Total 56,898 skills, AI & Machine Learning has 9462 skills
Showing 12 of 9462 skills
Portable .agent/ folder with memory, skills, and protocols that works across Claude Code, Cursor, Windsurf, and other AI coding harnesses
Comprehensive guide to understanding and implementing AI agent systems using Claude Code architecture patterns
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.
Generate images using Codex's ChatGPT backend with zero production dependencies. Reuses existing local Codex authentication (~/.codex/auth.json) — no new credentials needed. Supports CLI (gti command), Node.js library, and Python SDK. Accepts text prompts with optional reference images (PNG/JPG/GIF/WebP). Includes dry-run mode and debug output. Triggers on: god-tibo-imagen, gti, image generation, codex image, chatgpt image, ai image, gpt image generation.
Manages custom Agent resources on Gemini Enterprise Agent Platform. Use when the user wants to programmatically create, configure, list, update, or delete stateful, server-managed Agent resources (including mounting files, skills, and tools) before executing conversations.
Generate llms.txt and llms-full.txt files for a website to improve AI discoverability. Use when the user asks to create llms.txt, generate llms.txt, fix llms.txt, make site AI-readable, or mentions llms.txt generation.
Guides commercial contract review and negotiation support for B2B agreements—MSAs, SaaS/order forms, vendor and customer contracts, DPAs, SLAs, limitation of liability, indemnity, IP, payment terms, and redline/issue logs with business impact notes. Use when reviewing or negotiating commercial terms, comparing vendor or customer paper, drafting negotiation positions, or triaging contract risk before sign-off—not for SOC/ISO GRC programs or vendor questionnaire ops (compliance-specialist), technical audit evidence (compliance-engineer), revenue recognition under ASC 606 (senior-revenue-accountant), or product requirements (business-analyst), strategy (business-consultant). Corporate/board: corporate-counsel. AI architecture for contract review: applied-ai-architect-commercial-enterprise. M&A economics mandate: transaction-principal. Drafting assistance only; human counsel must approve.
Guides privacy research engineering for safeguards—PII and sensitive-data detection research, redaction and de-identification evals, memorization and extraction risk studies, privacy benchmarks and labeled corpora, logging/retention minimization for safety pipelines, and research memos on privacy–utility trade-offs for guardrail systems. Use when measuring PII detector quality, designing privacy eval suites for moderation stacks, studying training-data leakage or prompt logging risk, or recommending privacy mitigations for safeguard models—not for SOC 2/GDPR evidence automation (compliance-engineer), legal DPIA or AI policy (ai-risk-governance), harm/toxicity classifier R&D (ml-research-engineer-safeguards), production inference gateways (ml-infrastructure-engineer-safeguards), or general non-privacy research (ai-researcher).
A CLI tool that calls the Wind Alice Agent (A2A protocol, SSE streaming) to execute specified Skills and obtain analysis results. It applies to scenarios where users explicitly request actions like "run a certain Skill with Alice", "generate a research question list for a company", "create a one-page investment memo", "verify a piece of financial information", etc., that involve Alice sub-Skills.
This skill should be used when the user asks to "quantize a model", "run PTQ", "post-training quantization", "NVFP4 quantization", "FP8 quantization", "INT8 quantization", "INT4 AWQ", "quantize LLM", "quantize MoE", "quantize VLM", or needs to produce a quantized HuggingFace or TensorRT-LLM checkpoint from a pretrained model using ModelOpt.
Visualize a specific transformer decoder layer from an AutoDeploy FX graph text dump as a hierarchical DOT/PNG diagram. Optionally annotate nodes with actual GPU kernel names and durations from an nsys trace. Use when the user wants to visualize, inspect, or debug a layer in an AutoDeploy model graph dump. Triggers on: "visualize layer", "show layer", "graph of layer", "layer visualization", "dump graph layer". Assumes graph dumps already exist in a directory (produced by AD_DUMP_GRAPHS_DIR).
Smart answering framework that automatically adapts to question types and delivers evidence-based, natural responses.