openclaw
← All products
Content & Creator

linkedin-engine

LinkedIn ghostwriting in YOUR voice. 5-10 style examples → cached system prompt → consistent output.

Get startedSource on GitHub

Launch kit

linkedin-engine — launch kit

1-liner

LinkedIn ghostwriting in YOUR voice. 5-10 style examples → cached system prompt → consistent output.

Tweet hook

Most AI LinkedIn tools produce "competent business prose" — universally unreadable.

Built one that REQUIRES style examples + banned phrases up front. Output sounds like the user, not ChatGPT.

Demo: same topic, two voices 🧵

Reddit

  • r/LinkedIn: "Stop using AI for LinkedIn. Or do this instead."
  • r/Entrepreneur: "Voice-locked ghostwriting"

Cold-email ICP

B2B founders + execs already posting on LinkedIn 2-4x/week. Reach via their own posts (DM "loved your post on X — quick offer").

Cold-email template

Subject: voice-locked ghostwriting (saw your {recent post})

Hi {first} — your post on {topic} got 200+ comments. Real engagement.
The voice IS the product.

Most AI tools dilute that voice. I built one that locks to it: you
provide 5 style examples + banned phrases; output mimics. Free trial.

$199/mo for 4 posts/wk; $499 full DFY. Reply for setup.

SEO content

  1. "Why AI LinkedIn tools sound like ChatGPT"
  2. "Voice-locked ghostwriting: setup guide"
  3. "LinkedIn algo 2026: what hooks still work"

Documentation

linkedin-engine

LinkedIn ghostwriting engine for founders, executives, and operators. Generates posts in the user's voice (forced via 5-10 style examples) on cron-scheduled topics. Output goes to clipboard / Buffer / Hypefury for human approval before publishing.

Pricing

  • $199/mo per voice — 4 posts/wk generated, daily option
  • $499/mo — full DFY: voice setup + idea curation + posting
  • $1,499/mo — exec ghostwriting tier (CEO/founder)
  • DIY $0 — operator runs it for themselves

The Qwen-local angle: cloud-LLM ghostwriting tools charge $99-499/mo because their inference is the cost. Ours is electricity. We can either undercut on price OR pocket the margin.

What's shipped (v0)

  • Voice configuration via YAML (style examples, banned phrases, topic boundaries, default CTA)
  • Single-post generation respecting voice constraints
  • CLI: liwrite post --voice founder.yaml --topic "hiring senior BDRs"

What's stubbed for v1

  • Buffer / Hypefury / native LinkedIn API publish
  • Topic-idea pipeline (RSS → trending angle → daily generation)
  • Engagement-data feedback loop (which past posts won → tune prompts)
  • Multi-voice multiplexing for an agency

Run

cd C:\openclaw-products\linkedin-engine
python -m venv .venv
.\.venv\Scripts\activate
pip install -e .

# LM Studio with Qwen running

liwrite post --voice examples/founder-voice.yaml \
             --topic "Hiring BDRs from no-name SaaS vs big-logo backgrounds" \
             --angle "I just hired a CPaaS-background BDR and they're crushing"

The voice problem

The reason most AI LinkedIn tools produce dreck is they don't capture the user's actual voice — they produce "competent business prose" which is universally unreadable.

Our solution: the voice YAML REQUIRES style examples (3-5 of the user's own best posts), banned phrases, and topic constraints. The system prompt feeds this as cached context per generation. Output sounds like the user, not like ChatGPT.

If the user provides bad style examples, the output will be bad. That's on them.