Total 58,125 skills, AI & Machine Learning has 9662 skills
Showing 12 of 9662 skills
Google Model Armor: Filter user-generated content for safety.
This skill should be used when the user says "interview me about", "help me clarify", "stress-test my idea", "let's explore this concept", "challenge my assumptions about", "grill me on", "drill into my plan", or needs structured questioning to refine and articulate their thinking.
Deterministic default-avatar generator per user.
Query papers using RAG (PaperQA2 or LEANN). Use when user needs synthesized answers from papers, asks "what does paper X say about Y", or needs cited responses.
Manage RVF (Ruflo Vector Format) files for portable agent memory and cross-platform transfer
Route tasks through hooks_route, partition by Agent Booster availability, and report Tier 1 bypass utilization with $0 cost
Invoke the @empjs/skill CLI tool via natural language to manage AI Agent skills. Use this skill when users need to: 1. Install/add skill packages (install/add) 2. List installed skills (list/ls) 3. Delete/uninstall skills (remove/rm/uninstall) 4. View supported AI Agent platforms (agents/list-agents) 5. Manage skills using the eskill command.
Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.
Use only then explicitly asked.
This skill should be used when processing meeting transcripts to auto-detect meeting type (leadgen, partnership, coaching, internal) and extract type-specific structured analysis. Triggers on "process meeting", "analyze meeting", "meeting summary", or after syncing new Fathom/Granola transcripts.
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
Build a short music video from a song theme — N keyframes, animate each, generate matching music.