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

* F11-F16 reach is absolute 2026-corpus reach (often source-driven: a reshare or a famous author), NOT a baseline multiplier like the F1-F10 numbers. The two columns measure different things and are not comparable: F11's "256k" is raw reach, F8's "550, 19.64x" is a format multiplier. Do not rank formulas by putting these side by side. See ../../references/hook-formulas.md for each formula's real reference and caveats.

† F17-F20 are structural formulas: they shape the logic of a post (a controlled comparison, a false binary, an evidence bridge, two diverging curves) rather than its topic. They carry no reference number and are chosen by primary goal. They were built for the founders edition and several founder angles pin them by name.

Full skeletons in ../../references/hook-formulas.md. F1-F10 are the long-form thought-leadership set; F11-F16 (validated against a 2026 corpus of above-average performers) skew shorter and emotional and each carries a primary engagement goal.

2026 reach caveats (Sep 2026 audit)

The reference numbers above are unchanged; what changed is how the 2026 feed treats the device each formula leans on. Every formula in ../../references/hook-formulas.md now carries a "2026 reach note"; the ones that matter when picking:

• Never open with a question. Question as the first line is -34% median likes across all follower bands (MagicPost, 1.2M posts; vendor data, proprietary AI-score). Move the question to the close, where it is +3%.
• Prefer number-first. An odd-precision number in line 1 is +34% median likes (same source). F7 is the strongest 2026 opener; F3, F5, F17 are number-first by construction.
• F4 Confession, use with care: a specific, dated, uncomfortable fact with no "let me be honest" / "confession:" framing; substance inside the first 3 lines. Manufactured candor is the "false vulnerability" tell; genuine vulnerability is +7 to +10% (vendor data).
• F6 Comment-Gate, use with care: comment-gate CTAs are the named target of LinkedIn's March 2026 authenticity update, and the July 2026 "AI slop" report button cuts flagged posts ~40% views. Only with a real, named deliverable, and never "comment X to get Y" phrasing.
• F9 Curiosity-Gap, use with care: teaser phrases ("what nobody tells you", "what most people miss", "the real question is") are on the 2026 AI-tell consensus lists. The gap must be specific and pay off within 2 lines, before the fold.
• F12 Permission Slip and F18 False-Binary, use with care: both are generic-frame devices ("Stop X, start Y" -6.7%, "It's not X, it's Y" -4.9%, vendor data). They survive with a dated fact and as the post's only contrast.
• Density rule: one contrast and one triple per post, zero "The result?" / "Plot twist:" / "Here's what" bridges. 98-100% of top human creators still use these devices; the tell is repetition plus emptiness, not the device.
• Still lifts reach: number-first line, closing question, P.S. sign-off (+7.5%), 1,000+ chars (1.18x) and 20+ sentences (1.14x, AuthoredUp 3M posts), 1-2 sentence paragraphs with blank lines (recommended layout, not a tell).

Pick by goal first

If the user knows what they want the post to earn, start here, then narrow by topic. Canonical mapping: ../../references/hook-formulas.md → Engagement-goal split.

Steps

Voice profile first (all drafts). If ../../references/voice-profile.md has filled: yes, load it and match the user's voice fingerprint, hard rules, and CTA/link style throughout. If it is not filled, mention once that linkedin-humanizer --mode profile can learn their voice from a few posts, then proceed with the generic voice rules. If ../../references/story-bank.md has filled: yes, load it too and take concrete details (numbers, dates, named projects) from there instead of asking mid-draft. Never invent a figure that is not in it; if the bank has nothing that fits, ask the user or offer linkedin-interviewer.

Founder mode (when the writer is a founder). Before picking a formula, open ../../references/founder-topics.md and offer a founder angle (A1-A10) that fits their goal. The angle picks the territory (reprice the category, the scarce-shots math, the delegation line, and so on); several angles pin the formula for you (A9 uses F17, A10 uses F18+F20). Founder angles compound trust with a narrow audience of investors, hires, and design partners rather than chasing broad reach. Fill the angle's bracketed slots with the founder's real numbers, then continue from step 3.

