pr-review-toolkit
claudeComprehensive PR review agents specializing in comments, tests, error handling, type design, code quality, and code simplification
1 command · 6 agents
Interactive learning mode that requests meaningful code contributions at decision points (mimics the unshipped Learning output style)
Plugin learning-output-style from marketplace claude-code-plugins in Ralle1976/claude-code-multimodel. Commands read from the repo's manifest — 1 month ago.
This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.
${CLAUDE_PLUGIN_ROOT}/hooks-handlers/session-start.sh
Comprehensive PR review agents specializing in comments, tests, error handling, type design, code quality, and code simplification
1 command · 6 agents
Comprehensive feature development workflow with specialized agents for codebase exploration, architecture design, and quality review
1 command · 3 agents
Development kit for working with the Claude Agent SDK
1 command · 2 agents
Commands for git commit workflows including commit, push, and PR creation
3 commands
Automated code review for pull requests using multiple specialized agents with confidence-based scoring to filter false positives
1 command
Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
hooks
Interactive learning mode that requests meaningful code contributions at decision points (mimics the unshipped Learning output style)
hooks
Agentic QE (external: maintained in proffesor-for-testing/agentic-qe, pinned to v3.15.17): a quality-engineering fleet with 11 QE agents, 9 skills, 9 commands, the agentic-qe MCP server pinned to the plugin version, and the aqe-mod guard that refuses destructive operations on AQE learning data
9 skills · 9 commands · 11 agents · 1 MCP server
Cross-session memory and token savings for coding agents.
6 skills · 1 agent · 1 MCP server · hooks
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
Claude Code native multi-agent orchestration with intelligent model routing across agents and skills. Zero learning curve. Maximum power.
85 skills · 20 commands · 19 agents · 1 MCP server · hooks
Turn any learning intent into a personalized Coursera experience. Asks three quick questions (topic, familiarity, preferred format), searches Coursera's catalog, and delivers the right next step — a course, hands-on project, short video, or live roleplay — then maps a path forward. Requires the Coursera connector for catalog tools.
1 skill
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