Linkedin post writer
Skill sickn33/agentic-awesome-skills/skills/linkedin-post-writer
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npx -y skills add sickn33/agentic-awesome-skills --skill linkedin-post-writerAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
What its author says it does
Copied from the file, not written here
Draft LinkedIn posts from 16 tested hook formulas mapped to engagement goals (comments, reposts, likes, saves), with 2026 algorithm formatting rules and an AI-tell scrub pass before publishing.
The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
9.7 KB, as published. Nobody here has run it
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)
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
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