Reverse-engineer the hook formula from a viral LinkedIn post URL. Returns which of the 16 canonical 2026 formulas it uses (anaphora, R.I.P., year-pivot, time...
15 次安装
安全
Linkedin Post Writer
试用按互动目标（评论、转发、点赞、收藏）从 16 个经实测的钩子公式中挑选，撰写一篇 LinkedIn 长文。
它能做什么
从零开始撰写 LinkedIn 长文，可选用 16 个钩子公式中的任意一种（首语重复、R.I.P. 计告、年终转折、好奇缺口、逆向观点、情感开场等），按你想要的互动目标（评论、转发、点赞、收藏）来匹配。遵循 2026 年 LinkedIn 算法规则：钩子放在前 210 字符、字数落在 900–1,300 字符的甜区、双换行分段、0–2 个标签、正文不放外链；随后运行人味化检查去除 AI 痕迹，再送入审批卡片。批准后由发布包装器（lib.publish）接管排期，包括对接 Publora 的路径。
什么时候用它
- 围绕一个话题和角度，从零开始写一篇 LinkedIn 帖子
- 按互动目标（评论/转发/点赞/收藏）挑选合适的钩子公式
- 一站式完成撰写 + 人味化 + 排期发布
- 生成严格遵循 2026 年 LinkedIn 算法规则的文案
技能文档
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 |
| F11 | Emotional Cold-Open | high-reach* | Real story with emotional stakes (likes) |
| F12 | Permission Slip | comment-heavy* | Encouragement, reassurance (comments) |
| F13 | Bait-and-Switch Reversal | high-reach* | Policy/process change that's an upgrade (likes) |
| F14 | Named Gratitude / Tribute | repost-heavy* | Thanking mentors / team / departing colleague (reposts) |
| F15 | Explain-to-Kids | save-heavy* | Demystifying jargon (saves) |
| F16 | Status-Strip Humility | like-heavy* | Senior voice wanting warmth not distance (likes) |
* 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.
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.
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.
| Goal | Reach for |
|---|---|
| Comments | F4, F10, F12, F9 |
| Reposts | F14, F2, F8 |
| Likes | F11, F13, F16 |
| Saves | F15, F7, F8 |
Steps
- 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).
- 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.
- 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)
- 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.
- Run audit. Optionally invoke
linkedin-humanizer --mode auditfor algorithm + voice checks before showing to user. - 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.
- 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."). This holds even for F11 Emotional Cold-Open: carry the intensity with word choice, never caps.
- 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 16 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 auditfor pre-publish reviewlinkedin-hook-extractor— reverse-engineer a hook from a viral post you admire
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