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Recruiting is a context problem. Every hiring conversation is data — the richest, most honest signal your organisation has about the talent market, your candidates, and your own process. This library contains customizable prompts created by the Metaview team for use across the Metaview platform. They are examples designed to inspire and guide you. You remain responsible for how you configure Metaview’s AI features, the prompts you use, the conversations you apply them to, and any decisions you make based on the outputs.
Placeholders. Anywhere you see [Company], [role], [your name] or {COMPANY_NAME}, swap in your own details before running. Where a prompt references an attached document, upload your JD, competency framework or scorecard alongside the transcript.
These prompts examples produce drafts for you to review. Notetaker captures and organises what was said in a conversation; it does not evaluate candidates, infer traits, or make hiring decisions. Every prompt below is designed to surface evidence — you decide what that evidence means. See Best practices for Notes before rolling any of these out to your team.

What’s inside


The prompting guide

A prompt is a written instruction that tells the AI what to extract from your transcript, which sources to use, and how to format the result. Think of it as briefing a very fast, very literal junior teammate — the more precise you are, the better the output.

The six core prompting principles

1

Be specific

Ask how, what, and with what result — not yes/no questions. The AI defaults to the broadest interpretation, so specifics reduce the chance of it filling gaps.
2

Assign a role

Tell the AI who it is — e.g. “Act as a senior technical recruiter.” It frames everything that follows.
3

Specify your sources

Metaview can pull context from several places, so be explicit — e.g. “Refer to both the transcript and the job description.”
4

Add your guardrails

Be clear on what it should not do — e.g. “Do not infer or speculate — only return what was explicitly mentioned in the transcript.” Always define what to return when a topic didn’t come up (N/A, Not discussed, or a blank cell).
This is the highest-leverage line in any prompt. A missing fallback is what turns silence into a false negative.
5

Define the output

Spell out the format — bullets vs prose, exact labels, and whether to include direct quotes.
6

Test before sharing

Run a template on 2–3 real interviews and spot-check the citations before rolling it out widely.

The prompt builder

Writing a prompt from scratch? Fill in these five lines and you’ll have a solid prompt every time.
Notice what the Goal and Task lines don’t say. They ask the AI to capture and quote, not to judge whether the candidate “aligns”. Alignment is your call — the prompt’s job is to put the evidence in front of you.

1. Notetaker Templates

Notetaker templates turn any interview, intake or debrief call into a structured summary. Build one once, share it with the team, and reuse it across every conversation of that type.
Templates marked with * are designed for use with multi-source.Formatting your output. Templates render in Markdown, so you control how notes look: **bold** for titles, tools and company names; *italic* for skill or project types; <u>underline</u> to flag blockers; and - for bullet lists.
Keep section headings factual and focused on what was said. Avoid fields that ask the AI to infer personal traits, emotions or “fit” — see best practice guidelines for custom templates.

1.1 — Build a template with AI (meta-prompt)

What it’s for: Have any LLM draft a Metaview template from a plain-English description.

1.2 — Role kick-off / intake call (HM call)

What it’s for: Capture every detail from a hiring manager intake so the search starts aligned. Each block below is a separate template section.

1.3 — Client kick-off call (search firm)

What it’s for: A thorough intake one-pager for agency and search teams.
There’s no Soft Skills category here on purpose. Recording a soft-skill requirement is fine when the hiring manager stated one, but a category by that name invites the model to characterise people rather than capture requirements. If your process needs it, phrase it as a stated requirement — e.g. “Communication requirements the hiring manager explicitly described”.

1.4 — Recruiter screen (enriched with JD) *

What it’s for: A generalist template for recruiter screening calls that maps the conversation against an attached JD. Attach the JD before running.
Keep these fields about what the candidate said, not how they said it. Asking for “enthusiasm”, “clarity” or “personal alignment” pushes the model into reading tone and character instead of recording statements.
Note that the second section forces a distinction between “they said they haven’t done this” and “nobody asked”. Keep it. Treating an unasked question as a missing skill is the most common way a coverage gap turns into an unfair conclusion about a person — and it’s why this section isn’t called “misalignment”.
Resist adding a “red flags” field here, or asking the model to note “clarity” and “confidence”. Logistics are facts worth capturing; whether any of them is a concern is a judgment, and it belongs to you.

1.5 — Volume hiring *

What it’s for: At volume, map each candidate against the required skills in your job spec so you can see who to review first. Add all conversations, plus the JD.
A percentage of skills discussed in an interview measures what got covered, not candidate quality — two interviewers asking different questions will produce different percentages for identical candidates. Use this to decide who to read about next, never who to reject.