Spine from an interview (skip the asking). If linkedin-interviewer --mode post handed over a five-line spine (Moment, Number, Correction, Opposition, Ask), that spine is the skeleton: Moment opens the post, Number is the proof, Correction is the turn, Opposition fixes the audience, Ask is the close. Do not re-ask for anything the spine already carries, and do not fill a line the interview left labelled empty. Pick the formula that fits the spine rather than bending the spine to a formula; if none fits, say which line is the obstacle.

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. First ask (or infer) the goal: comments, reposts, likes, or saves. Use the "Pick by goal first" table to shortlist, then suggest 2-3 formulas that also fit the topic and let the user pick. Show the reference engagement number next to each, plus the formula's 2026 caveat if it has one. Two hook rules apply regardless of formula: never open with a question (-34% median likes; the question goes at the close, +3%) and prefer a number-first line (+34% median likes; both MagicPost vendor data, proprietary AI-score). If the best hook you have is a question, invert it into the number that answers it.
3. Draft the post. Fill the formula skeleton with user voice. Respect the 2026 algorithm rules:
   • Hook in first 210 chars (before "… see more"); line 1 is a statement or a number, never a question, never "Here's what/how", never "Stop X, start Y"
   • Length: the target the user picked in step 1 wins. 900-1,300 chars is the default when they express no preference, not a ceiling over their choice. If they asked for long (1,500-1,900), write long and do not trim toward the sweet spot: 1,000+ chars and 20+ sentences carry a 1.18x / 1.14x reach lift (AuthoredUp, 3M posts), so the evidence runs with them, not against them. The one hard limit is LinkedIn's 3,000 characters.
   • Double line-breaks between ideas, not single; 1-2 sentence paragraphs are the recommended layout
   • One contrast and one triple per post maximum; no "The result?" / "Plot twist:" reveal bridges (Density rule in ../../references/hook-formulas.md)
   • Close with a specific question, and add a one-line P.S. when there is a real follow-up (+7.5%)
   • 0-2 hashtags, placed at end
   • No external links in body (move to first comment)
4. Humanizer pass. Scrub 2026 AI vocab by density, cap em dashes (about one per 100 words), break stacked triads, generic openers and reveal bridges. Add at least 1 specific number, 1 named entity, 1 first-person concrete detail per 100 words.
5. Run audit. Optionally invoke linkedin-humanizer --mode audit for algorithm + voice checks before showing to user.
6. Optional illustration. If the post would land better with a visual (or the user asks), offer one: draft an image and generate it with lib.illustrate(prompt, kind="wide"), pulling brand handle/color from Voice & Brand Profile §6 for the overlay. Show the returned url + cost in the approval card and attach it via media_urls on publish. For a multi-image grid (2-10 images in one post) use lib.illustrate_set([p1, p2, ...], kind="wide", overlay=brand) and pass every url in media_urls=[...]. For a quote-card of the hook, skip the model and typeset it: lib.quote_card("<hook line>", handle="@handle", style="brand") — crisp text, same url flow. Full workflow: ../linkedin-humanizer/sub-skills/illustration.md. No Pixfaro key -> it drafts the prompt for the user to generate manually.
7. 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, and the illustration (if any).
8. On approval. Call lib.publish(kind="post", draft_text=<approved>, target_url="https://www.linkedin.com/post/new/", platforms=[{"platform":"linkedin","platformId":<id>}], scheduled_time=<iso_or_None>, media_urls=<list_or_None>). The wrapper handles Publora / manual / diy routing. If the user reconsiders after approving, call lib.unpublish(post_group_id=<postGroupId from the response>) to cancel it before it goes out. On the publora tier the post is already queued, so the dashboard is otherwise the only way back.