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
Master SurrealDB 2.3.x with Python for multi-model database operations including CRUD, graph relationships, vector search, and real-time queries. Use when working with SurrealDB databases, implementing graph traversal, semantic search with embeddings, or building RAG applications.
Universal dataset import for FiftyOne supporting all media types (images, videos, point clouds, 3D scenes), all label formats (COCO, YOLO, VOC, CVAT, KITTI, etc.), and multimodal grouped datasets. Use when users want to import any dataset regardless of format, automatically detect folder structure, handle autonomous driving data with multiple cameras and LiDAR, or create grouped datasets from multimodal data. Requires FiftyOne MCP server.
Create a FiftyOne dataset from a directory of media files (images, videos, point clouds), optionally import labels in common formats (COCO, YOLO, VOC), run model inference, and store predictions. Use when users want to load local files into FiftyOne, apply ML models for detection, classification, or segmentation, or build end-to-end inference pipelines.
Visualize datasets in 2D using embeddings with UMAP or t-SNE dimensionality reduction. Use when users want to explore dataset structure, find clusters in images, identify outliers, color samples by class or metadata, or understand data distribution. Requires FiftyOne MCP server with @voxel51/brain plugin installed.
Find duplicate or near-duplicate images in FiftyOne datasets using brain similarity computation. Use when users want to deduplicate datasets, find similar images, cluster visually similar content, or remove redundant samples. Requires FiftyOne MCP server with @voxel51/brain plugin installed.
Synthèse co-fabriquée par un conseil de 3 LLMs (Claude, Gemini, Codex). Ce skill devrait être utilisé quand l'utilisateur demande une synthèse robuste, traçable et vérifiée. Il orchestre trois modèles avec des rôles experts distincts (Extracteur, Critique, Architecte) pour produire une synthèse fidèle au texte source, avec contrôle des glissements sémantiques et trail d'audit complet.
| (1) Working with ANY external library (React, Next.js, Supabase, etc.) (2) User asks about library APIs, patterns, or best practices (3) Implementing features that rely on third-party packages (4) Debugging library-specific issues (5) Need current documentation beyond training data cutoff (6) AND MOST IMPORTANTLY, when you are installing dependencies, libraries, or frameworks you should ALWAYS check the docs to see what the latest versions are. Do not rely on outdated knowledge. Always prefer this over guessing library APIs or using outdated knowledge.
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows, and LLM integration. Master prompt engineering, function calling, streaming responses, and cost optimization for 2025+ AI development.
| Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
智谱AI的视觉语言模型，用于图像分析、内容识别和视觉问答
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
Automated compliance checking against CIS, PCI-DSS, HIPAA, and SOC 2 benchmarks
Use when the user asks to design RAG pipelines, optimize retrieval strategies, choose embedding models, implement vector search, or build knowledge retrieval systems.
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.
Generate output schemas (dataset_schema.json, output_schema.json, key_value_store_schema.json) for an Apify Actor by analyzing its source code. Use when creating or updating Actor output schemas.
UE Agent Benchmark 评测框架。定义通用评分体系、评测流程和质量层级，支持多场景 Benchmark。触发：用户提及 Benchmark/评测/基准测试/跑分 等关键词时激活。
> Research and compare how competing products implement a similar feature at the UX and interaction level. Provides structured comparison tables and strategic differentiation recommendations.
Use when defining or implementing Go interfaces, designing abstractions, creating mockable boundaries for testing, or composing types through embedding. Also use when deciding whether to accept an interface or return a concrete type, or using type assertions or type switches, even if the user doesn't explicitly mention interfaces. Does not cover generics-based polymorphism (see go-generics).
Use when optimizing Go code, investigating slow performance, or writing performance-critical sections. Also use when a user mentions slow Go code, string concatenation in loops, or asks about benchmarking, even if the user doesn't explicitly mention performance patterns. Does not cover concurrent performance patterns (see go-concurrency).
Finding and accessing AI/LLM model brand icons from lobe-icons library. Use when users need icon URLs, want to download brand logos for AI models/providers/applications (Claude, GPT, Gemini, etc.), or request icons in SVG/PNG/WEBP formats.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update 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.
Create an llms.txt file from scratch based on repository structure following the llms.txt specification at https://llmstxt.org/
Add instrumentation, build golden datasets, write eval-based tests, run them, root-cause failures, and iterate — Ensure your Python LLM application works correctly. Make sure to use this skill whenever a user is developing, testing, QA-ing, evaluating, or benchmarking a Python project that calls an LLM. Use for making sure an LLM application works correctly, catching regressions after prompt changes, fixing unexpected behavior, or validating output quality before shipping.
Update the llms.txt file in the root folder to reflect changes in documentation or specifications following the llms.txt specification at https://llmstxt.org/
>- Multi-model LLM Council with live dashboard. Query multiple AI models simultaneously, see responses side-by-side in a swarm-style dashboard, synthesize consensus, and run anonymous model-to-model voting. Use when the user asks to start the LLM council, compare models, query multiple models, convene council, ask all models, do model comparison, run multi-model queries, or launch the council dashboard. Supports Claude Sonnet 4.5, Claude Opus 4.5, GPT-4o, GPT-5.1, Gemini 2.5 Flash, and Gemini 2.5 Pro via AI Gateway.
