What it does
Summarize a Candidate Debrief is a free AI skill that turns a panel's interview feedback and scorecards into a short summary: evidence by criterion, where interviewers agree and differ, what is missing, and follow-up questions.
It's for recruiters and hiring managers, and works in Claude, ChatGPT, Microsoft Copilot and others. People make the decision.
The hiring criteria for the role
Interview notes and scorecards, with who interviewed and at which stage
Optional: each interviewer's vote, the rating scale, work-sample notes
Evidence by criterion, with the source of each point
How much evidence each criterion has, and where the panel agrees and differs
Votes recorded exactly as given
A process check: missing scorecards, criteria nobody covered, comments that weren't job-related
3–5 follow-up questions and a short agenda for the decision meeting
How it helps
It lays every feedback form and set of notes out to a fixed debrief template, criterion by criterion. It never adds an overall score, a ranking or a hire / no-hire recommendation.
Evidence before opinion
For each criterion it keeps what the candidate said or did apart from what the interviewer made of it, and names who saw it. Feedback outside the criteria goes under "Other observations".
Disagreement in plain sight.
It states where interviewers rated the same thing differently, without resolving it. A criterion covered by only one interviewer is marked "One interviewer", so a single view doesn't pass for agreement.
Process checks built in
It flags one strong impression colouring every criterion, "culture fit" comments with no job-related evidence, ratings with no notes behind them, and scorecards that never arrived.
Why we think it's useful
Fictional company, role, candidate and people.
Get this skill
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.
