explanatory-output-style
claudeAdds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
hooks
A contribution harness for open source: each project's unwritten rules in your agent's context, and your contributions counted on opensrcer.
Plugin opensrcer from marketplace opensrced in rahulp7801/opensrced. Commands read from the repo's manifest — 2 days ago.
This plugin runs code on your machine — it starts local MCP servers. The exact commands are listed below; read them before installing.
contributeUse when the user wants to contribute to an open-source repository they don't maintain — fixing an issue, opening a pull request, or deciding which issue to take. Brings in the project's measured unwritten rules (opensrcer repo_profile), then works so the PR fits the project and the user can stand b
learnUse when the user wants to learn an open-source codebase while contributing, wants to be taught or quizzed, or wants to write the fix themselves instead of getting a finished one. Runs an opensrcer lesson (lesson_start) where you are the teacher; the lesson panel shows the questions and a code map i
setupUse when the user is setting up opensrcer, asks whether it works or what's wrong with it, or when an opensrcer tool failed with a token, access or install error. Runs opensrcer's setup_check and walks the user through each fix.
opensrcerruns: node ${CLAUDE_PLUGIN_ROOT}/server.mjs
Adds educational insights about implementation choices and codebase patterns (mimics the deprecated Explanatory output style)
hooks
Open-source AI job search that runs locally in your AI coding CLI: scan job portals, evaluate listings into a structured A-H report with a 1-5 score, tailor your CV, track applications
5 skills · 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
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