colab-mcp

🏠 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.

Eight packages serve three runtimes: six marketplace plugins for Claude Code, three for Codex, and one npm plugin for OpenCode, with codemap-py and bridge_CC-Codex shared by two hosts each.

Design principles:

• Simple and reliable: favor workflows that complete the user's goal, recover from routine problems, and reuse valid evidence.
• No silent blockers: routine incomplete handoffs and self-created blockers are defects to fix. Genuine permission, prerequisite, and user-decision boundaries stay explicit, with safety and verification intact.
• Root cause, not symptom: establish the cause with evidence and a falsification check before changing behavior. If it is unproven, keep investigating.
• Verified fixes: a fix is complete only after the original failure and affected sibling workflows are verified.

Contents

• Start here
• What this setup enables
• Complete capability map
• How it fits together
• Install for Claude Code
• Install for Codex
• Install for OpenCode
• 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
• Articles
• 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.

If you use Claude Code, read the Claude guide. If you use Codex, read the Codex guide. OpenCode installs the npm package directly; its README is the host guide.

🎯 What this setup enables

Show the evidence-first value of the full suite

• A bounded route from uncertainty to evidence: Foundry audits configuration and routing, Develop gates Python changes, OSS organizes maintainer work, Research keeps experiments reviewable, Codemap-py supplies static structure, and Codex Rig and OpenCode Rig provide the host-native workflow layer for Codex and OpenCode.
• Scope-selected specialist review: OSS and Codex Rig use narrow context packs and mandatory QA/challenge coverage where their contracts require it; smaller or lower-risk work can stay on the parent path.
• Review-to-remediation continuity: OSS and Codex Rig save findings as artifacts, ask which work to select, group related findings, and verify the selected closure rather than silently applying every comment.
• Validate-first development: Develop's feature path requires a failing demo, its fix path requires a failing regression test, and its refactor path protects current behavior before editing.
• Metric-guarded research: Research proposes one scoped change, measures the configured metric and guard, keeps improvements, and reverts regressions as auditable Git history.
• Calibration and recovery: Foundry and Codex Rig measure workflow/instruction quality, confidence, and known leaks; those results improve the process but do not guarantee model correctness.

These are workflow contracts, not promises that every run launches every specialist, finds every dynamic dependency, or publishes a correct change. The linked plugin READMEs define each route's prerequisites, fallback, and stop behavior.

🔧 Complete capability map

This is the literal shipped skill inventory. The host guides list every agent, role, rule, and hook; the plugin READMEs document arguments, outputs, prerequisites, recovery, and edge cases.

Show complete capability map