1.6 — Compare candidates against a JD (enriched with JD) *

What it’s for: Lay out what each candidate said about each requirement in the JD, side by side, as preparation for a human debrief. Attach the JD.
This prompt does not pick a winner. It produces an evidence matrix — what each candidate said about each requirement — and stops there. Asking the AI which candidate is strongest, or for a “close second”, is the AI ranking people: Best practices for Notes rules that out, and the Interview Debrief skill refuses it too. How to weigh the evidence is the hiring team’s decision.

1.7 — Pipeline debrief / wash-ups

What it’s for: Let Metaview sit in on your debrief with the hiring team and turn the discussion into structured notes per candidate — capturing what the interviewers said, not adding a view of its own.
Use a repeating section, one per candidate discussed.
Every heading here asks what the interviewers said, not what the AI concludes. That distinction matters most on team fit: “What did the interviewers say about how the candidate would work with the team?” is a debrief record. “Will the candidate be a good fit?” asks the model for a fit judgment about a person.

1.8 — Brief interviewers on areas to probe further *

What it’s for: Turn one interview into a brief for the next, flagging what the following interviewer should explore. Add multiple interviews per candidate, or multiple candidates per role.
Use a repeating section, one per candidate.
This covers requirements and coverage, not “mindset” or “traits” — those are inferences about a person rather than gaps in the conversation. It also separates wasn’t asked from answered thinly, which lead to different follow-ups.

1.9 — Role insights from HM interviews *

What it’s for: Learn how hiring managers sell the role and answer candidate questions, so you can pitch it better yourself. Add multiple interviews via multi-source.

1.10 — Check interview coverage consistency *

What it’s for: See which topics an interviewer covers in every interview and which only come up sometimes. Use multi-source to add multiple interviews to compare.
This analyses your own team. Output describes coverage patterns across interviews — it is not a performance rating, and it shouldn’t feed appraisals or ranking. Before using it:
  • Keep individual output private to the person being coached.
  • Keep team views aggregated — no league tables of named interviewers.
  • Confirm your position on analysing employee performance first; works councils, employment law and internal policy may all apply.
The Interviewer Coaching skill applies the same principles with the governance built in.
This measures which topics came up across a set of interviews. Avoid framing it as interviewer behaviour — the output describes coverage, not conduct.

1.11 — Offer insights

What it’s for: Pull out what a candidate said they care about most, so you can build an offer that speaks to it. Add every conversation you’ve had with them.

2. Sourcing

Great sourcing starts with a sharp brief. These prompts help you tell the Sourcing agent exactly who you’re looking for — must-haves, signals, and the near-misses to avoid — so results come back matched to your criteria rather than to a keyword.
Matches reflect alignment with the criteria you set, not a judgment about candidate quality. You decide who to review and who to contact — see Best practices for Sourcing.

2.1 — Find candidates matching your criteria

Two things to keep as they are. Don’t put work authorisation in the exclusions — it isn’t in profile data, so the agent would infer it, and inference runs through nationality and place-of-education proxies. Handle sponsorship as a question in a human conversation. And keep “a profile that meets these criteria” phrased against your criteria rather than as a quality label on people.

2.2 — Find companies, not candidates

What it’s for: Flip the search to find target companies — useful for search firms placing a known candidate.

3. Sequences

The best outreach doesn’t read like outreach. Sequences let you go beyond {Company} placeholders: AI prompt blocks written between {{ }} generate candidate-specific content at send time.
Pair these with the Reply-Worthy Outreach guide, which covers the framework and cadence these prompts are built for.

How prompting works in Sequences

There are two kinds of variable you can drop into a sequence step, and they do very different jobs.
Every first-touch sequence needs two fixed elements, and neither should be a prompt block — save them as static text so they can’t be rewritten per candidate:
  • A link to your organisation’s privacy notice, so candidates know how their data is being used and what their rights are.
  • A simple opt-out, honoured promptly.
This is required by Best practices for Sourcing — Sequences lets you save a reusable privacy footer in your template so it’s on every first message automatically.
Prompt blocks draft the message; you approve what goes out. These blocks generate at send time, so review the output across a sample of candidates before you enable a sequence — and keep the copy grounded in the person’s stated experience and public professional work.

3.1 — Subject lines that get opened

What it’s for: Salesy subjects (“Unlimited Growth!!”) get filtered or ignored. Short, lowercase and specific wins.

3.2 — The personalised opener

What it’s for: Replace generic first lines (“I came across your profile…”) with a hook specific enough that it passes the 50-person test. Drop into the first line of your first email step.

3.3 — The personalised value prop

What it’s for: Pitch the right reason to move, based on where the candidate is coming from — instead of the same value prop for everyone. Adapt the if/then angles to your role’s personas.
Tailor to what the profile actually shows — company size, industry, scope of their current work. Writing to an inferred personal “driver” or “pain” is an assumption about someone you haven’t spoken to, and it doesn’t outperform grounding the angle in their real background.

