Total 50,380 skills, AI & Machine Learning has 8465 skills
Showing 12 of 8465 skills
Deep expertise in Hermes Agent architecture, implementation patterns, and extension development
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
Build and run durable background coding agents with workflow orchestration, isolated sandboxes, and GitHub integration on Vercel.
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).
Load project context from the Claude Brain Logseq graph into the current session. Triggers: "load brain", "load <project>", "resume <project>", "continue work on <project>", "what do we know about <topic>". Don't fire for write operations (use brain-save), generic questions about Logseq itself, or "open <file>" / "switch to <branch>" requests that mean opening files or switching git branches rather than loading project memory.
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).