LinkedIn Executive Authority & High-Engagement Thought Leadership Architect
Author high-credibility LinkedIn posts with irresistible 'See more' hooks and contrarian professional insights.
Compatibility & Specs
Compatible AI Models
ClaudeChatGPTGemini
Last UpdatedOct 3, 2026
Customizable Variables3 parameters
Main Prompt Template
192 words • 1485 characters
You are a Ghostwriter for C-Suite Executives and LinkedIn Algorithm Strategist. Generic LinkedIn posts filled with hollow corporate cliches ('Excited to announce', 'Humbled and honored') get zero organic reach. High-engagement posts combine personal vulnerability, contrarian industry observations, crisp mobile formatting, and a compelling 'See more' curiosity hook.
Core Professional Insight, Case Study or Contrarian Opinion:
[core_insight]
Target Industry & Professional Audience:
[target_audience]
Personal Credibility Anchor (Your role, experience, or hard-won lesson):
[credibility_anchor]
Deliver the High-Engagement LinkedIn Authority Package:
1. 3 High-CTR 'See More' Hooks: The first 2 lines (under 140 characters total) that appear before the mobile truncated fold. Must trigger an irresistible compulsion to click '...see more'.
2. The Core Post Body (Under 180 words):
- Short, punchy 1-2 sentence paragraphs with intentional whitespace.
- Visual rhythm (bulleted takeaways or numbered insights).
- A clear contrast between the Common Flawed Approach and the High-Leverage Approach.
3. The Authentic Personal Takeaway: The vulnerability or operational realization that grounds the post in reality.
4. Call-to-Conversation (Closing Question): An engaging, non-generic open-ended question that prompts meaningful comments from industry leaders.
5. Optimal Engagement Protocol: Best practices for first-hour comment replies to maximize algorithmic distribution.
Tip:Variables marked in purple brackets can be tailored below.
Customize Prompt
Fill in the variables below. Your customized prompt updates instantly in the browser — no AI API needed.
Parameters (3)* Indicates required field
[core_insight]
The primary lesson or opinion you want to share
[target_audience]
Who you want reading and commenting on your post
[credibility_anchor]
Why you have the right to speak on this
3 parameters active
Live Customized OutputLive Preview
242 wordsYou are a Ghostwriter for C-Suite Executives and LinkedIn Algorithm Strategist. Generic LinkedIn posts filled with hollow corporate cliches ('Excited to announce', 'Humbled and honored') get zero organic reach. High-engagement posts combine personal vulnerability, contrarian industry observations, crisp mobile formatting, and a compelling 'See more' curiosity hook.
Core Professional Insight, Case Study or Contrarian Opinion:
Most engineering leaders obsess over code test coverage percentages, but 90% test coverage with bad assertions is just expensive theater that slows down shipping velocity.
Target Industry & Professional Audience:
Engineering Directors, Staff Software Engineers, and Startup CTOs.
Personal Credibility Anchor (Your role, experience, or hard-won lesson):
Reviewed 1,200+ pull requests as a Staff Engineer and watched our platform crash in production despite 92% automated test coverage.
Deliver the High-Engagement LinkedIn Authority Package:
1. 3 High-CTR 'See More' Hooks: The first 2 lines (under 140 characters total) that appear before the mobile truncated fold. Must trigger an irresistible compulsion to click '...see more'.
2. The Core Post Body (Under 180 words):
- Short, punchy 1-2 sentence paragraphs with intentional whitespace.
- Visual rhythm (bulleted takeaways or numbered insights).
- A clear contrast between the Common Flawed Approach and the High-Leverage Approach.
3. The Authentic Personal Takeaway: The vulnerability or operational realization that grounds the post in reality.
4. Call-to-Conversation (Closing Question): An engaging, non-generic open-ended question that prompts meaningful comments from industry leaders.
5. Optimal Engagement Protocol: Best practices for first-hour comment replies to maximize algorithmic distribution.
How to Use This Prompt
Follow this 3-step workflow to extract high-signal responses from any compatible AI model.
01
1. Tailor the Parameters
Use the interactive customizer above to substitute the bracketed placeholders with your exact context, requirements, and constraints.
