14 AI agent prompts for HR & People

Copy-paste, beginner-friendly prompts to help you onboard new hires, answer policy questions and write job posts. Each one is based on a real-world example and works with ChatGPT, Claude or an AI agent. 13 are free below, and 1 more is in AgentPlaybook Pro.

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Free HR & People prompts

New hire onboarding plan

Create a 30-60-90 day plan and welcome pack.

Create an onboarding plan for a new [role] at my [company type]: first-day checklist, week-1 schedule, 30-60-90 day goals, and a welcome email. Tools they'll use: [list].

Job description + screening questions

Write a clear job post and fair screening questions.

Write a job description for [role] at [company]: summary, responsibilities, must-haves, nice-to-haves, salary range [x], and 5 screening questions with what a good answer looks like.

Employee feedback analyzer

Find themes in survey comments.

Here are anonymous employee survey comments: [paste]. Group them into themes, estimate sentiment for each, quote representative comments, and suggest 3 actions leadership could take.

Pre-payroll missing data check

Catch missing or odd pay data before you run payroll, an idea Workday is building into its payroll agent.

Here's this pay period's payroll data: [paste or attach]. Here's last period's for comparison: [paste]. Flag anyone with missing hours, rates, bank details, or tax info, plus any pay that changed by more than [X]% from last time. Put it in a table with a suggested question to ask for each.

Open roles at-risk watch

Check every open job and flag the ones running short on candidates or likely to miss their fill date, like Workday's always-on recruiting agent.

Here are my open roles with posting date, target fill date, and candidates at each stage: [paste]. Flag roles at risk of missing their date and explain why. For each, suggest 2 actions (for example, widen the search, reword the post, or follow up with stalled candidates) and draft the post edit or outreach message.

AI-fluency interview plan

Build a structured, hands-on interview that scores how candidates frame problems for AI, steer it and judge its output, modeled on the AI Fluency evaluation in HackerRank's Chakra AI interviewer.

Here is the job description for [role]: [paste]. Create a structured interview plan for [45/60] minutes with: sections and timings; one realistic hands-on task where the candidate can use AI tools, based on our real work: [describe]; sample questions and follow-up probes for each section; and a 1-4 scoring rubric for three AI-fluency skills: Framing (how well they define the problem and give the AI context), Steering (how they guide, refine and redirect the AI), and Judging (how they review and validate what the AI produces). Show what a strong vs weak answer looks like, and flag anything in the plan that's too long or could be unfair to candidates.

Talent market intelligence for a role

Turn a job description into a hiring-manager brief with comparable roles, pay ranges, and talent-pool notes across locations, like the ChatGPT Work tool Oracle's talent acquisition team built that cut research from 2–4 days to about 15–20 minutes.

Build a talent market intelligence brief for this role. Job description: [paste]. Target locations: [list]. Our pay band if known: [paste or say unknown]. Research comparable titles, typical experience, and public compensation ranges for each location. Assess how deep the talent pool looks (scarce / moderate / plentiful) and name 3–5 competitor or peer employers who hire similar roles. Finish with 5 sharp questions I should ask the hiring manager in the intake meeting. Cite the kind of public source you'd use for each pay figure (job boards, salary sites, government data). Mark anything you're unsure about; don't invent exact salaries.

Screen candidates with a voice agent first

Let candidates take an on-demand first-round voice screen so recruiters stop waiting weeks for calendars—like WebFX, where about 1 in 3 hires started with a Fireflies Voice Agent screen and roughly 200 recruiter hours were saved across ~300 screenings.

You are a first-round screening voice agent for [role]. Must-ask questions and knock-out criteria: [paste]. Scorecard dimensions: [e.g. role fit, communication, experience depth, culture signals]. After the candidate answers, produce: a 5-bullet summary, scores 1–5 per dimension with evidence quotes, stack-rank vs other candidates in this batch [paste prior scores if any], and a recommend (advance / maybe / reject) for a human recruiter. Never invent resume facts. Never make an offer, discuss compensation bands beyond [public range], or reject legally protected characteristics. Save only as a draft scorecard until a recruiter reviews.

Prep payroll in chat and only approve the exceptions

Have an AI pull hours, tips, bonuses and time off together, flag anything blocking the payroll run, and wait for you to approve. Gusto's plugin for ChatGPT does this on your live payroll data, with every payroll-affecting action requiring your confirmation.

Help me prepare this pay period's payroll before I submit it. Pay period: [dates]. Employees and rates: [list]. Hours and tips: [paste spreadsheet rows]. Bonuses, reimbursements or one-time deductions: [list]. Time off taken: [list]. Check everything and give me: (1) a summary table per employee with gross pay, (2) a list of anything that looks wrong or missing (overtime, missing hours, PTO over balance, new hires without details), (3) total labor cost compared with [budget], and (4) the questions I need to answer before running payroll. Do not submit anything; I approve payroll myself.

