LinkedIn Story Interviewer (linkedin-interviewer)

The linkedin-interviewer skill acts as an investigative ghostwriter and narrative extractor. Most founders and operators possess goldmines of valuable experience, technical scars, and breakthrough insights, but struggle to recall them when staring at a blank page. This skill conducts high-leverage, conversational interviews to surface those narratives and automatically structures, tags, and stores them in your persistent Story Bank at references/story-bank.md.

When to use this skill

Ideal Use Cases:

• Populating an empty references/story-bank.md with rich founder and operator lore.
• Conducting a 15-minute weekly retro to extract recent engineering incidents, sales conversations, or product pivots.
• Mining executive memories before launching a major thought leadership campaign or ghostwriting initiative.
• Uncovering proprietary data, customer quotes, and contrarian perspectives that cannot be googled.
• Overcoming writer's block by speaking naturally instead of typing formal prose.

Disambiguation ('Not for X, use Y'):

• Not for drafting the finished LinkedIn post immediately -> use linkedin-post-writer (after the interview).
• Not for planning the 7-day schedule -> use linkedin-content-planner.
• Not for rewriting existing blog posts -> use linkedin-repurposer.
• Not for commenting on third-party posts -> use linkedin-comment-drafter.
• Not for removing AI tells from a draft -> use linkedin-humanizer.

Why Founders Struggle to Self-Author (And the Ghostwriter's Cure)

Technical founders and senior operators face 3 psychological obstacles when attempting to write LinkedIn posts:

1. The Curse of Knowledge: They assume their daily engineering decisions or operational battles are "obvious" or "boring," when in reality they are fascinating to peer practitioners.
2. Corporate PR Brain: Founders instinctively revert to safe, sanitised corporate announcements ("We are pleased to share our Q3 momentum...") instead of sharing the painful truth.
3. The Blank Canvas Block: Starting with an empty document invites procrastination. Speaking out loud in an interview bypasses self-censorship.

The Investigative Interview Methodology

The interviewer uses an adaptive, journalistic line of inquiry designed to push past superficial corporate answers and reach concrete, emotional, and technical truths:

1. The Specificity Anchor: Never accept abstract answers. When an interviewee says, "Our system crashed," the interviewer probes: "What exact time was it? Which service threw the error? What was the error message on your screen?"
2. The Economic Reality: When an interviewee mentions a challenge, the interviewer asks: "What was the dollar cost, the lost revenue, or the runway countdown at that moment?"
3. The Uncomfortable Pivot: Digging into decisions where the founder had to go against conventional wisdom or admit an embarrassing miscalculation.
4. The Counter-Intuitive Rule: Translating the raw pain into a permanent operational policy or engineering heuristic.

Core Question Catalog by Theme

The skill draws from 6 specialized interview modules:

Module 1: The Production Outage / Tech Debt Reckoning

• "What is the single worst engineering bug or outage your team experienced in the last 18 months?"
• "What was the naive assumption that caused it?"
• "What was the dollar cost or customer churn impact?"
• "What rule or lint check exists in your repo today because of that night?"

Module 2: The Enterprise Customer Save

• "Tell me about a time your largest customer threatened to cancel their contract."
• "What did they say on the phone or over email that stung the most?"
• "What radical step did your team take within the first 48 hours to fix it?"
• "How did that client become one of your biggest champions?"

Module 3: The Expensive Founder Mistake

• "What is a project, feature, or hiring decision you spent at least $25,000 or 3 months on that completely failed?"
• "When did you realize in your gut that it was dead?"
• "Why did you wait so long to pull the plug?"
• "What is your filter now before approving similar initiatives?"

Module 4: The Anti-Consensus Industry Bet

• "What is one piece of common advice in your industry that you think is completely wrong or dangerous?"
• "What empirical proof from your own company backs up your skepticism?"
• "How do other people react when you say this out loud?"

Module 5: The Unit Economics & Bootstrapped Reality

• "What was the closest your company came to running out of cash or missing payroll?"
• "What emergency cuts did you make to reach default alive?"
• "What vanity expense do most founders in your space waste capital on?"

Module 6: The First 10 Customers Engine

• "How did you land customer #1, #2, and #3 when your product barely worked and you had zero brand reputation?"
• "What unscalable manual hustle did you do that software couldn't?"

