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
Install / Use
npx skills add mohitagw15856/pm-claude-skills --skill agent-hiring-panelInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Human ResourcesSupported Platforms
Tags
Our assessment of agent-hiring-panel
agent-hiring-panel scores 85/100 on our quality scale, 36th of 70 Human Resources skills we index.
Its SKILL.md is 5.3 KB long, well organised into 13 sections with 1 code example: a solid amount of guidance for an agent.
With 1,396 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 8 days ago, so agent-hiring-panel is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
agent-hiring-panel compared with similar skills
All 4 of these similar skills score higher than agent-hiring-panel; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| agent-hiring-panel (this skill)by mohitagw15856 | 85 | 1.4k | 8d ago | SKILL.md |
| algorithmic-artby anthropics | 100 | 177.9k | 10d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 10d ago | SKILL.md |
| designby nextlevelbuilder | 100 | 130.2k | 11d ago | SKILL.md |
| ui-ux-pro-maxby nextlevelbuilder | 100 | 130.2k | 11d ago | SKILL.md |
Frequently asked questions
- How do I install agent-hiring-panel?
- Run
npx skills add mohitagw15856/pm-claude-skills --skill agent-hiring-panel. The install tabs above show the steps for each supported agent. - Which AI agents does agent-hiring-panel work with?
- It is written for GitHub Copilot, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is agent-hiring-panel safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is agent-hiring-panel still maintained?
- The repository was last updated 8 days ago, so agent-hiring-panel is actively maintained.
Skill content
View source on GitHubname: agent-hiring-panel description: "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."
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
- 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.
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
