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
LangChain LLM application framework with chains, agents, RAG, and memory for building AI-powered applications
AI session compression techniques for managing multi-turn conversations efficiently through summarization, embedding-based retrieval, and intelligent context management.
> Detect performance anti-patterns and apply optimization techniques in Go. Covers allocations, string handling, slice/map preallocation, sync.Pool, benchmarking, and profiling with pprof. Use when checking performance, finding slow code, reducing allocations, profiling, or reviewing hot paths. "benchmark this", "profile", "optimize Go code". Do NOT use for concurrency correctness (use go-concurrency-review) or general code style (use go-coding-standards).
| Use when a user runs, debugs, verifies, or ships a DL experiment on a GPU they OWN or RENT (AutoDL, RunPod, vast.ai, Lambda, Paperspace, 恒源云/矩池云/Featurize/揽睿星舟, bare SSH, Slurm, K8s; spot 抢占/preemption, 断点续训/resumable, 防 SSH 断线/tmux 守护, 多实例 ablation, 关机/销毁/stop-vs-terminate billing, checkpoint 磁盘满, CUDA OOM/显存不足, loss NaN/spike/不收敛, overfit 单 batch, FSDP/DeepSpeed/torchrun, 多卡 hang, 训练太慢/GPU util 低, dataloader/数据增广 bug; 消融结果异常/ablation looks wrong, 复现/reproducibility, 数据泄漏/leakage/test-set tuning, mAP=0/全零指标, 输出恒定/model-ignores-input, train-good/val-collapse, 对比不公平/unfair baseline, 单 seed/no error bars, loss 太好/too-good-to-be-true, 跨文档对账/cross-doc drift; 交付产物/deliverable, 唯一真源/single source of truth, best ckpt 拉回, 结果可视化/论文图脚本, manifest/provenance, 一键复现/repro, EVIDENCE.json. NOT for multi-cloud price-shopping + auto spot-recovery (SkyPilot), BYOC dev environments (dstack), or zero-ops serverless inference (Modal).
Use this skill when reviewing, designing, or improving a workshop, lesson, training session, facilitation plan, talk, or educational explanation. Use it when the user provides a topic, concept, or explanation and wants better framing questions, curiosity hooks, opening prompts, discussion questions, learner reflection prompts, or ways to make participants think before being taught.
Verifies claims in backlog items, skill documentation, or plugin content against primary sources using web lookups. Spawns parallel verification agents that must use WebFetch/WebSearch/gh — training data recall is explicitly rejected as evidence. Produces VERIFIED/REFUTED/INCONCLUSIVE verdicts with citations. Use when items are marked UNVERIFIED or when verifying tool API claims, CLI flags, or documented software behavior.
When setting up local LLM inference without cloud APIs. When running GGUF models locally. When needing OpenAI-compatible API from a local model. When building offline/air-gapped AI tools. When troubleshooting local LLM server connections.
Audit skill lifecycle by tracing call chains, detecting circular dependencies, finding instruction contradictions, identifying duplicated datasets, analyzing bidirectional coherence, discovering scriptable sequences, and learning patterns. Use when checking skill coherence, validating skill workflow, finding semantic gaps in plugin structure, or auditing plugin before marketplace submission. Generates audit reports to .plugin-creator/audits/ with findings by dimension.
> Plan verification and validation campaigns for simulation codes using manufactured solutions, canonical benchmark problems, grid/time refinement, uncertainty propagation, and pass/fail acceptance criteria. Use when an agent needs to prove a solver, model, or result is trustworthy rather than only plausible.
| R performance profiling, benchmarking, and optimization strategies. Use this skill when code is running slowly, comparing alternative implementations, deciding between dplyr/data.table/base R, or implementing parallel processing. Covers profvis and bench usage, performance workflow, parallel processing with in_parallel(), data backend selection, modern purrr patterns (list_rbind, walk), and common performance anti-patterns to avoid.