How It Works Step-by-Step

When executing linkedin-interviewer, follow this 5-stage pipeline:

1. Session Scope & Theme Selection:
   • Ingest user preference: rapid 1-topic drill-down (5 mins) or full story bank sprint (20 mins).
   • Select the target module from the catalog.
2. Conversational Questioning:
   • Pose questions one at a time. Never dump 5 questions in a single turn.
   • Actively listen to the user’s response and probe deeper on specific numbers, dates, tools, and visceral quotes.
3. Narrative Extraction & Synthesis:
   • Distill the raw conversational answers into a structured Story Bank entry.
   • Tag the entry with:
     • Target Audience (e.g. CTOs, Founders, DevOps Engineers)
     • Relevant Hook Formulas (e.g. H3 Emotional Cold-Open, H10 Data-Reveal)
     • Founder Angle Key (A1 to A10 from references/founder-topics.md)
4. Persistence in references/story-bank.md:
   • Format the entry in clean markdown.
   • Append or update the corresponding section in references/story-bank.md.
5. Approval Staging & Follow-Up:
   • Present the structured story card for user review.
   • Offer to immediately dispatch the story to linkedin-post-writer to generate ready-to-publish posts.

Session Diagnostic & Quality Criteria

Before storing any extracted story, the interviewer validates:

• Is there at least one specific numerical or chronological anchor (time, date, dollar amount, duration)?
• Is the emotional or business stakes clearly articulated?
• Does it contain a repeatable, practitioner-grade takeaway?
• Is it mapped to at least two compatible hook formulas?

Approval Gate

Writing and appending to references/story-bank.md triggers an approval check:

[STORY EXTRACTION COMPLETE]
Topic: Enterprise Customer Save (MedTech Account Outage)
Identified Hook Fit: H3 (Emotional Cold-Open) / H19 (Customer Quote Cold-Open)
Founder Angle: A3 (High-Stakes Customer Save)

Extracted Story Summary:
"When our largest enterprise client experienced a 4-hour synchronization freeze during their quarterly audit, their VP of Operations told us: 'If this isn't resolved by midnight, we are canceling.' Instead of sending standard PR apologies, our CEO and Lead Architect hopped on a continuous Zoom bridge, built a custom patch in 3 hours, and shipped an automated validation script that prevented duplicate sync locks. Result: The client renewed for $180k ARR."

Action: APPEND TO references/story-bank.md (Section 3: Customer Saves)
Approval Token: 55ee2290-aa11-4f99-8877-334455667788
Status: PENDING USER APPROVAL TO SAVE.
Command: Approve token to persist story in Story Bank.

Examples

Example 1: Interactive Interview Transcript

Interviewer: "What was the single most painful technical outage your company experienced this year?"

Founder: "It was back in March. Our database crashed during a product launch because our read queries overwhelmed the primary node."

Interviewer (Probing for Specificity): "That hurts. What exact time did the alerts go off, and what was the customer impact? Were users unable to log in, or did transactions fail?"

Founder: "Alerts fired at 11:15 AM right as our TechCrunch feature dropped. Users were getting 504 timeouts at the signup screen. We had about 8,000 visitors hitting a cold Redis cache and hammering Postgres."

Interviewer (Extracting the Financial & Emotional Stakes): "How long did it take to stabilize, and what was the lasting rule your team put in place because of it?"

Founder: "We were down for 45 minutes. We probably lost 2,000 signups. That afternoon we instituted a strict rule: no marketing launch happens without pre-warming the cache and load-testing read replicas to 5x peak traffic."

Example 2: Formatted Story Bank Output Saved to Disk

### Entry 3.2: The TechCrunch Launch Cache Meltdown
- **Date/Period:** March 14, 2026 (11:15 AM)
- **The Context:** TechCrunch launch feature brought an unexpected surge of 8,000 concurrent visitors.
- **The Crisis:** Users hit cold Redis cache, driving simultaneous un-indexed queries directly into primary Postgres instance. 504 gateway timeouts on signup.
- **The Cost:** 45 minutes total downtime; estimated 2,000 lost user signups.
- **The Resolution:** Spin up read replicas and pre-warm cache keys using synthetic traffic.
- **The Enduring Rule:** Launch Day Checklist Rule #1: All marketing campaigns require pre-warmed cache layers and 5x load testing against read replicas.
- **Compatible Hook Formulas:** H3 (Emotional Cold-Open), H10 (Data-Reveal), H11 (Confession).
- **Compatible Founder Angles:** A6 (Architecture Rewrite), A2 (Post-Mortem).