What it does
Prepare an Offer Approval Brief is a free AI skill for recruiters and hiring managers. Give it the role, the selection evidence and the proposed pay. It drafts a one-page offer approval request with position in band, an internal-equity check, the approvals chain and closing risks.
It works in Claude, ChatGPT, Microsoft Copilot and other AI Assistants.
The role, level, location and employment type, and why it is being filled (new role, backfill, growth)
The hiring decision, who made it and a short summary of the selection evidence
The proposed offer and the approved pay band
Optional: anonymised peer pay, your approvals chain, notice period, start date and counter-offer signals
The role need in two or three lines
The selection evidence by criterion, as the panel recorded it
The offer against the band, with position in band, compa-ratio and total cash
An internal-equity check
The approvals chain, with status and date
Closing risks, a closing plan and a checklist
How it helps
It puts everything an approver needs to see about an offer on one page, in the same order every time.
Numbers anyone can check
Position in band and compa-ratio show their working. An offer outside the band is labelled an exception.
Internal-equity check
It compares the offer with people doing equal work or work of equal value, not just the same title, and flags pay above peers. It never uses current or past pay.
Approval before anything goes out
It never recommends a pay figure or drafts the offer as if approved. Closing risks come only from what the candidate or team said.
Why we think it's useful
Position in band: 70% ((74,000 - 60,000) / (80,000 - 60,000))
Compa-ratio: 1.06 (74,000 / 70,000)
Exception to band? No
Finding: Above peers on base. The offer is EUR 3,000 to 6,000 above both current seniors; total cash comparison [TBC].
Action: HR (Marta) to confirm which roles count as equal work or work of equal value, compare total cash, check peers' evidence and tenure, and decide whether peer pay needs review before or alongside this offer.
The candidate's current pay was left out of this brief. The offer should rest on the band, the role and the selection evidence.
Fictional company, role, candidate and people.
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Which AI do you use?
Closing the predictive-validity loop requires structured pre-hire data on every requisition. Assessment scores. Behavioral profiles. Hard-skills signals including AI fluency. Without that dataset, you cannot correlate pre-hire signals with post-hire QoH. You cannot tune the next cohort sharper than the last. The hiring system stays static. The score drifts.
Bryq builds the pre-hire dataset that makes the loop possible.
Claude
ChatGPT
Microsoft Copilot
Something else (Gemini, Mistral and others)
Better with Bryq
Already use Bryq? Add your assessment results.
Link your Bryq account to your AI assistant, and five skills can include your candidates' Bryq results. For example, your debrief shows them next to the interview notes, and your follow-up questions focus on where the two differ. The link only reads data; it never changes anything in Bryq. Results you bring into a chat are shared with your AI provider, like anything else you paste in. Every skill works fully without it.
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