Agent Hiring Panel Skill
SkillProductivityHire 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Agent Hiring Panel Skill skill
What this skill tells your AI
The instructions your AI receives, as published by mohitagw15856/pm-claude-skills in skills/agent-hiring-panel/SKILL.md and read by ahel’s review.
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
- 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.
- 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.
- 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?
- 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.
- 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
Related
[[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.
Signals
- GitHub stars
- 1k
- Forks
- 239
- Last commit
- Sep 2026
Advanced
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- skill
- Gateway key
agent-hiring-panel- Source
- github.com/mohitagw15856/pm-claude-skills