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
Use UMAP-learn for nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
Conduct compensation benchmarking analysis to position salaries against market data. Use this skill when the user needs to assess pay competitiveness, build salary bands, or analyze pay equity — even if they say 'are we paying market rate', 'salary benchmarking', or 'compensation analysis'.
SME and family-business management for Taiwan's industrial structure (98% SME, 70%+ family-controlled listed). Integrates four pillars: resource-constrained decision-making, family governance (Tagiuri-Davis three circles), four-layer succession, and professionalization. Use for sub-200-employee strategy, capital-constrained prioritization, family-vs-professional-manager conflict, founder handover, family-constitution design, or SME financing limits. Triggers: 『二代接班』『家族企業』『中小企業』『家族憲章』『家族辦公室』『傳承規劃』『小公司策略』『資源有限』『家族跟專業經理人』『三圓模型』『隱形冠軍』. For 成大 EMBA SME strategy, 政大 EMBA family-business succession, 逢甲 EMBA SME management cases. Fills Asgard gaps in resource-constrained strategy and Chinese-family governance.
Apply Cognitive Load Theory to optimize instructional design by managing intrinsic, extraneous, and germane load within working memory limits. Use this skill when the user needs to diagnose why learners are overwhelmed, redesign training or documentation for better comprehension, evaluate UI/UX information architecture for cognitive burden, or when they ask 'why is this tutorial confusing', 'how to simplify complex instructions', or 'what causes information overload'.
Apply the SERVQUAL model (Parasuraman, Zeithaml, and Berry, 1988) to measure service quality gaps across five dimensions. Use this skill when the user needs to diagnose service quality shortfalls, benchmark customer expectations against perceptions, design service improvement programs, or when they ask 'where is our service failing', 'what do customers expect vs experience', or 'how do we measure service quality'.
Calculate and diagnose Overall Equipment Effectiveness (OEE) by decomposing into Availability, Performance, and Quality rates. Use this skill when the user needs to measure production line efficiency, identify equipment losses, benchmark manufacturing performance, or justify capital investment — even if they say 'why is our output low', 'machine utilization report', 'production efficiency', or 'how much capacity are we losing'.
Strategic talent-management toolkit integrating 9-box grid (performance × potential), succession planning, competency models, and talent review into one operational flow. Use for key-position-risk diagnosis, succession pipeline design, family-business second-generation grooming, annual talent review, competency/promotion systems, or post-M&A talent retention. Triggers: 『誰來接班』『關鍵職缺斷鏈』『高潛人才』『績效考核跟晉升脫節』『家族二代怎麼養』『留才方案』『9-box』『繼任規劃』『職能模型』『人才盤點』. For Taiwan EMBA strategic-HR/OB/family-business courses (政大／成大). Algorithmic HR tools: use Asgard `algo-hr-compensation`, `algo-hr-matching`, `algo-hr-turnover`.
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
Guides tenant isolation architecture in Qdrant for multi-tenant or multi-user applications. Use when someone asks 'how to isolate customer data', 'how to build multi-tenant search/RAG', 'how many collections should I create', 'how to partition tenants by payload', 'a customer's data legally has to stay in a certain country or region'. Also use when they describe a symptom: one customer's data is way bigger than the rest and slowing everyone down, or one tenant is hogging resources.
Diagnoses and improves Qdrant search relevance. Use when someone reports 'search results are bad', 'wrong results', 'low precision', 'low recall', 'irrelevant matches', 'missing expected results', or asks 'how to improve search quality?', 'which embedding model?', 'should I use hybrid search?', 'should I use reranking?', 'how to measure retrieval quality?', 'build a golden set', 'ground truth dataset', or 'how to score recall@k?'. Also use when search quality degrades after quantization, model change, or data growth.
Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', 'quality dropped after quantization', 'how to measure retrieval quality', 'build a golden set', 'ground truth dataset', or 'how to score recall@k'. Also use when search quality degrades without obvious changes.
Guides Qdrant search strategy selection. Use when someone asks 'should I use hybrid search?', 'how to rerank?', 'results are not relevant', 'I don't get needed results from my dataset but they're there', 'retrieval quality is not good enough', 'results too similar', 'need diversity', 'MMR', 'relevance feedback', 'recommendation API', 'discovery API', or 'missing keyword matches
Constructing prefetch queries for hybrid retrieval, including sparse/dense and multi-field setups, and choosing a sparse embedding model. Use when someone asks 'dense and sparse in one search?', 'how to combine multiple fields for retrieval?', 'payloads or sparse vectors for lexical?', 'which sparse embedding model to use?', or 'BM25 vs SPLADE?
Expanding the candidate pool via relevance feedback, as an alternative to reranking when a dense retriever is too weak. Use when someone asks about 'Qdrant's Relevance Feedback API', 'improving dense search relevance/recall', 'how to discover/get more relevant results from vector search', 'cheaper/better alternative to reranking', 'using a more heavy/big embedding model for dense search but can't afford it', 'finding more relevant documents beyond the initial search pool', or 'feedback loops'. Also trigger when the user has a search quality problem due to a dense retriever being weak and is considering reranking as a solution — this API may be a better fit
> JeecgBoot AI 编排流程（AIFlow）全生命周期管理——通过自然语言描述需求，自动创建、编辑、查询、删除、调试、发布 AI 编排流程。 只要用户意图涉及「AI编排」「AIFlow」就必须使用本技能，包括但不限于： 创建 AI 编排流程（"做一个AI流程"、"创建aiflow"、"新建编排"、"做一个知识库问答流程"、"创建一个大模型对话流程"）， 修改已有流程（"给流程加个节点"、"改一下LLM的提示词"、"修改流程"）， 查询流程（"查看流程列表"、"有哪些AI流程"）， 删除流程（"删除流程"、"移除XX流程"）， 调试运行流程（"调试流程"、"运行流程"、"测试流程"）， 发布管理（"发布流程"、"取消发布"）， 复制流程（"复制流程"、"克隆流程"）。 关键词触发：aiflow、ai-flow、AI编排、AI流程、编排流程、大模型流程、知识库流程、LLM流程。 注意：本技能仅处理 AI 编排流程（AIFlow），不处理 BPMN 工作流（使用 jeecg-bpmn）、 不处理简流（使用 jeecg-lowcode-miniflow）。
Cloudflare Workers AI for serverless GPU inference. Use for LLMs, text/image generation, embeddings, or encountering AI_ERROR, rate limits, token exceeded errors.
Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors.
Type-safe Hono APIs with routing, middleware, RPC. Use for request validation, Zod/Valibot validators, or encountering middleware type inference, validation hook, RPC errors.
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
>- Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides REST API (curl) examples.
>- Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Go SDK examples.
>- Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Java SDK examples.
>- Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides JavaScript SDK examples.
>- Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Ruby SDK examples.
>- Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.
Compare WOZCODE vs vanilla Claude Code on the user's codebase — real cost, turn, and time savings. TRIGGER on "compare woz", "how much does woz save", "benchmark woz", "woz vs claude", "show me savings", or /woz-benchmark.
Deprecated alias — use `/woz benchmark`.
WOZCODE utilities. Subcommands — login, logout, status, settings, update, share, review (deep multi-persona code review), benchmark (WOZCODE vs vanilla comparison). Invoke as `/woz <subcommand>`, e.g. `/woz login` or `/woz review`.
Genetic algorithms, simulated annealing, particle swarm optimization, gradient-based methods, topology optimization, shape optimization, size optimization, and benchmark problems for AEC computational design
Train and optimize AI agents using Microsoft's Agent Lightning framework with reinforcement learning. Use when setting up agent training, instrumenting agents with tracing, configuring LightningStore, implementing reward functions, or optimizing prompts with RL/APO algorithms.
Expert in shipping production-grade AI-powered features — LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, safety and guardrails, streaming, and cost optimization.
Knowledge graph memory backend powered by Cognee. Query, status check, dataset management, and backend switching between Brain and Cognee. Triggers on: 'cognee', 'knowledge graph', 'graph memory', 'switch memory', 'memory backend'.
Read the recent M0 operational thread for a project, newest first, to pick up work started in this or another tool. Answers 'where were we' and 'what is next' before asking the user to repeat context. Local SQLite, no embeddings, no network. Triggers on: 'm0 recall', 'where were we', 'what did we do last time', 'catch me up', 'resume context', 'read the thread', 'what is next'.
Search the web for real-time information using the Skywork web search API. Use this skill whenever the user needs up-to-date information from the internet — for example, researching a topic, looking up recent events, finding facts or statistics, gathering material for a document or presentation, or answering questions that require current data. Also trigger when the user says things like "search for" / "搜索" / "検索" / "검색", "look up" / "查询" / "調べる" / "조회하다", "find information about" / "查找关于……的信息" / "……に関する情報を探す" / "…에 대한 정보를 찾다", "what's the latest on" / "……最新进展" / "……の最新情報" / "…의 최신 소식", or any request that implies needing information beyond your training data.
Comprehensive evaluation patterns for AI agents including multi-turn conversation testing, LLM-as-judge frameworks, benchmark suites, regression detection, and systematic eval pipelines for measuring agent quality and safety.
Test LLM, RAG, MCP, and agentic systems end to end. Build golden datasets, run deterministic checks and LLM judges, score retrieval, probe prompt injection, verify tool use, and gate CI on thresholds. Orchestrates DeepEval, Ragas, promptfoo, and Langfuse.
Instrument LLM apps with Langfuse tracing, then use traces, scores, and datasets to test in production, run evaluations on real traffic, catch regressions, and close the loop from incident to golden dataset.
Grade LLM and agent traces with OpenAI Evals - build datasets, configure string/python/model graders, run eval suites, and gate agent behavior changes in CI.
Evaluate and red-team LLM applications with promptfoo, declarative YAML evals, assertions, model comparisons, and automated adversarial scans for prompt injection, jailbreaks, PII leaks, and unsafe outputs in CI.
Measure RAG pipeline quality with context precision/recall, faithfulness, answer relevancy, and groundedness using Ragas and DeepEval, with golden datasets and pass/fail thresholds.
Gate RAG pipelines in CI with versioned golden eval sets, per-metric thresholds, baseline drift detection, and a build that fails when retrieval or answer quality regresses.
Evaluate RAG pipelines with Ragas, measuring faithfulness, answer relevancy, context precision and recall, building golden datasets, and wiring threshold gates into CI for retrieval regressions.
> Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
> Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI 2.0 ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
Patterns for Bayesian inference in R using brms, including multilevel models, DAG validation, and marginal effects. Use when performing Bayesian analysis.
R performance best practices including profiling, benchmarking, vctrs, and optimization strategies. Use when optimizing R code.
Keeps ingestion repository and published GPT-RAG documentation aligned with shipped formats, configuration, indexing, deployment, audit, and operator behavior.
Prepares and validates gpt-rag-ingestion releases. Use for release branches, VERSION, changelog entries, compatible GPT-RAG pins, tags, images, and GitHub Release notes.