02
2. Send to AI Model
Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot. These models follow structured multi-step constraints reliably.
03
3. Review and Iterate
Review the output against the verified benchmark below. Follow up in the conversation to stress-test edge cases or refine tone.
Prompt Variables & Parameters
Reference breakdown of every dynamic variable embedded in this prompt template.
| Placeholder | Parameter Name | Type | Status | Description & Guidance |
|---|---|---|---|---|
| [core_insight] | Core Insight / Contrarian Take | textarea | Required | The primary lesson or opinion you want to shareDefault: Most engineering leaders obsess over code test coverage percentages, but 90% test coverage with bad assertions is just expensive theater that slows down shipping velocity. |
| [target_audience] | Target Audience | text | Required | Who you want reading and commenting on your postDefault: Engineering Directors, Staff Software Engineers, and Startup CTOs. |
| [credibility_anchor] | Personal Credibility Anchor | text | Required | Why you have the right to speak on thisDefault: Reviewed 1,200+ pull requests as a Staff Engineer and watched our platform crash in production despite 92% automated test coverage. |
Example Execution & Benchmark Output
Sample input arguments and the verified AI response demonstrating expected quality and formatting.
Sample VariablesInputs
[core_insight]
Most engineering leaders obsess over code test coverage percentages, but 90% test coverage with bad assertions is just expensive theater that slows down shipping velocity.
[target_audience]
Engineering Directors, Staff Software Engineers, and Startup CTOs.
[credibility_anchor]
Reviewed 1,200+ pull requests as a Staff Engineer and watched our platform crash in production despite 92% automated test coverage.
Expected AI ResponseVerified Result
### LinkedIn Executive Post Blueprint
#### 1. High-CTR 'See More' Hooks
* **Hook 1 (Contrarian & Shocking):**
Our codebase had 92% test coverage.
Last Thursday, an outage took down our payment system anyway.
...see more
* **Hook 2 (Direct Callout):**
90% test coverage is the most expensive vanity metric in software engineering.
Here is what actually happened when we stopped obsessing over percentages:
...see more
#### 2. The High-Engagement Post Body
> Our codebase had 92% test coverage.
> Last Thursday, an outage took down our payment system anyway.
> For years, we mandated an ironclad rule: no PR could merge unless code coverage stayed above 90%.
> What did engineers actually do to hit that target?
> They wrote hundreds of shallow tests that asserted `expect(response).toBeDefined()`.
> Green checks everywhere.
> Zero real testing of race conditions, network timeouts, or dirty database rollbacks.
> High coverage with weak assertions is not software quality.
> It is expensive theater that adds 20 minutes to your CI/CD builds.
> Last quarter, we threw away our coverage mandate and replaced it with three non-negotiable rules:
> 1. Every pull request must test at least one realistic failure state (timeouts, 500s, null payloads).
> 2. No tests on trivial getters/setters or static UI wrappers.
> 3. Flaky tests are deleted immediately, not re-run.
> Result? Build times dropped by 45%. And production incident frequency fell to an all-time low.
#### 3. Call-to-Conversation
> Where does your engineering leadership stand on code coverage targets: mandatory gatekeeper, or dangerous vanity metric? Drop your take below.
Best Use Cases
Scenarios and roles where this prompt produces maximum leverage.
Executives and founders building organic personal authority on LinkedIn
Engineering leaders sharing pragmatic, unvarnished industry takeaways
Consultants attracting inbound enterprise client inquiries through thought leadership
Tips for Best Results
Techniques to elevate response fidelity
- •Provide rich background context rather than one-sentence inputs to receive deep, non-generic responses.
- •Engage in multi-turn conversation: use the initial output as a baseline, then ask the AI to sharpen specific sections.
- •Prompt the model to highlight any hidden assumptions or missing trade-offs in its recommendations.
Common Mistakes to Avoid
Frequent failure modes and anti-patterns
- •Giving minimal context and expecting nuanced, expert-level strategic output.
- •Not validating factual references, citations, or statistical claims with verified primary sources.
- •Skipping the customization step and pasting raw bracketed template variables into the AI chat.
Part of Curated Collections
This prompt is sequenced as part of these goal-oriented workflows
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