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Public-safe proof

AI workflows + durable context

Gemini/Codex Workflow Automation

Built a file-driven AI workflow system that keeps Gemini CLI and Codex CLI aligned through durable context, reconciliation, and validation gates.

ShellPythonGemini CLICodex CLIMCPn8n

Gemini/Codex Workflow Automation

Gemini CLI · Codex CLI · AI-assisted workflows · durable context

What this proves

  • Problem: long-running AI coding workflows break when context, handoff, and validation live only in chat memory.
  • Method: built a file-driven orchestration environment around declarative intent, reconciliation, bridge scripts, MCP / n8n automation lanes, explicit guardrails, and deterministic close-out gates.
  • Outcome: created a working automation system with 100 scripts, 34 docs, and 58 research artifacts in the read-only source repo.

Why it matters for AI workflow roles

The system treats AI-assisted work like an operating workflow: agents need durable context, defined handoff paths, validation gates, rollback-minded close-out, and public-safe documentation.

Role relevance:

  • AI workflow specialist
  • BI automation analyst
  • analytics automation engineer
  • business systems automation analyst
  • BI automation analyst

Public-safe proof surface

The public layer includes:

  • the documentation repo
  • the architecture/runbook narrative
  • the operating ideas that make the workflow durable

The larger working repo remains private and local-only.

Open public docs repo

Public proof themes

  • durable context
  • declarative intent
  • reconciliation loop
  • blackboard / swarm coordination
  • Gemini/Codex bridge interoperability
  • MCP / n8n automation lanes
  • guardrails and tool boundaries
  • deterministic close-out and validation gates