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
| Build this skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. it is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
| Automatic activation for ALL Google Cloud Agent Development Kit (ADK) and Agent Starter Pack operations - multi-agent systems, containerized deployment, RAG agents, and production orchestration.
| This skill enables Claude to validate the ethical implications and fairness of AI/ML models and datasets. It is triggered when the user requests an ethics review, fairness assessment, or bias detection for an AI system. The skill uses the ai-ethics-validator plugin to analyze models, datasets, and code for potential biases and ethical concerns. It provides reports and recommendations for mitigating identified issues, ensuring responsible AI development and deployment. Use this skill when the user mentions "ethics validation", "fairness assessment", "bias detection", "responsible AI", or related terms in the context of AI/ML.
| This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions "anomaly detection," "outlier analysis," "unusual data," or requests insights into data irregularities.
| Manage Apify datasets, key-value stores, and request queues programmatically. Use when reading/writing datasets, exporting data, managing Actor storage, or orchestrating multi-Actor pipelines. "export apify data", "apify pipeline", "apify request queue".
| Batch Inference Pipeline - Auto-activating skill for ML Deployment. Part of the ML Deployment skill category.
| Benchmark Suite Creator - Auto-activating skill for Performance Testing. Part of the Performance Testing skill category.
| Build and evaluate classification models for supervised learning tasks with labeled data. Use when requesting "build a classifier", "create classification model", or "train classifier". Trigger with relevant phrases based on skill purpose.
| Execute this skill allows AI assistant to construct and configure neural network architectures using the neural-network-builder plugin. it should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
| Build different types of Claude-powered applications — chatbots, RAG systems, Use when working with architecture-variants patterns. agents, content pipelines, and code generation tools. Trigger with "claude architecture", "anthropic rag", "build with claude", "claude agent pattern", "anthropic app design".
| Redirect to claude-model-inference for Messages API streaming, vision, and structured output patterns. Use when looking for the primary Anthropic workflow. Trigger with "anthropic workflow", "claude main workflow".
| Redirect to claude-embeddings-search for tool use (function calling) and agentic loop patterns with Claude. Use when looking for the secondary Anthropic workflow. Trigger with "anthropic tools", "claude function calling".
| Implement tool use (function calling) with Claude to let it execute actions, Use when working with embeddings-search patterns. query databases, call APIs, and interact with external systems. Trigger with "anthropic tool use", "claude function calling", "claude tools", "anthropic structured output with tools".
| Stream Claude responses, use system prompts, handle multi-turn conversations, Use when working with model-inference patterns. and process structured output with the Messages API. Trigger with "anthropic streaming", "claude messages api", "claude inference", "stream claude response".
| This skill enables Claude to construct and evaluate classification models using provided datasets or specifications. It leverages the classification-model-builder plugin to automate model creation, optimization, and reporting. Use this skill when the user requests to "build a classifier", "create a classification model", "train a classification model", or needs help with supervised learning tasks involving labeled data. The skill ensures best practices are followed, including data validation, error handling, and performance metric reporting.
| This skill enables Claude to execute clustering algorithms on datasets. It is used when the user requests to perform clustering, identify groups within data, or analyze data structure. The skill supports algorithms like K-means, DBSCAN, and hierarchical clustering. Claude should use this skill when the user explicitly asks to "run clustering," "perform a cluster analysis," or "group data points" and provides a dataset or a way to access one. The skill also handles data validation, error handling, performance metrics, and artifact saving.
| Use when fine-tuning review quality, training CodeRabbit with team preferences, adding code guidelines, or reducing false positives. Trigger with phrases like "coderabbit tune reviews", "coderabbit learnings", "coderabbit guidelines", "reduce coderabbit noise", "coderabbit false positives".
| Build a complete RAG pipeline with Cohere Chat, Embed, and Rerank. Use when implementing retrieval-augmented generation, building grounded Q&A systems, or combining search with LLM generation. Trigger with phrases like "cohere RAG", "cohere retrieval", "cohere grounded generation", "cohere search and answer".
| Migrate from OpenAI/Anthropic/other LLM providers to Cohere, or vice versa. Use when switching LLM providers, migrating embeddings between models, or re-platforming existing AI integrations to Cohere API v2. Trigger with phrases like "migrate to cohere", "switch from openai to cohere", "cohere migration", "replace openai with cohere", "cohere replatform".
| Implement Cohere reference architecture with layered project layout for RAG and agents. Use when designing new Cohere integrations, reviewing project structure, or establishing architecture standards for Cohere API v2 applications. Trigger with phrases like "cohere architecture", "cohere project structure", "cohere layout", "organize cohere app", "cohere design pattern".
| Implement Cohere streaming event handling, SSE patterns, and connector webhooks. Use when building streaming UIs, handling chat/tool events, or registering Cohere connectors for RAG. Trigger with phrases like "cohere streaming", "cohere events", "cohere SSE", "cohere connectors", "cohere webhook".
| Confusion Matrix Generator - Auto-activating skill for ML Training. Part of the ML Training skill category.