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Mission control tensorflow scaffolding

Skill MN755/Codex-Mission_Control/.codex/skills/mission-control-tensorflow-scaffolding

An Open Source Platform For Building Projects With OpenAI's Codex, Claude CLI, And Other Coding LLMs Using Multi-Agent Swarms And Customizable Workflows. Runs On Windows, MacOS, And Linux.

Install
npx -y skills add MN755/Codex-Mission_Control --skill mission-control-tensorflow-scaffolding

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Route TensorFlow and Keras project scaffolding through Mission Control with explicit validation, export, and product-readiness expectations.

SKILL.md

1.5 KB, 279 tokens by cl100k_base, as published. Nobody here has run it

Mission Control TensorFlow Scaffolding

Purpose

Use Mission Control to scaffold TensorFlow or Keras code with real project structure, validation steps, and product-facing outputs instead of another disposable notebook.

The Codex chat agent is not the Mission Control Manager. It is the bridge between the user and the Mission Control Manager.

Use when

  • The user wants to start a TensorFlow or Keras project.
  • The repo needs a clean training, evaluation, and export skeleton.
  • Product code matters more than demo-notebook theater.

Workflow

  1. Confirm the repo or requested feature actually needs TensorFlow.
  2. Ask Mission Control to choose a Keras-first starter shape for the product goal.
  3. Keep data loading, training, testing, export, and deployment surfaces separate.
  4. Require a validation loop that proves the scaffold runs before handoff.

Mission Control calls

Tools:

  • mission_control_start_task
  • mission_control_get_status

Resources:

  • mission-control://projects/{project_id}/codebase-map
  • mission-control://projects/{project_id}/validation-summary

Never do

  • Do not treat TensorFlow scaffolding like generic Python boilerplate.
  • Do not stop at model.fit() if the user asked for a product path.

Example invocation

Use Mission Control to scaffold a Keras-first TensorFlow product workflow in this repo.

What ships with it

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