Linkedin Story Interviewer

LinkedIn Story Interviewer

Generic posts are almost never a writing problem. They are a material problem: the draft was asked to be specific about a career nobody wrote down. The writer reaches for a number, finds none, and either stalls to ask the user or papers over the hole with something that sounds right. The first wastes the user's time on every single draft. The second puts an unverified claim under a real person's name.

This skill fixes the supply side. The agent runs a structured interview, presses each vague answer exactly once, records what the person can stand behind, and stores it in a story bank: one local JSON file holding roles, figures, things built, changes of mind, costs paid, arguable opinions and stories already told aloud. It is the right first step for someone who has never posted, because it needs a working life, not a posting history.

Offline only. This skill reads and writes local files and nothing else. It does not read a LinkedIn profile, fetch a page, post, schedule or call any API. Every fact in the bank comes from what the user says in the conversation or from a file they hand over.

Scope boundary. This skill gathers material; it does not write posts. Drafting a post from a seed is linkedin-post-writer. Stripping machine-sounding phrasing from a draft is linkedin-humanizer. Judging an opening line is linkedin-hook-analyzer. Laying entries out across a calendar is linkedin-content-planner. Reworking a talk or article the user already made is linkedin-content-repurposer. Headline, About and experience copy is linkedin-profile-optimizer. Comments, replies and thread follow-up belong to linkedin-comment-writer, linkedin-reply-manager and linkedin-thread-tracker; team programmes to linkedin-employee-advocacy; post-performance review to linkedin-engagement-analytics. The story bank file format is defined here and nowhere else. Siblings may read a bank, none require one.

When to use this skill

• The user says "interview me", "I don't know what to post about", or "I have nothing interesting to say"
• Someone is about to start posting and has no archive of their own writing to learn from
• Drafts keep stopping to ask for "a specific number or example"
• A plan has slots with topics but no entry behind them
• The user changed role, shipped something, or changed their mind since the bank was last updated
• One post is needed on one subject and there is a topic but no scene, date or figure yet

Inputs the skill expects

• The person, available to answer questions in the conversation (the only source of facts)
• Two to five content pillars, or a sentence about who they want reading; pillars can be drafted in the first ten minutes if absent
• An existing story bank file, if one exists, so nothing is asked twice
• Optional: a CV, a talk outline or old notes the user pastes in, treated as prompts for questions and never as answers
• A place to save the bank that is outside version control or ignored by it

Clarify First

Before the first question, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• Full bank session or single-post session — a bank session ranges across the whole career and takes most of an hour; a post session stays on one subject and ends in a draft seed. The question order is different from the first minute.
• Whether a bank file already exists and where — decides whether the session starts from the audit agenda or from a blank page, and prevents re-asking what is already answered.
• Who the reader is meant to be — the same warehouse story is told differently to future hires than to buyers; without a reader the pillar filing is arbitrary.
• Where the file will live — the bank holds named people and private figures; if the answer is "in this repo", the ignore rule gets added before anything is written.

Stop rule: ask only the two that most change the session. If the user says "just start", begin a bank session with general pillars and state the assumptions at the top of the session notes.

Workflows

Quick start: copy assets/story_bank_template.json, run Workflow 1, then validate the result with the audit before any draft touches it.

Workflow 1 — Run a full bank session

1. Audit the existing bank, or start from the template, so the session opens on the gaps rather than on page one.
2. Open with one wide question and follow whatever the person speeds up on. The kinds list is a checklist for the last ten minutes, not a script.
3. Ask one question at a time. Press each vague answer once for the number, the month or the name; accept what comes back.
4. Before closing, cover what is still empty: a change of mind, something that cost them, an opinion with opponents, and the stories they tell in person.
5. Settle naming and no-go subjects by asking directly. Record refusals.
6. Write the entries, keeping vivid phrasing verbatim in words. Mark anything unpressed as soft. Update updated and sessions.
7. Re-run the audit, verify that no blocker remains, and tell the user which two or three posts the new material makes possible.

python3 tools/linkedin/linkedin-story-interviewer/scripts/story_bank_audit.py \
  --input tools/linkedin/linkedin-story-interviewer/assets/sample_story_bank.json \
  --today 2026-10-07

Workflow 2 — Run a single-post session

1. Take the topic. If the user has none, offer three unused ready entries from the bank and let them pick.
2. Ask for the last time it actually happened: a day, a place, who was there.
3. Ask for one figure and how it was measured. If there is none, write not gathered; do not estimate.
4. Ask what they believed before and what they believe now.
5. Ask who the post is for and who would push back.
6. Ask what they would tell that reader to try on Monday.
7. Fill assets/draft_seed_template.md, read it back, apply their corrections, and add anything new to the bank as entries.

python3 tools/linkedin/linkedin-story-interviewer/scripts/story_bank_audit.py \
  --input tools/linkedin/linkedin-story-interviewer/assets/sample_story_bank.json \
  --format json --today 2026-10-07

Workflow 3 — Gate a bank before drafting or planning runs on it

1. Run the audit with --fail-on blocker. Exit code 1 means an entry names someone on the never-name list or touches a no-go subject.
2. Fix every blocker in the file itself. A blocker in the bank will be repeated by every draft that reads it.
3. Work the fix findings next: missing dates, bare figures, entries marked ready that still read soft.
4. Hand the agenda at the bottom of the report to the next session.

python3 tools/linkedin/linkedin-story-interviewer/scripts/story_bank_audit.py \
  --input tools/linkedin/linkedin-story-interviewer/assets/sample_story_bank.json \
  --min-per-pillar 3 --today 2026-10-07 --fail-on blocker

The sample bank fails this gate on purpose (exit 1): entry S-005 names a customer that the same file lists under naming.never.

Exit-code contract. 0 audit completed; 1 a finding reached the --fail-on level; 2 the file is missing, unreadable, not JSON or not shaped like a bank.

Decision frameworks

Which session to run