Machine Learning Claude Skills
1 780 machine learning skills from 282 authors. They train and fine-tune models, build embeddings, run RAG and measure quality. Half of them fit into 2 252 tokens or less — that is what one costs your context window when the agent loads it. 424 ship runnable scripts rather than instructions alone. 10 of them cannot work without an MCP server, most often rube. We also found 397 copies of these same skills sitting in other people's repositories — counted once here, not 397 times.
1 780 unique 282 authors 5 updated this month 189 from vendors
このスキルを使用して、パフォーマンスベースラインを測定し、PR前後の回帰を検出し、スタック代替案を比較します。
LLM APIの使用量のコスト最適化パターン — タスクの複雑さによるモデルルーティング、予算追跡、リトライロジック、プロンプトキャッシング。
Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.
Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.
日本語翻訳：このファイルは pytorch-patterns 用の日本語翻訳が必要です
作为代理工程师，采用评估优先执行、分解和成本感知模型路由进行操作。
Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
创建和更新宣传文稿、一页简介、投资者备忘录、加速器申请、财务模型和融资材料。当用户需要面向投资者的文件、预测、资金用途表、里程碑计划或必须在多个融资资产中保持内部一致性的材料时使用。
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced For You algorithm. Use this skill whenever the user is building any system that picks "the top K items for a (user, context)" — social feeds, content CMSs, RAG rerankers, task prioritizers, notification triage, search reranking, ad ranking.
批量查看和切换子 agent 的模型配置，用于统一调整多 agent 的 provider/model 设置。
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。
Semantic Similarity Index for disease research literature using PubMedBERT embeddings
Smart LLM router — save 67% on inference costs. Routes every request to the cheapest capable model across 41 models from OpenAI, Anthropic, Google, DeepSeek, and xAI.
Design irresistible offer packages with real salary benchmarks, negotiation playbooks, and competitive counter-strategies. Co-designed with Siku (司库). Includes total compensation calculator, negotiation scripts, and BATNA analysis.
Legal contract analysis using CUAD dataset (41 risk categories). Supports NDA, SaaS, M&A, employment, payment/merchant, and finder/broker agreements. Identifies red flags, suggests redlines, compares to market standards.
Audit OpenClaw token usage, purge stale sessions, and optimize inference speed. Use when the user sends /optimize, /audit, asks to purge sessions, or wants a token/workspace audit.
生成 Agent 与 Cron 的模型配置状态报告，展示主模型、fallback 链和任务分配情况。
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.
模型自动降级与故障切换。当主模型请求失败、超时、达到速率限制或配额耗尽时，自动切换到备用模型，确保服务连续性。支持多供应商、多优先级的智能模型选择，提供健康监控、自动重试和错误恢复机制。
检查已配置模型供应商的连通性、延迟和可用性，用于快速诊断模型侧故障。
调度远程 Ollama GPU 资源执行批量 embedding 或推理任务，提升多机环境下的算力利用率。
TCM meridian inference engine — health scoring from 6-meridian measurements
蒸馏Aaron Ross思维模式的实用框架——预可售、销售分工模型、可预测收入
蒸馏查理·芒格《穷查理宝典》——多元思维模型、逆向思维、25种误判心理学、Lollapalooza效应、能力圈决策框架
蒸馏Dan Koe Human 3.0框架——四象限发展模型、三级意识进化、相位成长体系、超级人类90天计划
蒸馏 Charlie Munger（Berkshire Hathaway）思维模式的实用框架：多元思维模型、反向思考、lollapalooza效应
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
> Receive and verify Hugging Face webhooks. Use when setting up Hugging Face webhook handlers, debugging X-Webhook-Secret verification, or handling events on models, datasets, and Spaces — repo updates, new commits and tags (repo.content), config changes (repo.config), discussions, Pull Requests, and discussion comments.
> Receive and verify OpenAI webhooks. Use when setting up OpenAI webhook handlers for fine-tuning jobs, batch completions, or async events like fine_tuning.job.completed, batch.completed, or realtime.call.incoming.
CRE Due Diligence analysis suite — 7 specialist skills for multifamily property analysis including rent roll validation, expense benchmarking, market study, physical inspection, environmental review, title review, and tenant credit assessment.
Unified Kaggle skill. Use when the user explicitly mentions Kaggle, kaggle.com, a Kaggle URL, Kaggle competitions, Kaggle datasets/models/notebooks, Kaggle forums/discussions/writeups, Kaggle benchmarks, hackathons hosted on Kaggle, Kaggle badges, or Kaggle account setup. Do not use for generic ML, GPU/TPU, notebook, dataset, benchmark, or data-science tasks unless the user clearly ties them to Kaggle.
Use when the user wants to build a consistent-identity LoRA for an original character — defining the character, generating a face/body-consistent multi-angle dataset (via the gpt-image-gen skill for codex image generation), captioning it, doing base-specific homework, training on a chosen base (Pony / Z-Image / others) on a local GPU, and producing a usable LoRA. This skill ORCHESTRATES the end-to-end pipeline and gates every expensive/irreversible step; it delegates actual image generation to gpt-image-gen and never improvises training settings from memory.
Use when the user wants to test, compare, or choose local Ollama models for their machine. Checks Ollama/GPU state, recommends model sizes from available VRAM, preserves existing benchmark records, pulls only approved models, runs repeatable benchmarks, restores stopped services, and writes a markdown comparison report. NOT for hosted API model evaluation or subjective chat-quality judging without local benchmark commands.
Implement loss functions in PyTorch with proper tensor operations.
Analyzing earthquake data relative to tectonic plate boundaries using PB2002 dataset and GeoPandas.
PyTorch patterns for implementing preference optimization losses (DPO, SimPO, etc.) for LLM training.
Parse and analyze SEC 13-F filing TSV datasets to extract AUM, holdings count, and fund details by accession number.
Guide for implementing preference optimization methods (DPO, SimPO, IPO) in PyTorch. Use when implementing loss functions for RLHF-style training.
Implements the SimPO (Simple Preference Optimization) loss function from the paper "SimPO: Simple Preference Optimization with a Reference-Free Reward". Use this when implementing or understanding SimPO training objectives.
Setup and installation of TRL (Transformer Reinforcement Learning) library with compatible torch/transformers versions. Use when setting up preference optimization training environments.
How to query and filter travel itinerary datasets (restaurants, accommodations, attractions, distances) to build constraint-satisfying travel plans.
Techniques for loading and initial processing of GeoJSON and coordinate-based datasets using GeoPandas.
Manages NLP environments, handling library dependencies like PyTorch, Transformers, and custom local packages.
Implements the Simple Preference Optimization (SimPO) loss function in PyTorch, focusing on length-normalized log probabilities and target reward margins.
Provides foundational techniques for loading, projecting, and manipulating geospatial datasets using GeoPandas.