Field notes on AI Agents, CLI workflows, GitHub integrations, and secure local governance.
Author: Abdellah MOUHTAJ
Title: Ops Consultant - AI Agents, CLI Workflows & Local Governance
This repository is the public landing page for a growing series of operational field notes.
It exists to document real integrations, the failure modes they expose, and the guardrails required to make them reproducible.
Test unstable tools, stabilize the workflow, document the failure modes, and publish reproducible runbooks.
- CLI workflows
- GitHub integrations
- local governance
- authentication chains
- secure wrappers
- human-in-the-loop validation
- failure-mode documentation
- not a generic AI hype page
- not a code dump
- not a tutorial farm
- not a secret-bearing operational log
| ID | Field Note | Status | Focus |
|---|---|---|---|
| 001 | Google Jules <-> Antigravity CLI | MVP | Jules CLI authentication, GitHub App mapping, GitHub CLI auth, Git porcelain guard, local governance |
| 002 | Making Google Jules Actually Report Back Through Antigravity CLI | MVP 2 | Remote Jules sessions, explicit repository targeting, read-only result retrieval, Jules-authored reports, positive allowlist prompting, human-controlled apply/commit/push |
| 003 | Governor Memory | MVP 3 | Governed persistent memory, Markdown-first canonical memory, secret-aware sanitization, rollback-ready writes |
| 004 | mvp-github-writer | MVP 4 | Local Codex skill laboratory, GitHub-ready MVP documentation, SkillOpt optimization, governed Markdown output |
| 005 | Hermes SOUL | MVP 5 | Minimal viable personality layer for local AI agents, SOUL.md identity file, prompt-marker validation, restart-aware behavior testing |
| 006 | Codex Vortex Second Living Brain | MVP 6 | Governed local second brain, Vortex knowledge vault, session reprise, health checks, divergence checks, preflight checks, Git proof |
| 007 | Context Engineering for Codex CLI | MVP 7 | Governed Codex CLI workspace with ULTIMA and Playbook companion summaries |
| Index | Field Notes Series Index | Active | Canonical series overview, structure, and writing standard |
Every field note follows the same workflow:
- Test - reproduce the real-world integration.
- Break - identify silent failures, misleading success states, and documentation gaps.
- Stabilize - add wrappers, checks, guardrails, or operational rules.
- Document - publish a reproducible field note with failed hypotheses and lessons learned.
The value here is not only that the tools work.
The value is that they can be made:
- auditable
- reproducible
- governed
- usable in real operations
Precision in the workflow. Governance in the process. Stability in the result.
Facing similar integration challenges with AI Agents in your local workflows?
Let's build the runbooks together.