization", "stateful modules", "module testing", or needs to design production-quality custom DSPy modules with proper architecture, state management, and testing.
This skill should be used when the user asks to "optimize a DSPy program", "use MIPROv2", "tune instructions and demos", "get best DSPy performance", "run Bayesian optimization", mentions "state-of-the-art DSPy optimizer", "joint instruction tuning", or needs maximum performance from a DSPy program with substantial training data (200+ examples).
Scaffold development rules for AI coding agents. Auto-invoked when user asks about setting up rules, coding conventions, or configuring their AI agent environment.
Generates new content drafts based on reference content analysis
Optimize AGENTS.md and rules for token efficiency. Auto-invoked when user asks about improving agent instructions, compressing AGENTS.md, or making rules more effective.
Context compression and summarization methodology. Techniques for reducing token usage while preserving decision-critical information.
Creates multi-agent orchestration workflows for complex tasks. Handles enterprise workflows, operational procedures, and custom orchestration patterns. Use when user needs to automate multi-phase processes with agent coordination.
Chain-of-Verification (CoVe) prompting system. Converts lazy prompts into rigorous 4-stage verified output. Use for any code generation, debugging, or implementation task. Automatically invoked by wavybaby for medium/high complexity tasks. Reduces hallucinations and catches subtle bugs.
【WHEN】Activate when the user's command explicitly includes instructions such as "codex collaboration", "codex coordination", "work with codex", etc.; 【WHEN】Activate when the user requests to use the codex MCP tool for collaborative development scenarios such as code analysis, prototype generation, review, etc. 【WHEN NOT】Not applicable for standalone code implementation tasks, pure document writing, or regular development processes that do not involve codex.
Meta-skill for analyzing PRs, issues, and user interactions to improve Cursor rules and skills automatically