AI Rig
A collection of personal AI coding assistant configurations, specialist agents, and automated workflows optimized for Python and ML open-source development.
Install
claude plugin install foundry@borda-ai-rigInstall command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →🏠 Borda's AI-Rig
Practical agent workflows for Python, ML, and open-source maintenance. AI-Rig turns recurring work—scoping a change, reproducing a bug, reviewing a pull request, running an experiment, or checking release readiness—into explicit workflows with specialist ownership, evidence gates, and reviewable artifacts.
Seven packages serve two runtimes: six marketplace plugins for Claude Code, three for Codex, with codemap-py and bridge_CC-Codex shared by both.
Contents
- Start here
- What this setup enables
- Complete capability map
- Installed-all blueprint
- Install for Claude Code
- Install for Codex
- What each plugin solves
- Practical workflow sequences
- How the packages compose
- Optional integrations and add-ons
- Artifacts and evidence
- Current boundaries
- Repository checkout and synchronization
- Upgrade and remove
- Troubleshooting
- Documentation map
- Contributing
⚡ Start here
Choose the smallest plugin that solves the job in front of you. Every package installs independently; optional integrations add specialist depth or structural context without becoming hidden prerequisites.
| Need | Start with | What it gives you |
|---|---|---|
| Keep Claude agent configuration, routing, and reusable guidance healthy | 🏭 foundry |
10 specialist agents, 11 configuration/content skills, rules, hooks, audit, and calibration |
| Triage GitHub work, review PRs, resolve feedback, and prepare releases | 🌱 oss |
4 agents and 5 maintainer skills with human-reviewed replies and no automatic publish step |
| Plan, build, fix, refactor, debug, or review Python changes | 🛠️ develop |
6 validate-first development workflows plus setup, with reproduction/demo/safety-net gates |
| Plan and run literature-grounded ML experiments | 🔬 research |
2 research agents and 10 skills for planning, judging, running, verifying, ablation, retrospectives, and Kaggle notebooks |
| Answer import, caller, coupling, rename, and affected-test questions | 🗂️ codemap-py |
The same 6 structural-analysis skills for Claude Code and Codex, backed by a local static Python index |
| Use evidence-first workflows and specialist role cards |
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