LinkedIn Post Writer

Overview

Drafts long-form LinkedIn posts using 16 hook formulas that were reverse-engineered from posts that outperformed their authors' baselines in 2025-2026, each with a reference engagement number. Instead of asking "what should I write", the workflow asks "what should this post earn" (comments, reposts, likes, or saves), shortlists 2-3 matching formulas, fills the chosen skeleton with the user's voice, then scrubs the draft for AI tells before it ships.

This is the flagship skill from sergebulaev/linkedin-skills, a 10-skill LinkedIn bundle (writer, humanizer, pre-publish audit, comment drafter, reply handler, hook extractor, content planner, profile optimizer, engager analytics, thread monitor) installable as a Claude Code or Codex plugin. This standalone version covers the drafting workflow; scheduling and publishing automation live in the full bundle.

When to Use This Skill

• Use when the user says "write me a LinkedIn post about X"
• Use when the user has a topic and a rough angle but needs a hook and structure
• Use when the user wants to pick from proven post formats instead of improvising
• Use when a draft exists but the hook is weak and needs a formula-based rebuild
• Not for replying to comments or optimizing profiles; this skill only drafts posts

How It Works

Step 1: Gather inputs

Collect: topic, angle, target audience (founders, operators, marketers), desired length (short 300-500, medium 900-1,300, or long 1,500-1,900 characters), and any raw material the user already has (numbers, anecdotes, names).

Step 2: Pick the formula by engagement goal first

Ask (or infer) what the post should earn, then shortlist:

| Goal | Earned by | Formulas | |---|---|---| | Comments | questions, contrarian takes, vulnerability | F4 Time-Anchor Confession, F10 Contrarian + Receipts, F12 Permission Slip, F9 Curiosity-Gap | | Reposts | quotable maxims, tributes, "X isn't Y" distinctions | F14 Named Gratitude, F2 R.I.P. Obituary, F8 Paid-vs-Free Reversal | | Likes | emotional stories, celebrations, status-strip | F11 Emotional Cold-Open, F13 Bait-and-Switch Reversal, F16 Status-Strip Humility | | Saves | simplifications, exact how-to, frameworks | F15 Explain-to-Kids, F7 Odd-Precision Money Ledger, F8 Paid-vs-Free Reversal |

The full set of 16, with reference engagement:

| Code | Formula | Reference | Best for | |---|---|---|---| | F1 | Platform Risk Anaphora | 4,240 eng | Category and platform-risk arguments | | F2 | R.I.P. Obituary | 3,822 eng | Era-ending claims, industry pivots | | F3 | Year-over-Year Pivot | 494 eng, 3.74x baseline | Identity shifts, founder reflection | | F4 | Time-Anchor Confession | 1,519+ eng | Vulnerability, voice reset | | F5 | Self-Proving Meta | 1,082 eng, 435 comments | Commitments and tests in public | | F6 | Comment-Gate Lead Magnet | 717-3,008 eng | List building (max once a month) | | F7 | Odd-Precision Money Ledger | 1,755 eng, 9.4x baseline | Build logs, cost breakdowns | | F8 | Paid-vs-Free Reversal | 550 eng, 19.64x baseline | Framework giveaways | | F9 | Curiosity-Gap Teaser | 306 eng, 4.25x baseline | Surprise and behind-the-scenes stories | | F10 | Contrarian + Historical Receipts | 3,083 eng | Sacred-cow takes backed by history | | F11 | Emotional Cold-Open | high raw reach | Real stories with emotional stakes | | F12 | Permission Slip | comment-heavy | Encouragement to a discouraged audience | | F13 | Bait-and-Switch Reversal | high raw reach | Bad-news framing that turns into an upgrade | | F14 | Named Gratitude / Tribute | repost-heavy | Thanking mentors, teams, departing colleagues | | F15 | Explain-to-Kids | save-heavy | Demystifying jargon into a reference post | | F16 | Status-Strip Humility | like-heavy | Senior voices trading prestige for warmth |

Important caveat: F1-F10 references are engagement counts or format multipliers against the author's own baseline; F11-F16 references are raw corpus reach, often inflated by a famous author or a reshare. The two groups measure different things, so never rank formulas across groups by number.

The full skeletons for all 16 formulas are bundled with this skill in references/hook-formulas.md, along with per-formula warnings and the "Never do" list.

