# linkedin-automation

> LinkedIn automation — post, comment (with @mentions), edit/delete comments, repost, read feed, analytics, like monitoring, and engagement tracking. Uses Playwright with persistent browser profile. Use for any LinkedIn task including content strategy, engagement analysis, and audience growth.

- Author: Robert
- Repository: BearyRadish/openclaw-video-dubber
- Version: 20260208072855
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-07
- Source: https://github.com/BearyRadish/openclaw-video-dubber
- Web: https://mule.run/skillshub/@@BearyRadish/openclaw-video-dubber~linkedin-automation:20260208072855

---

---
name: linkedin-automation
slug: linkedin-automation
display_name: LinkedIn Automation
description: LinkedIn automation — post, comment (with @mentions), edit/delete comments, repost, read feed, analytics, like monitoring, and engagement tracking. Uses Playwright with persistent browser profile. Use for any LinkedIn task including content strategy, engagement analysis, and audience growth.
---

# LinkedIn Automation

> **Author:** Andreas Kulpa ([bigdataheaven.com](https://bigdataheaven.com))
>
> ⚠️ **DISCLAIMER — PERSONAL USE ONLY**
> This skill is provided for **personal, non-commercial use only**. It automates your own LinkedIn account for personal productivity and engagement. Do NOT use this skill for spam, mass outreach, scraping other users' data, or any commercial automation service. Use responsibly and in accordance with [LinkedIn's User Agreement](https://www.linkedin.com/legal/user-agreement). The author assumes no liability for misuse or account restrictions.

Automate LinkedIn interactions via headless Playwright browser with a persistent session.

## Prerequisites

- Python 3.10+ with Playwright installed (`pip install playwright && playwright install chromium`)
- A logged-in LinkedIn browser session (persistent Chromium profile)
- Adjust paths in `scripts/lib/browser.py` to match your setup

## Commands

```bash
CLI={baseDir}/scripts/linkedin.py

# Check if session is valid
python3 $CLI check-session

# Read feed
python3 $CLI feed --count 5

# Create a post
python3 $CLI post --text "Hello world"

# Comment on a post (supports @Mentions — see below)
python3 $CLI comment --url "https://linkedin.com/feed/update/..." --text "Great insight @Betina Weiler!"

# Edit a comment (match by text fragment)
python3 $CLI edit-comment --url "https://..." --match "old text" --text "new text"

# Delete a comment
python3 $CLI delete-comment --url "https://..." --match "text to identify"

# Repost with thoughts
python3 $CLI repost --url "https://..." --thoughts "My take..."

# Engagement analytics for recent posts
python3 $CLI analytics --count 10

# Profile-level stats (followers, views)
python3 $CLI profile-stats

# Monitor your likes for new ones (for comment suggestions)
python3 $CLI scan-likes --count 15

# Scrape someone's activity
python3 $CLI activity --profile-url "https://linkedin.com/in/someone/" --count 5
```

All commands output JSON. Enable debug logging: `LINKEDIN_DEBUG=1`.

## @Mentions

Comments support `@FirstName LastName` syntax. The skill:
1. Types `@FirstName` → waits for typeahead dropdown
2. Progressively types last name letter by letter if needed
3. Clicks the match only if first+last name both match
4. Falls back to plain text if person not found (returns `mention_failed` warning)

Check `mentions` in the JSON result to see if mentions succeeded.

## Like Monitor

The `scan-likes` command checks your recent likes/reactions activity and returns any **new likes since the last check**. State is persisted to avoid duplicate alerts. Ideal for cron/heartbeat integration:

```
# In HEARTBEAT.md or cron job:
python3 $CLI scan-likes → if new likes found → suggest comment for each
```

## ⚠️ Golden Rule

**NEVER post, comment, repost, edit, or delete anything without EXPLICIT user approval.**

Always show the user exactly what will be posted and get a clear "yes" before executing. Read-only actions (feed, analytics, check-session, scan-likes) are safe to run freely.

## Content Strategy & Engagement

- **[references/content-strategy.md](references/content-strategy.md)** — Hook formulas, post structure, posting times, hashtag strategy, 4-1-1 rule
- **[references/engagement.md](references/engagement.md)** — Algorithm signals, comment quality formula, rate limits, weekly routine
- **[references/dom-patterns.md](references/dom-patterns.md)** — Known LinkedIn DOM patterns for troubleshooting

## Rate Limits

| Action | Daily Max | Weekly Max |
|--------|----------|-----------|
| Posts | 2–3 | 10–15 |
| Comments | 20–30 | — |
| Likes | 100 | — |
| Connection requests | 30 | 100 |

## Setup

1. Install dependencies: `pip install playwright && playwright install chromium`
2. Configure browser profile path in `scripts/lib/browser.py` (or set `LINKEDIN_BROWSER_PROFILE` env var)
3. Log in to LinkedIn manually once (the session persists)
4. Run `python3 scripts/linkedin.py check-session` to verify
5. **Learn your voice:** Run `python3 scripts/linkedin.py learn-profile` — this scans your recent posts and comments to learn your tone, topics, language, and style. The agent uses this profile when suggesting comments/posts so they sound like **you**, not like a generic bot.

## Voice & Style

On first setup, `learn-profile` analyzes your content and saves a style profile (`~/.linkedin-style.json`) containing:
- **Language** (de/en/mixed)
- **Tone** (casual / professional / professional-friendly)
- **Emoji usage** (heavy / moderate / minimal)
- **Top hashtags** you use
- **Sample posts and comments** for voice reference

The agent should ALWAYS read this profile (`get-style`) before drafting any comment or post suggestion. Never impose a foreign voice — match the user's natural style.

## Troubleshooting

- **Session expired**: Log in again via browser profile
- **Selectors broken**: LinkedIn updates UI frequently — check `references/dom-patterns.md` and update `scripts/lib/selectors.py`
- **Debug screenshots**: Saved to `/tmp/linkedin_debug_*.png` on failure