dev
claudeDevelopment experts for 10 programming languages (Python, TypeScript, JavaScript, Go, Rust, C++, Kotlin, PHP, Lua and Perl), plus FastAPI and Node.js specialists and a refactoring agent.
13 agents
Machine learning framework specialists for PyTorch, TensorFlow, and scikit-learn. Expert guidance for ML model development and training.
Plugin machine-learning from marketplace swe-marketplace in andisab/swe-marketplace. Commands read from the repo's manifest — 6 days ago.
pytorch-expertExpert in PyTorch for building and optimizing deep learning models.
scikit-learn-expertMaster scikit-learn for machine learning, focusing on model selection, feature engineering, and hyperparameter tuning. Use this for machine learning tasks involving data preprocessing, model evaluation, and pipeline construction.
tensorflow-expertExpert in TensorFlow, specializing in developing, optimizing, and deploying machine learning models using TensorFlow framework.
Development experts for 10 programming languages (Python, TypeScript, JavaScript, Go, Rust, C++, Kotlin, PHP, Lua and Perl), plus FastAPI and Node.js specialists and a refactoring agent.
13 agents
Comprehensive toolkit for creating production-ready Claude Code resources: agents, skills, plugins, commands, hooks, MCP tools, MCP servers, and patterns. Expert guidance with templates, examples, and best practices following Anthropic specifications.
7 skills · 1 agent
Infrastructure and DevOps specialists for AWS, GCP, Kubernetes, Docker, Terraform, GitHub Actions, GitLab CI, and security auditing.
8 agents
Style-driven deliverable suite: slide decks (PowerPoint or HTML), multi-page HTML knowledge bases, and diagrams/data charts (Mermaid, SVG, or draw.io) that all render from one shared brand/style registry. Works out of the box with 5 built-in styles; add your own brand once and every deliverable matches. A librarian agent keeps sites and decks current: what's due, update plans, and scheduled fact-check sweeps.
4 skills · 2 commands · 1 agent · 1 MCP server
Database experts for SQL, NoSQL, graph databases, and vector databases. Includes Postgres, MongoDB, Neo4j, Cassandra, MariaDB, and vector DB specialists.
7 agents
Data science and data engineering agents for visualization (D3, Highcharts), notebooks (Jupyter, Google Colab), R, and Python data engineering.
6 agents
Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams
26 skills · 4 commands · 3 agents · 1 MCP server
Eval-gated LLM fine-tuning lifecycle (LoRA/QLoRA, DPO, GRPO/RLVR, vision SFT, quantized export). Use when fine-tuning an open-weights model end-to-end: building the eval harness first, selecting method and model, preparing data, training with Unsloth or TRL, gating checkpoints, and exporting GGUF/FP8.
10 skills · 2 commands · 3 agents
32 engineering skills: architecture, frontend, backend, fullstack, QA, DevOps, security, AI/ML, data engineering, Playwright (9 sub-skills), self-improving agent, Stripe integration, TDD guide, tech stack evaluator, Google Workspace CLI, a11y audit (WCAG 2.2), Azure cloud architect, GCP cloud architect, security pen testing, Snowflake development, adversarial-reviewer, ai-security, cloud-security, incident-response, red-team, threat-detection. v2.8.1 audits senior-fullstack / senior-frontend / senior-backend against karpathy-coder + Matt Pocock — each ships a 7-question forcing-question library, 4 customization profiles (JSON), deterministic decision engine, composition map into POWERFUL specialists, plus cs-fullstack-engineer / cs-frontend-engineer / cs-backend-engineer orchestrator agents (context: fork) + /cs-fullstack-review, /cs-frontend-review, /cs-backend-review, /cs-engineer-grill slash commands.
35 skills · hooks
Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture
ML experiment tracking and lifecycle including Weights & Biases, MLflow, and TensorBoard. Use when tracking experiments or managing models.
98 skills
AI and ML skills for .NET: technology selection, LLM integration, agentic workflows, RAG pipelines, MCP, and classic ML with ML.NET.
1 skill
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