Run an employee offboarding request across IT, HR and facilities

Let an employee service agent take an offboarding request from Slack or Teams and coordinate every step: access removal approvals, laptop return, badge deactivation and paperwork such as an employment verification letter. Zendesk's new Employee Service AI Agents with Advanced Action Flows automate these multi-step requests, with early-access customers saving an estimated 500,000 hours.

You are an employee service assistant helping me offboard someone. Employee: [name, role, team, manager]. Last day: [date]. Systems and apps they use: [list]. Company equipment: [laptop, phone, badge, other]. Our offboarding policy: [paste steps/approvals]. Step 1: build a checklist grouped by team (IT, HR, facilities, manager) with an owner and a due date for each item, based only on our policy. Step 2: list which access removals need approval and from whom. Step 3: draft short messages to each owner asking them to complete their item. Step 4: draft the employee's farewell/next-steps note (equipment return, final pay questions to HR, any letter they requested). Do not remove access or send anything yourself, and mark any step not covered by the policy as 'confirm with HR'.

Adapt your training guides for each country and language you work in, not just translate them word for word

When a business grows into new cities or countries, its training guides, onboarding decks and how-to sheets often stay in one language with examples that don't fit local rules, money or habits, so new staff learn slower and make mistakes. An AI agent can rewrite each guide for a specific market: translate it, swap in local examples, flag rules that differ, and keep the format the same. TechCrunch reported on Oct 22, 2026 that Uber is rolling ChatGPT Work out to all of its roughly 30,000 employees after a 1,000-person pilot in which about 80% of users were non-engineers, and that pilot users used it for tasks including localizing training materials for different markets and languages, analyzing operations data to spot trends, and building personal dashboards.

Here is a training document we use: [paste or attach the guide, checklist or slide text]. It was written for [original country/market] in [language]. Please adapt it for [new country/market] in [target language]. Local details I know: [currency, units, laws or rules that differ, local holidays, job titles, anything else]. 1) Translate it in plain, friendly language a new hire would understand, keeping the same headings and order. 2) Replace examples, names, prices and units with local equivalents and list every change you made in a table (original, new, why). 3) Flag any step that may conflict with local laws or customs, and mark it 'check with a local manager' instead of rewriting it. 4) List any words or brand terms you kept in the original language on purpose. 5) Add a short 5-question quiz at the end in the target language to check understanding. Only use the local details I gave you; don't guess at local laws or rules.

Find past job applicants who fit the role you're hiring for now and invite them back

Most businesses already have strong runner-up candidates sitting in old applications, but nobody has time to dig through them when a new role opens. An AI assistant can compare your new job description against past applicants, shortlist the best matches with reasons, and draft a friendly re-engagement message for you to review. Paylocity launched Recruiting Optimizer on Oct 22, 2026, including a Talent Rediscovery Agent that identifies previous applicants who match current hiring needs, with recruiters setting the criteria and reviewing every output.

Help me rediscover past applicants for a role I'm hiring for now. New role: [paste the job description, location, pay range and must-have skills]. Past applicants: [paste or upload a list or spreadsheet with name, role applied for, date, key skills, notes from earlier interviews, and why they weren't hired]. Do this: 1) List the must-haves and nice-to-haves from the new role. 2) Score each past applicant against them (strong / possible / no fit) and give a one-line reason based only on what's in their record. 3) Skip anyone marked "do not contact" or who was let go for conduct. 4) Show the top [5-10] as a table: name, original role, date applied, fit score, reason, anything to check. 5) For each strong match, draft a short, warm message that mentions their earlier application and invites them to apply or chat. Don't guess at skills that aren't in the record, and don't use age, gender, race or other protected traits in any ranking.

Before you hand your service business to AI agents, write down how you actually do the work: turn your know-how into step-by-step workflow docs each agent follows, and review every output at first

Agents can only run a business the way you do if your know-how is written down. Emergent reported (Oct 23, 2026) that Sydney recruiter Alicia Keenan rebuilt her firm around four AI agents that find new business, produce market intelligence, nurture candidates and run searches, after spending months writing about 200 workflow documents to capture nearly two decades of expertise. The agents still make mistakes (garbled early reports, candidates shortlisted from the wrong country, emails in the wrong tone), so she checks every interaction and fixes the workflow each time something goes wrong, and expects about 18 months before she can step back from close supervision. This prompt helps you write the first workflow docs and the checks around them.

You are helping me turn my business know-how into workflow documents an AI agent can follow. My business: [e.g. specialist recruitment for finance roles in Sydney]. The jobs I want agents to take on: [e.g. finding new clients, market updates, keeping candidates warm, running a search]. Start with this one job: [name it]. Interview me one question at a time until you understand: the trigger that starts it, every step I take, the tools and data I use, how I decide (with real examples of good and bad cases), the hard rules (e.g. only candidates with the right to work in [country]), the tone of anything sent under my name, and what 'done well' looks like. Then write the workflow doc with: purpose, inputs, numbered steps, decision rules, things the agent must never do, a draft-for-approval point before anything goes to a client or candidate, and a checklist I use to review each output. Finish with a log template where I record each mistake and the rule we add to stop it happening again. Then ask which job to document next.

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