Total 56,499 skills, AI & Machine Learning has 9403 skills
Showing 12 of 9403 skills
When the user wants to optimize maintenance strategies, improve equipment reliability, reduce downtime, or implement predictive maintenance. Also use when the user mentions "preventive maintenance," "predictive maintenance," "TPM," "Total Productive Maintenance," "MTBF," "MTTR," "reliability analysis," "equipment maintenance," "condition monitoring," "CBM," "failure analysis," or "spare parts optimization." For quality improvements, see quality-management. For OEE, see lean-manufacturing.
Manage skills across 20+ AI platforms (Claude Code, Cursor, Copilot, Gemini, OpenClaw, Hermes, etc.). Use `list` as the unified entrypoint. Default behavior is listing skills only; only guide/recommend when the user explicitly asks what skill to use.
Générez des prompts optimisés pour chaque modèle de génération vidéo IA (Veo 3, Runway Gen-3, Kling 2.6, Pika), en exploitant leurs forces spécifiques. Use when: **Animer des frames de storyboard** - Transformer des images fixes en vidéo; **Choisir le bon modèle** - Sélectionner Veo, Runway, Kling ou Pika selon le besoin; **Optimiser la qualité de génération** - Prompts structurés pour meilleurs résultats; **Créer des transitions fluides** - Scene extension, first/last frame; **Utiliser le mo...
Protects LLM agent systems in real-time with a 5-tier filter (hash cache, rule engine, ML classifier, LLM judge, human approval) and an async learning engine. Synthesizes new rules from every detected attack, adding less than 50ms latency. Trigger on 'add security layer', 'prevent prompt injection', 'adaptive guard', 'runtime protection', or 'agent security'.
ABSOLUTE MUST to debug and inspect LLM/AI agent traces using PostHog's MCP tools. Use when the user pastes a trace URL (e.g. /llm-observability/traces/<id>), asks to debug a trace, figure out what went wrong, check if an agent used a tool correctly, verify context/files were surfaced, inspect subagent behavior, investigate LLM decisions, or analyze token usage and costs.
Guidance for creating, running, fixing, and promoting behavioral evaluations. Use when verifying agent decision logic, debugging failures, debugging prompt steering, or adding workspace regression tests.
Retrieve time-windowed RSS evidence from SQLite and let the agent produce final summaries using RAG over selected records and fields. Use when generating daily, weekly, monthly, or custom-range AI tech digests directly in agent responses instead of fixed template reports.
One AI integration. Manage Organizations, Users. Use when the user wants to interact with One AI data.
MUST be used whenever creating an AtlasTool (client-side tool) for an Atlas agent. Do NOT manually write AtlasTool definitions or wire them into useAtlasChat — this skill handles the TypeBox schema, execute function, and hook wiring. This includes tools that fetch data, render UI, call APIs, show charts, query local state, or perform any browser-side action. Triggers: AtlasTool, client tool, add tool, create tool, new tool, tool definition, agent tool.
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
MLA (Multi-Latent Attention) cost models, regime analysis, and kernel selection guide. Use when: (1) reasoning about which kernel approach to use for a given regime, (2) understanding cost model tradeoffs between FlashMLA, FlashAttention, and MLAvar6+, (3) analyzing roofline behavior across decode/speculative/prefill regimes, (4) setting optimization targets, (5) understanding MLA math and absorption trick.
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