Total 50,524 skills, AI & Machine Learning has 8481 skills
Showing 12 of 8481 skills
Install and use World2Agent (W2A) sensors to give AI agents structured, real-time perception of the real world
Download and analyze social videos using frames + transcript for AI agent understanding at 50× lower cost than multimodal APIs
Save session context, decisions, progress, and plans to the Claude Brain Logseq graph. Triggers: "save to brain", "save this", "remember this", "store this decision", "log this", "save progress", "before I quit", "wrap up". Don't fire for read operations (use brain-load) or status checks (use brain-status).
Detect and rewrite AI-like Chinese text with a practical workflow for scoring, humanization, academic AIGC reduction, and style conversion. Use when the user asks to 去AI味, 降AIGC, 去除AI痕迹, 论文降重, 知网检测, 维普检测, humanize chinese, detect AI text, or make Chinese text sound more natural.
Diagnose and fix common Gladia API issues. Use when the user encounters errors (401, 403, 429), unexpected behavior, poor transcription quality, billing confusion, audio format problems, WebSocket disconnections, polling failures, or asks about limits and rate limiting. SDK-first diagnostics — many issues are solved by migrating to the official SDK.
Enable the GitHub CLI (`gh`) in Claude Code cloud sessions and GitHub Copilot coding agent environments. Use this skill when: (1) setting up a project so cloud AI agents can use `gh` for PRs, issues, and releases, (2) configuring setup scripts or SessionStart hooks for `gh` installation, (3) adding `copilot-setup-steps.yml` for GitHub Copilot agents, (4) troubleshooting `gh` auth failures in cloud sessions, or (5) configuring `GH_TOKEN` for headless environments. Triggers on: "enable gh", "github integration", "Claude Code cloud setup", "copilot setup steps", "gh auth in cloud", "gh not working in cloud", "setup script", or any request involving GitHub CLI access from cloud-based AI coding agents.
Guides AI ops leadership—LLM SRE, model/prompt releases, eval/incidents, cost/capacity, vendors, and cross-functional cadence. Use for AI platform ops, LLM SLAs, incidents, rollout governance, unit economics, red-team/eval gates, and team rituals—not memory (ai-memory-developer), context code (ai-context-engineer), security programs (cybersecurity), token roadmaps (ai-token-improvement-plan-engineer), solution architecture (applied-ai-architect-commercial-enterprise), skills portfolio (ai-skill-manager), or vertical AI product eng management (engineering-manager-vertical-ai-products). Prompt/eval team management and golden-set release policy: engineering-manager-agent-prompts-evals. Safeguard inference platform: ml-infrastructure-engineer-safeguards. Safeguard model research: ml-research-engineer-safeguards.
Design, test, and optimize prompts for LLM interactions. Cover prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques. Triggers on "write prompt", "optimize prompt", "design system prompt", "few-shot examples", "chain of thought", "prompt evaluation", "LLM output formatting", "prompt testing", or "prompt patterns".
Build and operate predictive models for logistics networks—demand forecasting at SKU/location/lane granularity; inventory positioning and safety stock optimization interfaces; ETA and lead-time prediction; capacity and congestion signals; route and network flow forecasting at model-integration level; cold chain and perishables; promotion and seasonality; model monitoring, drift, and backtesting against operational KPIs (fill rate, OTIF, WMAPE/MAPE). Use for predictive logistics, demand forecasting logistics, ETA prediction, inventory positioning, safety stock optimization, OTIF forecast, lane demand, WMAPE, logistics ML, capacity forecasting logistics, or cold chain forecast—not pure OR/MIP without logistics domain (operations-research-algorithm-developer), supply chain strategy only (supply-chain-manager), WMS feature dev (wms-developer), fleet telematics ingestion (geospatial-telematics-developer), generic ML without logistics (data-scientist), or EDI document mapping (edi-engineer).
Guide for conducting thorough, multi-source research and producing comprehensive, well-sourced reports. Powered by AnyCap -- the capability runtime that equips AI agents with web search (including AI Grounded citations), web crawl, image generation, cloud storage, and one-click web publishing through a single CLI. Use when the user asks for deep research, competitive analysis, market research, technical deep dive, literature review, technology comparison, or any task requiring multi-source information gathering and synthesis. Also use when users say "investigate", "survey the landscape", "compare X vs Y", "state of the art", "write a report on", "look into", "find out about", "analyze the market", or any inquiry that needs more than a single search. Trigger on mentions of research, analysis, investigation, comparison, report, survey, or deep dive.
Lance une revue d'issue automatique avec des personas experts sélectionnés automatiquement, analyse la faisabilité, la complétude, les risques et l'architecture, puis publie un rapport structuré directement sur l'issue — le tout sans intervention de l'utilisateur.
Use when billing for AI model token usage — setting up @commet/ai-sdk tracked() middleware, configuring balance consumption model plans with AI model pricing, tracking input/output/cache tokens, cost calculation with margins, or building AI products that need usage-based billing.