Step 3: Draft the post

Read the chosen formula's skeleton from the bundled references/hook-formulas.md and fill it with the user's voice and material, respecting the 2026 formatting rules:

• Hook lands in the first 210 characters (before the "... see more" fold)
• 900-1,300 characters is the sweet spot for text posts
• Double line-breaks between ideas, never walls of text
• 0-2 hashtags, placed at the end
• No external links in the body (put links in the first comment)
• Prefer "How I" over "How to": first-person experience hooks outperform generic instruction 2-3x
• Put one specific number in the first sentence ($873.47 beats $900; 40,000 beats "a lot")

Step 4: Scrub AI tells

Before showing the draft: strip em dashes, AI vocabulary ("game-changer", "deep dive", "delve"), rule-of-three lists without receipts, and generic openers like "In today's fast-paced world". Add human fingerprints: at least one specific number, one named entity, and one first-person concrete detail per 100 words. Vary sentence length aggressively (mix 3-word and 25-word sentences).

Step 5: Present the result

Show the user: formula used, full draft, character count, and a suggested posting window (Tuesday to Thursday, 7:30-9:00 AM local time performs best for B2B audiences).

Examples

Example 1: Founder build-log (F7 Odd-Precision Money Ledger)

text
User:  write a post about what my bootstrapped SaaS actually costs to run
Agent: Goal check: this reads like a saves play (people screenshot cost
       breakdowns). Best fit is F7 Odd-Precision Money Ledger (9.4x baseline).
       Draft hook:

       "My SaaS costs $412.83 a month to run. Here is every line item."

       Then the ledger: hosting $94.20, email $35.00, monitoring $18.63...
       exact numbers only, totals that actually add up, one lesson at the end.

Example 2: Goal-first selection

text
User:  I want a post that gets real comments, topic: why I stopped doing demos
Agent: Comment-earners are F4 Confession, F10 Contrarian, F12 Permission Slip.
       For "I stopped doing X" the strongest is F10 Contrarian + Receipts:
       open with the unpopular claim, back it with 2 historical parallels,
       close with a question that forces side-picking. Reference: 3,083 eng.

Best Practices

• ✅ Pick the formula by engagement goal first, topic second
• ✅ Lead with a real failure or a specific number in the first 3 lines
• ✅ Include one moment of genuine vulnerability or concrete stakes; pure insight posts underperform in 2026
• ❌ Don't blend two hook formulas in one post; it dilutes both
• ❌ Don't use F5 Self-Proving Meta unless the user will actually keep the promise
• ❌ Don't pair F7 Money Ledger with rounded or invented numbers; readers notice
• ❌ Don't open with an all-caps line ("THIS CHANGED EVERYTHING")
• ❌ Don't frame LinkedIn as inferior inside a LinkedIn post

Limitations

• Reference engagement numbers describe the 2025-2026 corpus the formulas were extracted from; they are priors, not guarantees, and LinkedIn's ranking changes over time.
• The skill drafts text posts; it does not generate images, carousels, or video scripts.
• This standalone version does not schedule or publish. Scheduling, comment drafting, reply handling, and engagement analytics require the full bundle from the source repo.
• Voice quality depends on the raw material the user provides; a formula cannot invent authentic anecdotes, and the skill should ask for real details rather than fabricate them.

Common Pitfalls

• Problem: The draft sounds like every other AI-written LinkedIn post.

Solution: Run Step 4 ruthlessly. Cut em dashes, cut "game-changer" vocabulary, and force one concrete first-person detail per 100 words.

• Problem: The hook is buried in paragraph two.

Solution: The first 210 characters must carry the hook; everything before the fold decides the expand rate.

• Problem: Comparing F11's raw reach to F8's 19.64x multiplier and picking F11 "because the number is bigger".

Solution: The columns measure different things. Match formula to goal and topic, not to the largest number.

• Problem: Post gets reach but zero comments.

Solution: The formula was picked for the wrong goal. Comment-earners end with a question or a side-picking claim, not a summary.

Related Skills

• @linkedin-content-generator - broader LinkedIn content suite (carousels, newsletters, calendars)
• @linkedin-profile-optimizer - profile and authority optimization rather than post drafting
• @social-post-writer-seo - multi-platform social copy when LinkedIn is not the only target

Additional Resources

• Source repo with all 16 formula skeletons and worked examples
• Full 10-skill bundle install (Claude Code / Codex plugin)