LinkedIn Message Drafter

Write LinkedIn outreach that actually gets replies — personalized, warm, and ready in seconds. Feed it a name and a bit of context; it hands back a concise, human-sounding draft you can send as-is or tweak.

Draft-first and safe by design. It does not scrape LinkedIn, automate browser actions, or send anything on its own. You stay in control of every message — review, copy, send. Ready to automate? Wire in an approved LinkedIn integration through the adapter below.

Install

pipx install linkedin-message-drafter          # or: pip install linkedin-message-drafter
# for the Claude draft path: pipx install "linkedin-message-drafter[ai]"

Quick start

linkedin-draft prospect.json      # prints a draft and saves it to drafts/

From source (development)

git clone https://github.com/wuisabel-gif/linkedin-message-drafter.git
cd linkedin-message-drafter
python3 -m venv .venv && source .venv/bin/activate
pip install -e .                  # add [ai] for the Claude draft path: pip install -e ".[ai]"
linkedin-draft src/linkedin_message_drafter/examples/prospect.json

Web UI

Prefer a form to the CLI? A zero-dependency local UI ships with it:

linkedin-draft-web            # then open http://127.0.0.1:8000

Binds to localhost only, uses the same drafting engine (voice matching, slop check, short mode), and still just drafts — nothing is sent.

Input

See src/linkedin_message_drafter/examples/prospect.json. Required fields are name, context, and goal; company, role, and relationship are optional. CSV input uses the same column names, one prospect per row.

Options

# Batch — pass multiple files, a CSV, or a whole folder
linkedin-draft ./prospects/
linkedin-draft prospects.csv

# Connection-request note, guaranteed under LinkedIn's 300-char limit
linkedin-draft --short my-prospect.json

# Best-of-N — generate 3 AI drafts, keep the one cadence-deslop scores cleanest
linkedin-draft --best-of 3 my-prospect.json

# Write in a Cadence voice preset (AI path; needs CADENCE_VOICES)
export CADENCE_VOICES=/path/to/Cadence/voices
linkedin-draft --style punchy my-prospect.json

--short works with both the template and AI paths; --style applies to AI drafts only. Available presets are the .md files in Cadence's voices/ (punchy, column, plain, dispatch, …).

AI drafting (optional)

Set ANTHROPIC_API_KEY to draft with Claude instead of the built-in template — richer, more personalized copy. With no key set, the tool uses the dependency-free template and works fully offline.

export ANTHROPIC_API_KEY=sk-ant-...
linkedin-draft my-prospect.json

Sounds like you (Cadence)

Point VOICE_SAMPLE at your own past writing and the AI draft mimics the way you speak — tone, rhythm, and word choice. It takes a single file or a directory: drop in a folder of all your previous posts and messages and Cadence learns from the whole corpus.

export VOICE_SAMPLE=~/my-writing-sample.txt   # one sample
export VOICE_SAMPLE=~/my-linkedin-posts/       # or a folder of everything you've written

Automatic slop check. If the cadence-deslop detector is installed, every AI draft is scored 0–100 for AI-slop tells (uniform rhythm, hollow-confidence words, clichéd openers). If a draft reads as slop, Claude rewrites it once with the named tells fed back, and the cleaner version is kept. It's a no-op when the detector isn't installed.

npm install -g cadence-deslop                 # or: export CADENCE_DESLOP=/path/to/deslop.mjs

For a deeper voice pass, run the /cadence skill on any saved draft in drafts/.

Use it inside Claude or ChatGPT

The drafter plugs into AI assistants three ways:

• MCP server (Claude Desktop/Code, ChatGPT MCP connector). Install the extra and register linkedin-draft-mcp:

  pip install "linkedin-message-drafter[mcp]"
  

  It exposes draft_message and score_text tools over stdio. Point your MCP client at the linkedin-draft-mcp command.

• Claude Skill — skills/linkedin-drafter/SKILL.md. Drop it into your Claude skills directory (or plugin) and Claude drafts on request.

• ChatGPT GPT Action — integrations/chatgpt-action-openapi.yaml. Host the JSON backend (linkedin-draft-web serves POST /api/draft) behind a public HTTPS URL, set that URL in the spec's servers, and import it as an Action in a custom GPT.

All three stay draft-only: they return a message (and its Cadence slop score); nothing is scraped or sent.

Adding an approved API integration

src/linkedin_message_drafter/linkedin.py contains a deliberately unimplemented LinkedInClient. Implement only the endpoints and scopes that LinkedIn has approved for your application. Keep the default workflow draft-only and require a user confirmation before any send operation. Do not add scraping, credential collection, or bulk/unattended sending.

Read TUTORIAL.md before configuring credentials or requesting permissions.

Tests

python -m unittest discover -s tests -v

Publishing

Releases publish to PyPI automatically via Trusted Publishing — no API token is stored anywhere. One-time setup:

1. On pypi.org, add a pending publisher: project linkedin-message-drafter, owner wuisabel-gif, repo linkedin-message-drafter, workflow publish.yml, environment pypi.

2. Cut a release: bump version in pyproject.toml, then

   git tag v0.1.0 && git push origin v0.1.0
   gh release create v0.1.0 --generate-notes
   

The Publish to PyPI workflow builds the sdist + wheel and uploads them. Local dry run:

python -m build && twine check dist/*