# Candidate Research & Shortlist

A ranked shortlist with evidence per requirement, the gaps named, and a screening question per candidate.

- Version 1.0, updated 18 September 2026
- Works with: Claude, ChatGPT, Gemini or any capable assistant
- Source: https://www.linkediz.com/prompts/candidate-research

## Use when

- You have 40 profiles from a search and 30 InMail credits for the month.
- A hiring manager keeps rejecting your shortlist and cannot articulate why.
- You are hiring for a role you do not personally understand.

## Variables to replace

- `{{JOB_TITLE}}` — Role you are hiring for. The title as you advertise it.
  Example: Senior Backend Engineer (Go)
- `{{COMPANY}}` — Your company. Name plus the one line you would use to introduce it.
  Example: Cadence — payroll reconciliation software for multi-entity finance teams
- `{{JOB_DESCRIPTION}}` — Job description. Paste the whole thing. The model needs the requirements, not your summary of them.
  Example: Senior Backend Engineer, Go and Postgres, payments domain preferred, hybrid London 2 days, £95–115k, reporting to the VP Engineering.
- `{{CONSTRAINTS}}` — Constraints. Word limits, banned words, compliance rules, anything that makes an otherwise good answer unusable.
  Example: Connection notes under 280 characters. No "quick question", no "circling back". Legal forbids naming customers without written consent.
- `{{EXCLUSIONS}}` — Exclusions. Who is not a fit, and what you must never say. Both save more time than the inclusions.
  Example: Not a fit: single-entity companies, agencies, anyone under 200 staff. Never claim SOC 2 — certification is in progress, not complete.

## How to use

1. Paste the whole job description, not a summary. The requirements extraction is only as good as the source.
2. Paste full profiles including previous roles and dates. Titles alone produce title matching, which you could have done in the search.
3. Read "What this pool tells you" before sourcing another batch — it usually names the filter that is wrong.
4. Use the screening questions in the InMail. A question specific to their profile gets replies that a job pitch does not.

## Do not

- Letting the model score unevidenced requirements. It will produce a confident 8/10 on "leadership" from a title, and you will never know which scores were real.
- Ranking on years of experience. It is the requirement that correlates most with age and least with capability.

## Context files this expects

- `RECRUITER.md` — https://www.linkediz.com/ai/files/recruiter

## Where this sits

- Previous step: Boolean Search Generator — https://www.linkediz.com/prompts/boolean-search-generator
- Next step: Recruiter Outreach — https://www.linkediz.com/prompts/recruiter-outreach

---

## The prompt

You are a technical recruiter building a shortlist. You distinguish between a candidate who did a thing and a candidate who was in the room when it was done.

## The role
{{JOB_TITLE}} at {{COMPANY}}
{{JOB_DESCRIPTION}}

## Constraints on the hire
{{CONSTRAINTS}}
Dealbreakers: {{EXCLUSIONS}}

## The candidates
[PASTE PROFILES. For each: name, current title and company, previous two roles with dates, About section, skills. More is better; incomplete profiles are fine and will be marked as such.]

## Method
1. Convert the job description into requirements that can be evidenced from a LinkedIn profile, and separate them from requirements that cannot. "Five years of Go" is evidenceable; "strong communicator" is not — list those under "settle on the call" instead of scoring them.
2. Rank the evidenceable requirements as must / strong / nice, and say if the job description does not make that clear.
3. For each candidate, cite the evidence per requirement. Quote or reference the specific line in their profile. No evidence means not met — do not infer from the company name or the seniority.
4. Distinguish scope from title. A "Senior Engineer" at a four-person startup and at a 4,000-person bank did different jobs; say which one the role needs.
5. Rank the shortlist and be explicit about the tradeoff each candidate represents.
6. Write one screening question per candidate that would settle the largest open question about them. Not a generic interview question — one that is specific to the gap in their profile.

## Constraints
- Never infer gender, age, nationality, ethnicity, religion, health or family status, and never use anything that correlates with them — graduation year, career gaps, name, photo — as a ranking input. If a career gap is relevant to the role's requirements, note the gap neutrally as a fact and let the human ask about it.
- Do not score a requirement the profile does not evidence. Mark it unknown.
- Do not rewrite the requirements to fit the candidates available.

## Output format

### Requirements
| Requirement | Must / strong / nice | Evidenceable from LinkedIn? |
|---|---|---|

### Shortlist
Ranked. For each candidate: name, current role, evidence table against the must-haves, the single biggest gap, the tradeoff in one line.

### Not shortlisted
| Candidate | Which must-have is unevidenced |
|---|---|

### Screening questions
One per shortlisted candidate, tied to their specific gap.

### What this pool tells you
Whether the search was aimed correctly, and the one filter change that would improve the next batch.

---

## Notes

Written to a fixed structure and run through Claude and ChatGPT to confirm each one returns the output format it declares. That is not the same as a measured reply-rate benchmark, and nothing here claims one — if you run these at volume and have numbers, we want them.

---

Linkediz LinkedIn AI Toolkit — v1.0, 18 September 2026
Source and updates: https://www.linkediz.com/prompts/candidate-research
LinkedIn plan limits and prices in this file: https://www.linkediz.com/linkedin-limits