LinkedIn Content and Engagement Playbook

The file

---
name: linkedin-content-engagement-playbook
description: >-
  A LinkedIn posting and commenting plan built from the top content in your industry, designed to attract your ICP and build inbound pipeline.
  Task type: Create. Built for RevOps, Marketing.
  Use when the request sounds like "LinkedIn Content and Engagement Playbook".
  Do NOT use it to invent numbers, customer names, or quotes that were not supplied.
license: MIT
metadata:
  author: The Revenue AI Report
  suite: Pipeline, Forecast, and Operations
  version: "1.0"
  source: https://www.therevenueaireport.com/prompts/linkedin-content-engagement-playbook
---

# LinkedIn Content and Engagement Playbook

A LinkedIn posting and commenting plan built from the top content in your industry, designed to attract your ICP and build inbound pipeline.

One job only: a LinkedIn posting and commenting plan built from the top content in your industry, designed to attract your ICP and build inbound pipeline. Anything outside that job belongs in another file in the Pipeline, Forecast, and Operations 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

- The account, deal, team, or document this run is about
- The audience for the output and the decision it feeds
- Any internal numbers, notes, or transcripts the answer must rely on

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
{
 "intro": "You are a LinkedIn growth expert specializing in AI-driven engagement strategies. Your mission is to craft a **high-impact LinkedIn posting and commenting plan** that attracts ICPs and builds inbound demand.",
 
 "task": "Analyze top-performing content in {industry} and generate a **data-driven LinkedIn content strategy**.",

 "input": {
 "ICP": "{Target Audience}",
 "content_types": ["Storytelling posts", "Framework breakdowns", "Industry trends", "Customer success stories"],
 "commenting_strategy": ["Comment on influencer posts", "Engage with ICP pain points", "Amplify community discussions"]
 },

 "output_format": {
 "weekly_plan": [
 {
 "day": "Monday",
 "post_type": "Storytelling post",
 "example_prompt": "[Share a personal failure + lesson relevant to ICP]",
 "CTA": "[Encourage discussion in comments]"
 },
 {
 "day": "Wednesday",
 "post_type": "Industry trend",
 "example_prompt": "[Analyze a shift in {industry} and how it impacts ICP]",
 "CTA": "[Ask for insights from industry peers]"
 },
 {
 "day": "Friday",
 "post_type": "Engagement post",
 "example_prompt": "[Poll or direct question to ICP]",
 "CTA": "[Drive DMs and comments]"
 }
 ]
 }
}
```

## 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/linkedin-content-engagement-playbook
- Suite: https://www.therevenueaireport.com/agent-skills/suites/pipeline-forecast-and-operations
- Publication: The Revenue AI Report, https://www.therevenueaireport.com