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Agent hiring panel

Skill Amey-Thakur/AI-SKILLS/skills/multi-agent-teams/agent-hiring-panel

Plug-and-play skills and prompts for every AI coding agent

Install
npx -y skills add Amey-Thakur/AI-SKILLS --skill agent-hiring-panel

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What its author says it does

Copied from the file, not written here

Simulate an interview panel as agents that design questions, score independently on anchored rubrics, and reconcile in a debrief. Use when you want to pressure-test a hiring loop, calibrate rubrics, or dry-run questions, not to score a real candidate.

SKILL.md

3.9 KB, as published. Nobody here has run it

Hiring panel of agents

A panel is only as honest as its independence. Run one agent as the whole interview and you get one perspective scoring itself; run four agents who see each other's scores first and you get agreement, not signal. A real loop assigns one competency per interviewer, scores each in writing before anyone shares, and reconciles on evidence. Skip the independence and the debrief becomes an echo of whoever spoke first.

Team

  • Loop designer (hiring-loop-design): builds the competency matrix and assigns one owner per signal.
  • Interviewers (bar-raiser-interviewing): each owns one competency, writes questions, scores independently.
  • Bar raiser (bar-raiser-interviewing): holds the same bar across the panel.
  • Debrief chair (perf-calibration, engineering-manager-role): reconciles scores.

Shape: sequential design, a parallel independent-scoring panel, then a calibration debrief.

Method

  1. Designer builds a competency matrix, one owner per signal. Map the role to four to six competencies (coding, system design, project depth, collaboration) and assign each to exactly one interviewer, so no signal is tested twice and none is missed. Output loop-plan.md.
  2. Each interviewer writes questions to an anchored rubric. Define what a 1, 2, 3, and 4 answer looks like with a concrete example per level before the interview. A question that probes three competencies scores none well.
  3. Score independently and in writing before sharing. Each submits scorecard-<name>.md: question, response summary, an evidence quote, a rating, and a hire or no-hire on its competency, locked before debrief. Shared scores in advance manufacture consensus.
  4. Separate evidence from impression. Every rating cites what the candidate said or built, not "seemed strong." A score with no evidence line is dropped in debrief.
  5. Bar raiser holds the line across candidates. One agent, not the hiring manager, checks each scorecard against the same bar so an urgent req does not lower it. It can veto, not boost.
  6. Debrief reconciles on evidence, not a vote count. The chair walks each competency, surfaces the split, and resolves it by re-reading evidence. A 3-1 split is a conversation about the one, not a majority overrule.
  7. Decide against written criteria and record dissent. debrief.md states the outcome, the deciding evidence, and any unresolved concern, so the loop stays consistent next time.

Run it

In Claude Code, run the designer as one subagent, then spawn interviewers as parallel subagents that cannot read each other's output directory until each has written its locked scorecard; the chair subagent reads all scorecards plus the bar raiser's notes to produce debrief.md. Port it to CrewAI as a design task feeding parallel interview tasks then a debrief task, to AutoGen as a GroupChat where the chair opens turns only after scorecards are filed, or to LangGraph as a fan-out with a barrier that blocks the debrief node until every scorecard commits.

Signals it works

  • Interviewers submit scores before seeing each other's, and the splits survive.
  • Every rating in a scorecard cites specific candidate evidence.
  • The debrief changes minds by re-reading evidence, not by majority.

Boundaries

This rehearses and calibrates a hiring loop; agents do not decide on real candidates, which is human judgment carrying legal and fairness duties agents cannot hold. Use it to pressure-test rubrics, dry-run questions, or train interviewers, not to auto-score a person. Competency definitions, the bar, and anti-bias practice are your organization's policy.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.