3.4 — The light ask

What it’s for: Replace heavy closers (booking links, “15–30 min call to discuss the opportunity”) with one low-friction question. Drop into the last line of your first email step.

3.5 — The follow-up that adds something new

What it’s for: Follow-ups that say “just bumping this” get ignored. Each follow-up step should introduce one new reason to reply.

3.6 — The break-up email

What it’s for: A graceful final step that often gets the highest reply rate in the sequence. Low pressure, leaves the door open.

3.7 — Build the entire email with one prompt block

What it’s for: Instead of writing copy with prompt blocks sprinkled in, let one instruction block generate the whole body. Best once you’ve calibrated the shorter blocks above.
Make it yours before you run it. This is an example scaffold, not a finished prompt. Rewrite the opener the way you’d actually introduce yourself, swap the example CTAs for something you’d say out loud, and adjust the rules to match your voice.

4. Deep Research

Before you kick off a search, you need to understand the market. These long-form prompts produce market maps, talent intelligence and account prep — the homework that makes every downstream conversation sharper.
Market numbers are estimates. These prompts are written to label what is observed, reported and inferred, and to say what they could not verify. Keep that framing when you pass the output on.

4.1 — Calibration context kickoff

As demoed in the Talent Mapping webinar. What it’s for: Build deep role context to frame your criteria across sourcing, screening and application review. Put this prompt in the sourcing agent, follow its instructions, and save the output into your Metaview knowledge.
Add your context before you run it. This is a template, not a finished prompt. Fill in each of the bracketed sections so it runs for your specific role.
This is the best-governed prompt in the library. “Evidence is input, not authority — I make the final call”, “Missing information is unknown, not evidence”, the input classification scheme, and the explicit stop-and-ask before saving are all patterns worth copying into your own prompts. The only edit made was removing “off-limits populations” from the exclusions list — exclusions should attach to the work, not to groups of people.

4.2 — Build a reverse-map from your current team

As demoed in the Talent Mapping webinar. What it’s for: Map your current team into feeder companies, tenure patterns and career-path shapes, so you can see where the team was actually hired from.
These patterns describe who was hired — not what predicts success. The prompt says so itself, and the output says so too. Treat feeder companies and tenure shapes as a description of your past sourcing, not as criteria that forecast performance.This prompt profiles named employees. Confirm your position on that internally before running it.
Note the discipline rules at the bottom of the prompt, particularly “These patterns describe who was hired, not what predicts success”. Keep them. Feeder companies and tenure shapes describe your past sourcing; they are not criteria that forecast performance.

4.3 — Market map analysis

What it’s for: A defensible, executive-ready market map before active sourcing begins.
Section 8 originally asked for “filters that predict alignment” and “green/yellow/red flags”. Reworded: market-level correlations are fine, but a red/amber/green flag list reads as a screening rule about people.

4.4 — Interview kit from an intake call

What it’s for: Turn an intake call into market intel, a sourcing shortlist and an interview kit. Paste or attach the intake transcript.
Two changes: ‘Off-limit Companies’ no longer includes “poor culture match” as a reason the AI should infer — it now records only what the hiring manager stated. And the Calibration Check now defines evidence thresholds rather than ‘Great’ vs ‘Mediocre’, which was a label applied to candidates.

4.5 — Company deep-dive (6-part analysis)

What it’s for: Prep for a meeting with a client or buyer — company, market, products, competitors.

5. Reports

One conversation is an anecdote; a hundred are a pattern. Report columns extract the same structured signal across your whole pipeline, so you can filter and spot trends.
To build your own AI column in Reports, click +, select Create with AI and paste your prompt.
Calibrate before you present. Run a new column across 8–12 real conversations and check each value against the transcript before you rely on the aggregate. Empty and “Not discussed” values are correct when the topic genuinely didn’t come up — and a column that reads silence as a negative will quietly skew every number built on it. The Interview Intelligence skill walks through this loop in full.

5.1 — Compensation as stated (current)

What it’s for: Capture what the candidate said they currently earn. Format: Currency
This records a figure the candidate volunteered in conversation. It is not a verified salary, and Metaview has no independent knowledge of what anyone is paid. Treat every value as “what they told us”, and follow your local rules on asking about current pay — pay-history questions are restricted or banned in a number of jurisdictions.

5.2 — Compensation expectations (desired)

What it’s for: Capture the compensation the candidate said they are targeting. Format: Currency
Multi-currency pipelines must be reported per currency, never averaged together. Group by geography before you read any comp aggregate.

