Linkedin Post Writer

LinkedIn Post Writer

Ship long-form LinkedIn posts using hook formulas that actually performed in 2025-2026 (verified engagement multipliers).

When to use

• User says "write me a LinkedIn post about X"
• User has a topic + a rough angle and needs a hook + structure
• User wants to pick from known-winning formats and fill in their voice
• User wants to audit + schedule in one flow

Formulas this skill can use

| Code | Formula | Reference eng | Best for | |---|---|---|---| | F1 | Platform Risk Anaphora | 4,240 | Category/platform posts, product-as-fix | | F2 | R.I.P. Obituary | 3,822 | Era-ending claims, industry pivots | | F3 | Year-over-Year Pivot | 494, 3.74x | Identity shifts, founder reflection | | F4 | Time-Anchor Confession | 1,519+ | Vulnerability, voice reset, ICP re-targeting | | F5 | Self-Proving Meta | 1,082 / 435 comments | Commitment-based posts, tests in public | | F6 | Comment-Gate Lead Magnet | 717-3,008 | List building (use sparingly, capped reach) | | F7 | Odd-Precision Money Ledger | 1,755, 9.4x | Founder build-log, cost breakdowns | | F8 | Paid-vs-Free Reversal | 550, 19.64x | Free framework give-away | | F9 | Curiosity-Gap Teaser | 306, 4.25x | Emergent behavior, behind-the-scenes | | F10 | Contrarian + Historical Receipts | 3,083 | Sacred-cow takes, AI/tech cycles |

Full skeletons in ../../references/hook-formulas.md.

Steps

1. Gather inputs. Topic, angle, draft ideas if the user has them, target audience (founders / operators / marketers), desired length (short 300-500 / medium 900-1300 / long 1500-1900 chars).
2. Pick the formula. If the user didn't specify, suggest 2-3 formulas that fit the topic and let them pick. Show the reference engagement number next to each.
3. Draft the post. Fill the formula skeleton with user voice. Respect the 2026 algorithm rules:

• Hook in first 210 chars (before "… see more")
• 900-1,300 char sweet spot for text posts
• Double line-breaks between ideas, not single
• 0-2 hashtags, placed at end
• No external links in body (move to first comment)

1. Humanizer pass. Strip em dashes, AI vocab, rule-of-three, generic openers. Add at least 1 specific number, 1 named entity, 1 first-person concrete detail per 100 words.
2. Run audit. Optionally invoke linkedin-humanizer --mode audit for algorithm + voice checks before showing to user.
3. Approval card. Show: formula used, full draft, char count, suggested posting window (Tue/Wed/Thu 7:30-9:00 AM local), reaction targets from likely commenters.
4. On approval. Call lib.publish(kind="post", draft_text=, target_url="https://www.linkedin.com/post/new/", platforms=[{"platform":"linkedin","platformId":}], scheduled_time=, media_urls=). The wrapper handles Publora / manual / diy routing.

Hard rules (from user feedback)

Global voice rules: see root SKILL.md §Voice rules. Additional skill-specific rules:

• Never frame LinkedIn as inferior in a LinkedIn post (algo penalty).
• Don't name-drop the user's product in a way that reads as self-promo. One mention max, and only when it's the natural conclusion, not the pitch.
• Include at least one moment of real vulnerability or concrete stakes. Pure insight posts don't land in 2026.
• Vary sentence length aggressively. Mix 3-word sentences and 25-word sentences.

Anti-patterns (skill will refuse)

• All-caps first line ("THIS CHANGED EVERYTHING.")
• Em dashes anywhere
• "In today's fast-paced world" openers
• Rule-of-three lists without receipts
• "Game-changer", "deep dive", "leverage", "fundamentally"
• External links in the body
• Reused engagement-bait closers ("tag someone who needs this")

Resources

• ../../references/hook-formulas.md — all 10 formula skeletons with worked examples
• ../../references/algorithm-heuristics.md — 2026 posting rules (timing, format, length)
• references/humanizer-checklist.md — the full scrub list

Related skills

• linkedin-humanizer — aggressive AI-tell scrubber, plus --mode audit for pre-publish review
• linkedin-hook-extractor — reverse-engineer a hook from a viral post you admire

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: sergebulaev
• Source: sergebulaev/linkedin-skills
• License: MIT
• Homepage: https://github.com/sergebulaev/linkedin-skills

Install and usage instructions live in the source repository linked above.