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
Framework for demonstrating AI capabilities in legal contexts. Provides detailed personas across tenant law, business contracts, startup disputes, employment claims, and consumer protection with progressive complexity scenarios. Use when: (1) Demonstrating AI-powered legal triage or intake systems, (2) Showcasing responsible AI-assisted client interactions, (3) Training staff on appropriate AI use in legal contexts, (4) Creating realistic scenarios for legal tech presentations, (5) Developing educational materials about AI in legal services, or (6) Testing AI-powered legal information systems in controlled environments.
> Builds a high-fidelity interactive legal assessment as a single self-contained HTML artifact. Output includes a live countdown timer, contract review tasks with hover-annotated problem clauses, candidate answer textareas, model answers hidden behind reveal blocks, scenario-based legal memo tasks, strategy and function-building questions, and a pre-submission checklist that encodes the marking criteria. Use when the user needs to (1) assess a legal candidate with a realistic timed exercise, (2) train or onboard junior lawyers using problem sets rather than doctrine, (3) help a candidate prepare for a real take-home assessment they are facing, (4) build educational materials for law students, in-house teams, or compliance training, or (5) produce scenario-based training modules on specific legal topics. Triggers on "legal test", "take-home", "mock exam", "contract redline exercise", "candidate assessment", "legal training exercise", "practice test", or similar phrasing even when informal.
>- Act as experienced opposing counsel to attack, undermine, and expose weaknesses in a legal argument, submission, witness statement, or structured reasoning. 1. A core theory of attack identifying the single most effective way to defeat the argument; 2. A reconstructed version of the opposing argument stripped of rhetoric to expose its fragility; 3. Primary lines of attack grouped by category (legal misstatement, evidential gaps, causation failures, internal inconsistency, over-reliance on assertion, procedural weakness); 4. An "if I were the judge" section showing how a sceptical tribunal would dismantle the argument; 5. Surgical strikes - 3 to 5 high-impact points ready for oral submissions; and 6. An analysis of what the argument is trying to hide. Written in formal, adversarial British English for a legally trained audience.
Generates standalone interactive HTML "deal cards" that translate complex regulations into negotiation-ready reference tools, systematically distinguishing mandatory obligations from negotiable implementation choices. Use when the user needs an interactive regulatory guide for (1) contract negotiation support, (2) client education or internal training, (3) regulatory briefings for commercial stakeholders, or (4) structured comparison between required and flexible compliance paths. Primary focus on EU digital regulation (Data Act, AI Act, CRA, DORA, NIS2, GDPR) but the structural pattern transfers to any regulation where separating hard obligations from implementation choice is the point. Supports bilingual output where the jurisdiction calls for it.
Draft matter status reports from emails, call notes, and updates. Internal and client-facing formats, RAG logic, variance commentary, escalation flags. Use when asked to draft a status report, write a project update, summarise matter progress, prepare a client report, create a weekly or monthly update, convert emails into a status summary, or produce any kind of matter reporting. Also triggers when the user pastes email threads and asks what the status is, or needs to turn internal updates into client-facing reports.
Use when building AI agents in Dart, implementing Genkit flows or tools, integrating LLMs into Dart or Flutter applications, or using Genkit Dart plugins.
>- Inspects and configures the security headers a Power Pages site sends to browsers — Content Security Policy, frame and clickjacking protection, cross-origin sharing, cookie behavior, and related site settings. Identifies gaps and walks the user through fixes. Use when the user wants to review headers, fix CSP errors, allow embedding in another site, control cross-origin access, harden cookie settings, or asks "are my browser settings safe?", "fix my CSP", "set up CORS" — even if they only mention a specific header name without saying "security headers".
Swift 6.2 concurrency updates including default MainActor inference, @concurrent for background work, isolated conformances, and approachable concurrency migration. Use when adopting Swift 6.2 concurrency features or fixing data-race errors.
SwiftUI framework skills — data flow (identity, Observation, state ownership), layout & containers (Layout protocol, lazy-stack performance), AlarmKit, 3D charts, rich text editing, customizable toolbars, and WebKit embedding. Use for SwiftUI state/rendering bugs, custom layouts, scroll performance, or these feature areas.
WebKit integration in SwiftUI using WebView and WebPage for embedding web content, navigation, JavaScript interop, and customization. Use when embedding web content in SwiftUI apps.
> Build a webinar or live event registration page as a self-contained HTML file with countdown "create a webinar sign-up page", "event registration landing page", "live training registration page", "workshop sign-up page", "create a webinar page", "build an event page", "free webinar landing page", "live demo registration page", "online event page", "create a registration page for my webinar", "build a training event page".
数学建模的题目理解、模型选择和算法设计阶段。输出题目分析报告与术语表格。
Shopify/DTC profit margin calculator for sellers. Calculate cost breakdowns including ad spend, CAC, payment processing fees, and 3PL costs. Includes LTV/CAC analysis and DTC-specific benchmarks. No API key required.
Supply Chain Bottleneck Analyzer for Shopify/DTC stores. Diagnose cash flow, inventory, shipping costs, and customer acquisition efficiency. Includes CAC/LTV analysis, 3PL cost optimization, and ad spend benchmarks. No API key required for basic analysis.
Accepts a product description and URL to algorithmically identify relevant Venture Capital investors targeting exactly that stage, industry, and niche based on a curated static dataset.
