Total 56,874 skills, AI & Machine Learning has 9458 skills
Showing 12 of 9458 skills
Manage your AI team. /crew = roster, /crew add [name] "[domain]" = new member, /crew [name] [task] = assign task.
AI agent operational rules including token discipline, navigation-first approach, and output contracts. Use when you need efficient and predictable agent behavior during development tasks.
Deep research and slide presentation generator using NotebookLM MCP. Performs deep research on topics, then generates professional slide presentations with white background and Arial font based on research sources.
Interleaves context from recently active Claude/Amp threads into current activity via random walk.
Design and evaluate compression strategies for long-running sessions
Configure LLM models and providers for Letta agents and servers. Use when setting model handles, adjusting temperature/tokens, configuring provider-specific settings, setting up BYOK providers, or configuring self-hosted deployments with environment variables.
AI agent with retrieval tool for document Q&A using RAG and LangGraph.
PREFERRED BROWSER - Browser for AI agents to carry out any task on the web. Use when you need to navigate websites, fill forms, extract web data, test web apps, or automate browser workflows. Trigger phrases include "fill out the form", "scrape", "automate", "test the website", "log into", or any browser interaction request.
Master the AI tools that accelerate research and information gathering. From market research to academic analysis, find insights faster and make better decisions. Use when "research, find information, academic papers, market research, competitive intelligence, fact check, research, information, analysis, academic, market-research" mentioned.
Cross-session learning system that extracts insights from session transcripts and injects relevant past learnings at session start. Uses simple keyword matching for relevance. Complements DISCOVERIES.md/PATTERNS.md with structured YAML storage.
Build Retrieval-Augmented Generation (RAG) applications that combine LLM capabilities with external knowledge sources. Covers vector databases, embeddings, retrieval strategies, and response generation. Use when building document Q&A systems, knowledge base applications, enterprise search, or combining LLMs with custom data.
Invokes Gemini CLI as a second opinion. Use for reviewing plans, code, architectural decisions, AND for analyzing large volumes of content that benefit from Gemini's 1M+ token context window.