Tailor your resume to a job, with an AI checking the AI

Resume Tribunal is a free, open-source skill for AI assistants. It rewrites your resume for a specific job description, then has an assistant from a different company review it blind, so no AI grades its own writing.

npx skills add mohiddin7/resume-tribunal

Then tell your assistant: "Tailor my resume to this job: [link]". Source and install options on GitHub.

How it works

STEP 1

You give the job, your resume, and a few sentences in your own words about what you did.

STEP 2

Your assistant drafts the resume using only your facts. In full council mode, three assistants from three companies each draft one.

STEP 3

An assistant from another company reviews the draft without knowing who wrote it. Every criticism has to quote the line it is about.

STEP 4

A script checks for ATS problems, page length and common AI writing habits. You confirm every changed claim.

STEP 5

You get the resume, the job requirements it still does not show, and the interview questions its claims will invite.

If you have other AI tools installed, the reviews run by themselves in a few minutes. If not, you paste one prompt into another AI app and paste the reply back.

Why a second AI

AI models favour their own writing. In a 2025 study of resume screening, models preferred resumes they had rewritten over human-written ones 67 to 82 percent of the time (Xu, Li and Jiang). If one AI writes your resume and also judges it, its blind spots go unchecked. Resume Tribunal splits the two jobs.

It does not promise interviews, and no study shows a council of AIs writes better than one strong assistant. What you get is independent review and a resume you can defend line by line.

What it will and will not do

A real run

A fictional operations coordinator applying for an operations analyst job. The run used three assistants from three companies. Employers are described by type, and nothing was edited by hand afterwards.

- Responsible for daily shipment tracking and reporting
- Worked with carriers and customers to resolve issues
- Helped improve on-time delivery
- Utilized Excel for various reports
- Assisted with training of new hires

- Traced one carrier's late loads out of the Dayton terminal to its Friday pickups using six months of data in Excel, and presented the finding to the operations manager and the terminal manager.
- Raised on-time delivery at that terminal from 81 percent to 93 percent over about three months, as part of the team that moved those Friday loads to a different carrier.
- Built the weekly report on late deliveries with pivot tables and Power Query, which reduced report preparation time from half a day to about 40 minutes.
- Track about 400 shipments a day across 12 carriers.

• Each reviewer caught something the others missed. One noticed a draft had rounded one year and ten months up to "2 years". The host caught a draft crediting the candidate alone for a decision the team made.
• Nine enlarged or invented claims were rejected. A reviewer suggested "from 4 hours to 40 minutes"; the candidate had said "half a day".
• Gaps stayed visible. The job asks for Power BI. The candidate has not used it, so it is not on the resume, and it is on the list of gaps to address.

Both outside reviewers, reading blind, said they would phone-screen the candidate. See the full run: every draft review, the final resume and the interview questions.

Install

Questions