Build the game for its approved target runtime. Compress GAME_DESIGN and ART_DIRECTION into a minimal BUILD_BRIEF, hand it to the current coding agent or another strong model to implement a fully playable build, and iterate against real runs and captured evidence. Use for implement the approved game design, build the game prototype, turn this design into a running game. 游戏构建执行。把批准后的 GAME_DESIGN 与 ART_DIRECTION 压缩成最小 BUILD_BRIEF，交给当前编码智能体或其他强模型，在选定的目标运行环境中实现可完整游玩的版本，并通过真实运行和证据迭代。用于把批准的游戏方案实现成可运行游戏。
OWASP LLM Top 10 (2025) audit checklist for AI applications, agent tools, RAG pipelines, and prompt construction. Use when performing any security review touching LLM client code, prompt templates, agent tools, or vector stores.
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.
Optimize Android app startup, UI rendering, frame stability, and benchmark performance with Baseline Profiles, Macrobenchmark, and lazy initialization. Use when reducing startup time, diagnosing jank, or measuring rendering; defer Compose state API and test-only questions.
Use when calling the app's AI gateway from agent tools — chat completions, embeddings, listing models, configuring defaults or BYOK, reading token/cost usage
Use as the AI build stage of the Butterbase journey. Implements the AI section of 02-plan.md by delegating to the ai skill. Calls manage_ai (update_config) to set defaults and optionally BYOK. Skipped if the plan has no LLM/embeddings usage.
Use as stage 2 of the Butterbase journey, after journey-idea has written 01-idea.md. Translates the idea + capability map into a concrete Butterbase plan — tables (with columns/types/RLS shape), auth providers, function list (name + trigger), storage buckets, AI/RAG/realtime/durable usage, and the chosen frontend stack. In hackathon mode, ruthlessly cuts scope into a "ship now" vs "post-hackathon" split. Produces docs/butterbase/02-plan.md.
Use as the RAG build stage of the Butterbase journey. Implements the RAG section of 02-plan.md by delegating to rag-dev. Calls manage_rag_content (create_collection, ingest_document). Skipped if the plan has no knowledge-base feature.
Use when building knowledge bases, ingesting documents, running semantic search, or adding LLM-synthesized Q&A over private content with Butterbase RAG
Compares tools or implementations through reproducible A/B workloads, correctness oracles, and controlled measurements. Use to choose alternatives; not to optimize a known bottleneck.
Design discipline-specific critical thinking tasks grounded in knowledge-contingent reasoning rather than generic skills. Use when embedding higher-order thinking into subject content.
Design a structured outdoor learning sequence embedding curriculum objectives in an available outdoor space. Use when planning lessons in school grounds, parks, or local natural environments.
Design a critical thinking task targeting specific skills like evaluating evidence, identifying bias, or analysing arguments. Use when embedding critical analysis into subject lessons.
Slow down interpretation from observation to action. Use when students or adults need to examine assumptions in conflict, dialogue, or inquiry.
Framework for measuring and tracking agent response quality over time. Detects regressions before they reach production. Use when evaluating agent changes, auditing quality, or establishing performance baselines.
Competitive analysis - feature matrix, SWOT, market positioning, benchmark.
LLM Tuning Patterns
Diagnose marketing performance for global businesses — root cause analysis, 5-Whys, 48-hour action plan. Has 4 region variants for benchmarks (US/EU/SEA/LATAM). Reads `.agents/product-marketing-context-global.md`. INCLUDES Dropshipping KPI section (ROAS, BE-ROAS, profit margin, CAC). Trigger: 'performance review', 'ad performance', 'marketing diagnosis', 'KPI analysis', 'dropshipping ROAS'.
Phan tich doi thu canh tranh 3 tang (truc tiep, gian tiep, thu cap) — dinh vi, SWOT, content benchmark, tim khoang trong thi truong
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster. Build reliable, observable, and retriable workflows for production AI systems.
Implement multi-layer LLM caching with exact match, semantic similarity, and provider-side prompt caching. Reduce API costs by 30–70%, cut latency, and improve throughput using Redis, GPTCache, and provider caching APIs.
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.
Build production LLMOps platforms with CI/CD, model promotion workflows, evaluation gates, rollback, and governance across cloud and self-hosted inference.
Monitor and evaluate RAG systems with retrieval quality metrics, groundedness checks, hallucination detection, and continuous regression testing.
Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.
Auto-scale LLM inference clusters on Kubernetes using KEDA, custom GPU metrics, and horizontal pod autoscaling. Handle traffic spikes, implement queue-based scaling, and optimize cost with spot instances for AI workloads.
Configure a Mac mini as a reliable local LLM server with remote access, observability, and power-safe operation. Use when building an always-on private AI inference server on Apple Silicon.
Design secure, multi-tenant LLM hosting platforms with tenant isolation, quotas, billing attribution, noisy-neighbor protection, and per-tenant policy controls.
Set up infrastructure for fine-tuning LLMs with QLoRA, LoRA, and full fine-tuning using Hugging Face TRL, Axolotl, and distributed training with DeepSpeed or FSDP. Covers dataset prep, training runs, and model export.
Run local LLM workloads with Ollama, Open WebUI, and GPU-aware tuning for private development environments. Use when setting up private inference, local AI dev environments, or air-gapped LLM deployments.
Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.
Deploy and manage vLLM for high-throughput LLM inference. Configure continuous batching, tensor parallelism, quantization, and OpenAI-compatible API endpoints for production LLM serving.
Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.