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Agent Hiring Panel

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mohitagw15856
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.

Overview

Publishermohitagw15856
Repositorypm-claude-skills
Skill nameagent-hiring-panel
Stars
1.4K
Forks
240
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mohitagw15856 on GitHub. Read the source before you install it.

Installation

Install the Agent Hiring Panel AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills.git /tmp/pm-claude-skills
mkdir -p .claude/skills
cp -r /tmp/pm-claude-skills/exports/openclaw/agent-hiring-panel .claude/skills/agent-hiring-panel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Hiring Panel in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Hiring Panel on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Hiring Panel is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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.

Frequently asked questions

What does the Agent Hiring Panel AI skill do?

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.

Why use Agent Hiring Panel on TypingMind?

Because you install it once and use it with any model. Agent Hiring Panel is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Hiring Panel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mohitagw15856/pm-claude-skills/tree/main/exports/openclaw/agent-hiring-panel. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Hiring Panel?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Hiring Panel?

As many as you like. As long as a model supports skills, you can use Agent Hiring Panel with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Hiring Panel AI skill free?

Yes. It is published on GitHub by mohitagw15856 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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