Implements the Syncfusion .NET MAUI Range Selector (SfRangeSelector) component. Use when working with range selection with embedded charts, numeric range input, RangeStart/RangeEnd, track customization, or thumb styling in .NET MAUI. Covers tooltip configuration, label formatting, ticks/dividers, selection modes, events, and embedding SfCartesianChart inside a range control.
Use when an award run needs external evidence — literature, datasets, benchmarks, domain constants, prior-art checks, or citation verification — with combined built-in WebSearch and search MCP discovery, source reconciliation, document reading, citation confirmation, token budgets, and reproducibility rules.
| Before declaring any task complete, actually verify the outcome. Run the code. Test the fix. Check the output. Claude's training optimizes for plausible-looking output, not verified-correct output. This skill forces the verification step that doesn't come naturally. No victory laps without proof.
Use when migrating from SwiftData to SQLiteData — decision guide, pattern equivalents, code examples, CloudKit sharing (SwiftData can't), performance benchmarks, gradual migration strategy
Use when wrapping UIKit views/controllers in SwiftUI, embedding SwiftUI in UIKit, or debugging UIKit-SwiftUI interop issues. Covers UIViewRepresentable, UIViewControllerRepresentable, UIHostingController, UIHostingConfiguration, coordinators, lifecycle, state binding, memory management.
CoreML diagnostics - model load failures, slow inference, memory issues, compression accuracy loss, compute unit problems, conversion errors.
Use when deploying custom ML models on-device, converting PyTorch models, compressing models, implementing LLM inference, or optimizing CoreML performance. Covers model conversion, compression, stateful models, KV-cache, multi-function models, MLTensor.
Semantic Similarity Index for disease research literature using PubMedBERT embeddings
Local scVI-based single-cell latent embedding and batch-aware integration from raw-count .h5ad or 10x Matrix Market input, with stable integrated AnnData export for downstream latent analysis.
Automate Benchmark Email tasks via Rube MCP (Composio). Always search tools first for current schemas.
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.
Automate Mistral AI tasks via Rube MCP (Composio): completions, embeddings, fine-tuning, and model management. Always search tools first for current schemas.
Automate OpenAI API operations -- generate responses with multimodal and structured output support, create embeddings, generate images, and list models via the Composio MCP integration.
Golang benchmarking, profiling, and performance measurement. Use when writing, running, or comparing Go benchmarks, profiling hot paths with pprof, interpreting CPU/memory/trace profiles, analyzing results with benchstat, setting up CI benchmark regression detection, or investigating production performance with Prometheus runtime metrics. Also use when the developer needs deep analysis on a specific performance indicator - this skill provides the measurement methodology, while golang-performance provides the optimization patterns.
INVOKE THIS SKILL when creating evaluation datasets, uploading datasets to LangSmith, or managing existing datasets. Covers dataset types (final_response, single_step, trajectory, RAG), CLI management commands, SDK-based creation, and example management. Uses the langsmith CLI tool.
Standards for Baseline Profiles, Startup Time, and UI Rendering. Use when optimizing app startup, jank, or UI rendering performance in Android. (triggers: **/*Benchmark.kt, **/*Initializer.kt, BaselineProfile, JankStats, recomposition)
OWASP LLM Top 10 (2025) audit checklist for AI applications, agent tools, RAG pipelines, and prompt construction. Load during any security review touching LLM client code, prompt templates, agent tools, or vector stores. (triggers: LLM security, prompt injection, agent security, RAG security, AI security, openai, anthropic, langchain, LLM review)
Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
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.
PyTorch深度学习模式与最佳实践，用于构建稳健、高效且可复现的训练流程、模型架构和数据加载。
Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.
Machine learning development patterns, model training, evaluation, and deployment. Use when building ML pipelines, training models, feature engineering, model evaluation, or deploying ML systems to production.
Details of the RAG Chatbot, including UI and backend logic.
Details on the Retrieval Augmented Generation pipeline, Ingestion, and Vector Search.
Write token-efficient documentation for LLM context. Use when creating CLAUDE.md, README, technical docs, agent instructions, or any documentation consumed by AI assistants.