tensorflow

Essential infrastructure for the Han plugin marketplace including skills, commands, MCP servers, and quality enforcement hooks.

24 skills · 1 agent · 3 MCP servers · hooks

MCP server for ClickUp integration with automatic acceptance criteria validation. Provides task management, workspace access, and ensures work completeness.

8 skills · 1 MCP server

MCP server configuration for GitHub integration providing repository management, issues, pull requests, actions, and code search.

7 skills · 1 MCP server · hooks

MCP server configuration for GitLab integration providing issue management, merge request operations, code search, and project data access.

7 skills · 1 MCP server · hooks

MCP server for ClickUp integration with automatic acceptance criteria validation. Provides task management, workspace access, and ensures work completeness.

8 skills · 1 MCP server

MCP server configuration for GitHub integration providing repository management, issues, pull requests, actions, and code search.

7 skills · 1 MCP server · hooks

Comprehensive PR review agents specializing in comments, tests, error handling, type design, code quality, and code simplification

1 command · 6 agents

Multi-agent swarm coordination, memory management, SPARC methodology, and intelligent hooks for Claude Code. Provides 60+ skills, 100+ commands, and 80+ specialized agents.

hooks

Install the "AAS Automation Builder" workflow plugin from Agentic Awesome Skills.

20 skills

Transform Claude into an Azure expert. This plugin integrates the Azure MCP server and specialized Azure skills to move beyond generic advice. It enables Claude to perform real-world tasks: listing resources, validating deployments, diagnosing infrastructure issues, and optimizing costs across 50+ Azure services.

44 skills · 1 MCP server

Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.

1 skill · 3 commands · 1 agent

Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture