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How to run an evidence-bound review with the requirement-ledger CLI with GPT 6 Astra Skill

Create and check a bounded audit scaffold with an explicit window, timezone, and approved output path.

Who it is forA developer or auditor who needs a reproducible, checkable review of one target for a fixed time window.

The problemA quick chat audit cannot be reproduced or handed off, so reviews need explicit inputs and a checked report.

The outcomeA validated audit scaffold and a review-check result for a final report written to an approved non-home path.

What you need first

  • Git
  • Codex with local plugin support
  • Python 3 for the requirement-ledger CLI, installed with uv or pip
  • A local project folder to select as the target

Steps

  1. From the checkout, run uv tool install ..
  2. Run requirement-ledger --version and confirm the release you installed.
  3. Run requirement-ledger review-init --mode audit --target project:example --start 2026-08-01T08:00:00+08:00 --end 2026-08-02T08:00:00+08:00 --timezone Asia/Shanghai --output /approved/review/final-report.md, replacing the example values with your own approved values. (inferred, check the README)
  4. Run requirement-ledger review-check /approved/review/final-report.md and read the result before treating the report as checked.

Prompt for your agent

Using the requirement-ledger CLI from the gpt-6-astra-skill repo, create an audit scaffold for project:<target> over the window <start> to <end> in timezone <timezone>, written to the approved non-home path <output>. Return the report path and the review-check output. Verify the review-check output before calling the report checked.

Caveat. The CLI is the compatibility core, and passing it does not by itself prove that every product or public-release gate has passed.

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