5.3 — Reason for leaving (as stated)

What it’s for: Capture the reason the candidate gave for looking for a new role. Format: Single-line text
Two things this prompt deliberately won’t do. It takes stated reasons only and returns Unknown otherwise — an implied reason is an inferred one. And Personal Circumstances returns a single non-specific label rather than naming health or family situations: those are special-category and caring-status data, and they don’t belong in a filterable column about a job candidate even when volunteered.

5.4 — Interest in your company

What it’s for: Capture what the candidate said attracted them to the role or company. Format: List or single-line text
“Explicit or implicit reasons” narrowed to stated reasons, “strongest signal to weakest” changed to the candidate’s own emphasis, and “diversity and inclusion” removed as an example value under Culture / People — inviting a column to record that a candidate mentioned D&I risks capturing a proxy for protected characteristics.

5.5 — Current company

What it’s for: Extract and normalise the candidate’s current employer. Format: Single-line text

5.6 — Talent market signals

What it’s for: Read what candidates say about their current employer, to understand which companies people are leaving and why — a market read drawn from conversations you’re already having.
This classifies what the candidate said about conditions at their employer — the market signal — not how they feel about it. Don’t reframe it as sentiment: inferring a person’s feelings is trait inference, and emotion inference in an employment context is restricted in some jurisdictions.
Format: Single-line text

5.7 — Location extraction columns

Format: Single-line text (city, country) / List (relocation)

5.8 — Interview coverage

What it’s for: Check whether your interviews are covering the competencies your process expects.
This analyses your own team. It counts whether expected questions were asked — it is not a performance rating and shouldn’t feed appraisals, ranking, or compensation. Keep individual output private to the person being coached, keep team views aggregated, and confirm your position on employee-performance analysis before rolling it out. The Interviewer Coaching skill applies these rules by default.
Define your own topics. The example below uses one competency. Amend it to reflect your process and your defined competencies.
Format: Single-line text
Define what each competency actually looks like. Traits like “leadership” or “collaboration” are too vague and leave the model to interpret. Instead of “did the interviewer assess their leadership skills”, spell out the questions that indicate the topic was explored — as above.

5.9 — AI use signal

What it’s for: Surface where it may be worth looking more closely at how answers were produced.
Use with care, and read this before you enable it.This is a signal to help you decide where to look more closely — never a verdict, and never grounds for a decision on its own. Treat any flag as a prompt for a human conversation with the candidate, not a conclusion about them.Be aware of what you are taking on. The signals this column reads — polished phrasing, structured answers, formality — also describe well-prepared candidates, non-native speakers, neurodivergent candidates, and anyone coached on interview technique. A column like this can systematically disadvantage those groups if it is treated as evidence. If you use it, use it to prompt a fair question, log why you looked, and never let it reach a rejection reason.The prompt below requires a verbatim quote alongside any flag, so every signal can be checked against the recording. A label with no evidence attached should be treated as no signal at all.
Format: Single-line text
The top label is “Review”, not “Suspected” — an instruction to a person, not a finding about a candidate. Keep it that way, keep the default-to-Unlikely rule, and keep the requirement for a verbatim quote. A label stating a conclusion, with no evidence attached, in a filterable field, is the version of this that causes harm.

6. MCP

MCP lets Metaview reach beyond a single conversation — pulling your interviews, calendar, tech stack and documents into one place so your AI assistant can work across everything at once.

Already built as a skill

Several MCP workflows now exist as installable AI Skills, with the process and safeguards built in. Start there rather than building from scratch:
You can also use the Metaview MCP connector to build the Report columns in section 5.
The two prompts below don’t have a skill equivalent yet.

6.1 — Get started (open prompt)

What it’s for: Start high-level. This prompt gives you ideas for using the Metaview MCP and walks you through building an analysis from your workspace data — shaping the question, setting scope, and picking an output format.

6.7 — Updated hiring thesis memo

What it’s for: Turn synthesised hiring findings into a single-page thesis memo that mirrors the voice, structure and formatting of an existing thesis you attach, then export it to PDF and check it fits on one page.
A reference memo is required — the prompt needs an existing document to copy voice and structure from.

Use these responsibly

Review before acting. Every prompt here produces a draft. Check it against the source conversation before sharing it or using it in a hiring, outreach or people decision.
Give every prompt a fallback. Not discussed / Not specified / Unknown — so a topic nobody raised never reads as a negative answer about a person.
Prompts that analyse your own team — interview coverage, consistency, pitch quality — should stay private to the person being coached or aggregated at team level. Confirm your organisation’s position on employee-performance analysis before enabling them.
For the full picture, see Best practices for Notes, Best practices for Sourcing, and Best practices for Application Review.