Use these before relying on Codex output or a Codex-adjacent tool in real work. They are practical checks, not legal, security, or compliance advice.
- Confirm the output answers the stated work problem.
- Check names, dates, owners, numbers, links, and source references.
- Mark assumptions, missing inputs, and uncertain claims.
- Remove private or sensitive material before sharing.
- Confirm the output format is usable by the next reviewer.
- Decide whether the work needs a second human reviewer.
- Link each important claim to a source, file, note, or dataset.
- Separate source-backed facts from model judgment.
- Flag any claim based on stale, partial, or unverified input.
- Preserve retrieval dates for time-sensitive external sources.
- Do not copy user, forum, community, or third-party prose as your own analysis.
- Keep source files available for the person who must approve the work.
- Know what system the MCP server or connector can read.
- Know what it can write, send, delete, or change.
- Check whether it sends data to a third-party service.
- Avoid sending customer, employee, legal, medical, financial, credential, or confidential data unless your policy allows it.
- Prefer read-only access for exploration and review tasks.
- Revoke or rotate credentials when a test connector is no longer needed.
- Confirm a human approved the final text or file.
- Confirm the intended audience and destination are correct.
- Remove draft notes, internal paths, credentials, and hidden comments.
- Check that any Codex or OpenAI wording is clearly independent and sourced.
- Confirm no unsupported claim about performance, availability, pricing, or official status remains.
- Save the source links and final approved version where your team can find them.
- Does the entry map to a real workflow problem?
- Is there a public source URL?
- Does the entry state what the tool does not do?
- Is the submitter affiliation clear?
- Is the category correct?
- Would an office or semi-technical user know why this matters?