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 this skill whenever a question should be answered from documents stored in a LlamaCloud Index v2 knowledge base — retrieving passages, locating indexed files, or searching indexed content through the LlamaParse Platform REST API with curl. Teaches agentic retrieval — navigating an index like a file system instead of one-shot RAG.
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt ...
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific dom...
Use when improving documentation structure, accuracy, and RAG readiness.
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debug...
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, buildin...
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or ...
Automate Mistral AI tasks via Rube MCP (Composio): completions, embeddings, fine-tuning, and model management. Always search tools first for current schemas.
Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.
Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...
RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
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.
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/
Automate Google BigQuery tasks via Rube MCP (Composio): run SQL queries, explore datasets and metadata, execute MBQL queries via Metabase integration. Always search tools first for current schemas.
Automate Mistral AI operations -- manage files and libraries, upload documents for fine-tuning, batch processing, and OCR, track fine-tuning jobs, and build RAG pipelines via the Composio MCP integration.
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems.
Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
Pre-ingestion verification for epistemic quality in RAG systems with 9-point verification and Two-Round HITL workflow
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
Use when running performance benchmarks, establishing baselines, or validating regressions with sequential runs. Enforces 60s minimum runs (30s only for binary search) and no parallel benchmarks.
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tok...
环境准备。安装依赖、下载模型、验证环境。触发词：安装、环境准备、初始化
LLM and AI application security testing skill for prompt injection, jailbreaking, and AI system vulnerabilities. This skill should be used when testing AI/ML applications for security issues, performing prompt injection attacks, testing LLM guardrails, analyzing AI system architectures for vulnerabilities, or assessing RAG pipeline security. Triggers on requests to test LLM security, perform prompt injection, jailbreak AI systems, test AI guardrails, or audit AI application security.
This skill should be used when the user asks to "fine-tune on books", "create SFT dataset", "train style model", "extract ePub text", or mentions style transfer, LoRA training, book segmentation, or author voice replication.
Execute Hugging Face Hub operations using the `hf` CLI. Use when the user needs to download models/datasets/spaces, upload files to Hub repositories, create repos, manage local cache, or run comput...
| issue, the version-bump and changelog prep PR, or the post-release development-version bump PR. Reads release metadata from `<project-config>/release-trains.md` and `<project-config>/release-management-config.md`. Every output is a draft confirmed by the Release Manager before filing; the agent never marks a PR ready, never merges, and never closes any artefact.
Compose Mapbox MCP tools to produce grounded, cited location-aware responses from live data instead of training data
> Marketing benchmark triage skill. Use whenever metrics are shared with questions like "is this good?", "is this normal?", or "should I worry?" — ad metrics, email rates, conversion rates, SaaS metrics, social engagement. Compares the user's numbers against published, sourced benchmarks using panel-matching rules (same platform, model, market, and denominator), abnormal and which are benchmark anxiety.
> Use the moment a request is underspecified and you are about to act on an ASSUMED goal. Enumerate the candidate goals the request could mean, project two or more plausible goals lead to materially different actions or artifacts, ask exactly ONE targeted clarifying question; otherwise proceed on the most-likely goal and state the assumption in one line. Clarification is an evidence-producing action, not a delay — but one question, never an interrogation. Distinct from gsd-spec-phase (GSD-phase-bound, emits a heavy SPEC.md) and intent-router (fetch strategy, not goal disambiguation). Backed by agent goal-state inference (arxiv 2606.16813v1). Triggers on acting under an assumed goal when the request admits more than one materially different reading.
> Use at write time to vector/embedding memory — Grove content-addressed insertion, chroma/pgvector upserts, memory-consolidation promoting session traces to MEMORY.md, or embedding externally-ingested content. Scores each candidate record against a fixed panel of sentinel queries and quarantines any record that would become the nearest neighbor of too many unrelated queries — a hub — whether from adversarial poisoning or accidental over-generality. This is the memory-record-side sibling of skill-injection-guardian (file-side) and the write-side complement of memory-use-warrant (read-side). Quarantine, never silently drop; a human reviews. Backed by the admission-time hubness gate (arxiv 2606.19692v1). Triggers on inserting into vector memory, consolidating memory, and embedding stranger content.
Complex systems and emergent behavior — chaos theory, fractals, neural networks, cellular automata, and computability. Analyzes dynamical systems for chaos via Lyapunov exponents, classifies bifurcations, measures fractal dimension, and models emergent phenomena. Use when analyzing chaotic systems, computing Lyapunov exponents, classifying bifurcations, measuring fractal dimension, modeling neural networks, or studying emergent behavior from simple rules.
Sketched Isotropic Gaussian Regularization primitive. Scalar loss matching the embedding distribution to a standard-normal target via Cramér-Wold slicing and the Epps-Pulley empirical characteristic function test. Port of rbalestr-lab/lejepa (MIT). Default-off in v1.49.571.
Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck. Also triggers on "who are the competitors to X", "benchmark X against peers", "build a market map", or any request to systematically evaluate competitive dynamics across an industry.
Use whenever the user wants to find, shortlist, vet, or enrich US AI/ML/data consulting firms (consultancies) — AI/ML development, MLOps, generative AI / LLM apps (RAG, chatbots, agents), computer vision, NLP, recommendation systems, data engineering, BI/analytics. Triggers on "find an AI/ML consulting firm to build our recommendation engine", "shortlist three RAG/LLM consultancies for an enterprise chatbot", "compare three AI/ML consulting firms with strong ratings", or "pull contact info for these 8 AI consultancy domains", even when described indirectly (we want to use AI for X, deploy ML to production). Drives the ServiceGraph API (api.servicegraph.co) — a 100k+ US firm catalog filterable by industry, services, location, size, ratings. Defer to find-software-developer for general app/backend work where AI is just a feature. Skip in-house ML/data hires, LLM/AI-tool comparisons (ChatGPT vs Claude), "how do I fine-tune X" DIY questions, AI courses for individuals, non-US firms, individual freelancers.
Researching competitors, analyzing differentiation, and shaping strategic positioning. Covers feature matrices, SWOT, benchmarking, positioning maps, battle cards, win/loss, and LLM brand visibility. Research only — no code. Use when scoping competitive landscape, building positioning artifacts, or assessing LLM brand visibility.
Designing and evaluating AI/ML systems across prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, and cost optimization. Use when designing AI/ML pipelines, RAG architectures, prompt strategies, evaluation harnesses, or LLM cost models.
Designing search engines and vector DBs for full-text, vector, and hybrid retrieval. Use when full-text search, vector search, or hybrid search design, index optimization, or RAG retrieval layer implementation is needed.
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.
Measure first, optimize second. Data-driven performance improvements with before/after benchmarks and production validation.
Autonomously research, implement, train and ship ML code using the Hugging Face ecosystem. Port of huggingface/ml-intern as a Claude Code skill. Triggers when the user asks to implement, train, fine-tune, or reproduce an ML model / paper / dataset workflow (e.g. "implement DeepSeek-V3 at 100M", "fine-tune Qwen on dataset X", "reproduce paper Y"). Clarifies ambiguous tasks before starting, runs under an explicit experiment budget, explores multiple viable solution paths in parallel via implementation subagents, and diagnoses + retries failed runs. HF-native: pulls datasets/models/papers from the Hub, pushes trained checkpoints + run logs back to the Hub. Emits Telegram + Slack milestone alerts via scripts/notify.sh.
Local LLM operations with Ollama on Apple Silicon, including setup, model pulls, chat launchers, benchmarks, and diagnostics.