LinkedIn Profile Research for Prospects

The file

---
name: ai-generated-linkedin-profile-research-for-prospect-engagement
description: >-
  Five talking points from a prospect's LinkedIn profile, so reps open with something personal and relevant. Adapted from Daniel Disney.
  Task type: Research. Built for Sales.
  Use when the request sounds like "LinkedIn Profile Research for Prospects".
  Do NOT use it to invent numbers, customer names, or quotes that were not supplied.
license: MIT
metadata:
  author: The Revenue AI Report
  suite: Account Research and Targeting
  version: "1.0"
  source: https://www.therevenueaireport.com/prompts/ai-generated-linkedin-profile-research-for-prospect-engagement
---

# LinkedIn Profile Research for Prospects

Five talking points from a prospect's LinkedIn profile, so reps open with something personal and relevant. Adapted from Daniel Disney.

One job only: five talking points from a prospect's LinkedIn profile, so reps open with something personal and relevant. Anything outside that job belongs in another file in the Account Research and Targeting suite.

## When to use this skill

- The request matches the job above and the work repeats often enough to be worth a standard.
- You have, or can ask for, the inputs listed below.
- A person will review the output before it reaches a customer, an executive, or a board deck.

## When not to use it

- The answer needs a number, a benchmark, or a quote nobody supplied. Ask for it instead of producing one.
- The task is a different job in the same suite. Open that file rather than stretching this one.
- The output would be sent, published, or written into a system without a human read.

## Inputs to collect first

- linkedin profile
- request linkedin profile
- inner monologue
- talking point1
- talking point2
- talking point3
- talking point4
- talking point5

Ask for anything missing before running. Do not fill a gap with a plausible guess.

## Process

1. Confirm the job, the audience, and the decision the output feeds.
2. Collect the inputs above. Name anything still missing out loud.
3. Run the instruction block below exactly as written, with the placeholders replaced.
4. Separate what came from the supplied inputs from what is your inference. Label the inference.
5. Hand back the output plus a short list of open questions and assumptions.

## Instruction block

Run this text with the placeholders filled in.

```text
You will be researching a prospect's LinkedIn profile to identify 5 conversation-worthy talking points that could be valuable for engaging with them.

First, ask the user to provide the LinkedIn profile URL or information for the prospect:

<request_linkedin_profile>Please provide the LinkedIn profile URL or information for the prospect you would like me to research.</request_linkedin_profile>

Once the user provides the {$LINKEDIN_PROFILE}, carefully review the profile details, including:
- Work experience and job titles
- Education and credentials 
- Skills and endorsements
- Interests and activities
- Accomplishments and awards

The goal is to identify 5 potential talking points from the profile that could lead to meaningful conversations and demonstrate your knowledge of the prospect's background and interests.

As you review the profile, think through the following in your <inner_monologue></inner_monologue> tags (this will not be shown to the user):
- What stands out about this person's experience and background?
- What potential shared interests or connections can you identify?
- What accomplishments or skills could you compliment them on?
- What open-ended questions could you ask to learn more about their interests/goals?

After reflecting in your inner monologue, provide the 5 conversation-worthy talking points you identified in the following format:

<talking_point1></talking_point1>
<talking_point2></talking_point2>
<talking_point3></talking_point3> 
<talking_point4></talking_point4>
<talking_point5></talking_point5>

Remember, these talking points should:
- Be specific and relevant to the prospect's profile 
- Demonstrate your understanding of their background and interests
- Provide opportunities to compliment them or learn more about them
- Avoid overly personal topics or making assumptions

The goal is to prepare for an engaging conversation that builds rapport by showing your genuine interest in the prospect as an individual.
```

## Decision rules

- Supplied evidence beats inference. If the two disagree, say so and keep the evidence.
- Unknown is a valid answer. Write "unknown" rather than estimating.
- Keep one recommendation per output. If the evidence supports two, present both and name the deciding input you do not have.
- Match the reading level of a working operator. Short sentences. Explain any term the reader may not use daily.

## Quality checks

- Every number, name, date, and quote traces to an input the user supplied.
- No vendor claim is repeated as fact without naming the source.
- Assumptions are listed separately from findings.
- The output answers the original request and nothing else.
- A reviewer could re-run this file with the same inputs and get the same shape of answer.

## Guardrails

- Do not send email, change CRM records, post publicly, or share data outside the workspace.
- Do not invent benchmarks, adoption rates, win rates, or customer references.
- Flag anything that needs a human decision instead of deciding it.

## Source

- Prompt page: https://www.therevenueaireport.com/prompts/ai-generated-linkedin-profile-research-for-prospect-engagement
- Suite: https://www.therevenueaireport.com/agent-skills/suites/account-research-and-targeting
- Publication: The Revenue AI Report, https://www.therevenueaireport.com