Total 50,540 skills, AI & Machine Learning has 8483 skills
Showing 12 of 8483 skills
添加和配置第三方 API 中转站供应商到 OpenClaw。当用户需要添加新的 API 供应商、配置中转站、设置自定义模型端点时使用此技能。支持 Anthropic 兼容和 OpenAI 兼容的 API 格式。
Open-world and environment specialist - Masters UE5 World Partition, Landscape, procedural foliage, HLOD, and large-scale level streaming for seamless open-world experiences
Expert in physical and human geography, climate systems, cartography, and spatial analysis — builds geographically coherent worlds where terrain, climate, resources, and settlement patterns make scientific sense
- **Role**: You are a rigorous prompt engineer specializing exclusively in authentic human representation. Your domain is defeating the systemic stereotypes embedded in foundational image and video...
Builds production AI/ML systems — model training, fine-tuning, MLOps pipelines, model serving, evaluation frameworks, RAG optimization, and agent orchestration at scale. Use when the user asks to build, train, or deploy ML models, set up MLOps pipelines, optimize RAG systems, create inference endpoints, or design production AI agents.
Investigate LLM analytics clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.
Discover and use shared team skills stored in PostHog. Use when the user asks to list, browse, load, or manage "shared skills", "team skills", or references the "skills store" / "skill store".
Design prompts, schemas, validation, and recovery logic for reliable machine-readable model outputs. Use when generating JSON, typed objects, extraction results, tool arguments, or any output another system must parse safely.
Context window coach. Proactive guidance for token-efficient Claude Code projects, multi-agent systems, and skill architecture.
KERNEL-based prompt engineering — transforms vague requests into structured, high-performance prompts optimized for first-try success.
Use when the user wants a full feature-development chain: clarify a rough feature idea into a prompt, review it with the user, then hand it to grill-with-docs, to-prd, to-issues, and tdd.
Audit whether an ML or AI paper's experimental baselines are necessary, fair, current, and reviewer-proof. Use this skill whenever the user is planning experiments, comparing methods, choosing baselines, worried about missing SOTA or unfair comparisons, preparing a reviewer-proof experiment section, or converting a literature review into must-have, should-have, optional, and not-comparable baselines.