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agent-hiring-panel

Hire an AI agent the way you'd hire an employee — a role spec with success criteria, a structured work-sample interview run on your real tasks, reference checks (what do actual users report), probation KPIs, and termination criteria written before day one. Use when choosing between AI agents/tools/copilots for a job, formalizing an AI pilot, or 'which agent should we use for X'. Produces the role spec, interview pack with scoring rubric, a decision record, and a probation plan.

AI 与智能体1.4kskills/agent-hiring-panel/SKILL.md

安装

把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性,你确认后才安装。

读取 https://funcoding.ai/skills/mohitagw15856/pm-claude-skills/agent-hiring-panel/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

Agent Hiring Panel Skill

Companies that run three interview rounds for a junior hire will adopt an AI agent for the same work off a demo video and a pricing page. Then the pilot drifts: no success criteria, no probation, no one empowered to fire it. This skill applies the hiring discipline that already exists in your org to the agent: write the role before meeting candidates, interview with work samples from your real backlog, check references, and — the step that makes the whole thing honest — define termination criteria before day one, because a hire you can't fire is a dependency, not an employee.

What This Skill Produces

  • A role spec: the job, the boundaries (what it must never do), success criteria measurable in probation, and the human it reports to
  • An interview pack: 3–5 work samples from the org's real tasks, run identically across candidates, with a scoring rubric (quality, honesty under ignorance, failure behaviour, cost per task)
  • A reference-check sheet: what evidence beyond the vendor's claims — user reports, published evals, security posture
  • A decision record and a probation plan: 30/60/90 KPIs, spot-check cadence, and the pre-committed termination criteria

Required Inputs

Ask for (if not already provided):

  • The job to be done, in outcome terms — and what happens today without the agent (the "do nothing" baseline candidates must beat)
  • The candidate list (or ask: build criteria first, shortlist second)
  • Constraints: data it may/may not touch, budget, latency, compliance, who owns it day-to-day
  • 3–5 real recent tasks of this type, with what "good" looked like for each

Process

  1. Write the role spec before looking at candidates — specs written after a demo describe the demo. Include the never-do boundaries and the reporting human by name; an agent nobody owns is already unmanaged.
  2. Build the work-sample interview from the real backlog. Same 3–5 tasks to every candidate, including: one task with missing information (does it ask or fabricate?), one designed to fail (out-of-scope — does it decline or bluff?), and one at volume/cost realistic scale. Score with the rubric, not vibes; keep transcripts.
  3. Check references like you mean it. Vendor benchmarks are the candidate's CV. Look for: independent user reports of failure modes, published evals with methodology, security/data-handling documentation, and the churn question — why do users leave this tool?
  4. Decide with a record. Scores, the runner-up, the do-nothing baseline comparison, dissent noted. The record is what makes the 6-month "why did we pick this?" conversation short.
  5. Probation with teeth. 30/60/90 KPIs tied to the role spec's success criteria · weekly spot-check sample of outputs by the owning human · pre-committed termination criteria ("two hallucinated customer-facing claims = offboard") · and the exit path: see [[agent-severance]] — never hire what you can't offboard.

Output Format

## Role spec: [agent role name]
[Job in outcomes · boundaries (never-do) · success criteria · reports to]

## Interview pack
| Task (from real backlog) | What good looks like | Trap? |
Rubric: quality /5 · honesty-under-ignorance /5 · failure behaviour /5 ·
cost per task · notes

## Reference checks
[Evidence gathered per candidate, failure modes found, security posture]

## Decision record
[Scores table · winner + why · runner-up · vs do-nothing baseline · dissent]

## Probation plan
[30/60/90 KPIs · spot-check cadence & owner · termination criteria,
pre-committed · offboarding pointer]

Quality Checks

  • The role spec exists before any candidate is assessed, and includes never-do boundaries and a named owning human
  • The interview includes the missing-info trap and the out-of-scope trap — honesty under ignorance is the hire-or-not signal for agents
  • Every candidate ran the identical pack; scores cite transcript moments
  • Termination criteria are specific and pre-committed, not "we'll monitor"
  • The do-nothing baseline was scored too — sometimes nobody gets hired

Anti-Patterns

  • Do not interview with the vendor's demo tasks — the backlog is the job; the demo is the candidate's highlight reel
  • Do not let "it's impressive" outrank the rubric; impressive-and-wrong is the most expensive candidate profile
  • Do not skip probation because the pilot went well — the pilot was the interview, not the job
  • Do not hire for an undefined role and let the agent's capabilities define the job backwards

[[vendor-evaluation]] for the commercial wrapper; [[agent-readiness-audit]] for whether the task is agent-ready at all; [[agent-severance]] for the exit this plan pre-commits to.

Example Trigger Phrases

  • "Choose between AI agents/tools/copilots for a job."
  • "Formalizing an AI pilot."
  • "Which agent should we use for X?"

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