Top 10 LinkedIn Content Topics

The file

---
name: ai-generated-top-10-linkedin-content-topics
description: >-
  Your ten most engaging LinkedIn topics based on likes, comments, and shares, so you know what to post next. Adapted from Daniel Disney.
  Task type: Analyze. Built for Sales.
  Use when the request sounds like "Top 10 LinkedIn Content Topics".
  Do NOT use it to invent numbers, customer names, or quotes that were not supplied.
license: MIT
metadata:
  author: The Revenue AI Report
  suite: Marketing and Demand
  version: "1.0"
  source: https://www.therevenueaireport.com/prompts/ai-generated-top-10-linkedin-content-topics
---

# Top 10 LinkedIn Content Topics

Your ten most engaging LinkedIn topics based on likes, comments, and shares, so you know what to post next. Adapted from Daniel Disney.

One job only: your ten most engaging LinkedIn topics based on likes, comments, and shares, so you know what to post next. Anything outside that job belongs in another file in the Marketing and Demand 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 analytics
- content areas of interest
- analytics request
- content areas request
- top topics

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 analyzing LinkedIn analytics data to identify the 10 most engaging topics for content creation. Here are the steps:

1. First, I will ask you to provide the LinkedIn analytics data you have access to:

<analytics_request>
Please paste or upload the LinkedIn analytics data you have available.
</analytics_request>

{$LINKEDIN_ANALYTICS}

<content_areas_request>
Are there any specific content areas or topics you want to focus on? If so, please list them here:
</content_areas_request>

{$CONTENT_AREAS_OF_INTEREST}

2. To identify the most engaging topics from the LinkedIn data, look at metrics like:
- Number of impressions, clicks, likes, comments, and shares for each post
- Engagement rate (engagements/impressions) for each post
- Follower growth and audience demographics for high-performing posts

Posts and topics with high engagement rates and strong engagement signals (likes, comments, shares) are likely the most engaging for your audience.

3. Analyze the data and list the top 10 most engaging topics you identified, with justification for each selection:

<scratchpad>
[Your analysis and justification for the top 10 topics goes here]
</scratchpad>

4. Finally, write your top 10 most engaging topics inside <top_topics></top_topics> tags, with a brief justification after each one:

<top_topics>
1. [Topic]: [Justification]
2. [Topic]: [Justification]
...
10. [Topic]: [Justification]
</top_topics>

Make sure to thoroughly analyze the provided data and prioritize topics that received the highest engagement according to the metrics discussed. Let me know if you need any clarification on the instructions!
```

## 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-top-10-linkedin-content-topics
- Suite: https://www.therevenueaireport.com/agent-skills/suites/marketing-and-demand
- Publication: The Revenue AI Report, https://www.therevenueaireport.com