https://github.com/manthis/linkedin-spam-filter

OpenClaw skill: Detect LinkedIn prospection/spam via Beeper MCP
https://github.com/manthis/linkedin-spam-filter

beeper linkedin openclaw skill spam-detection

Last synced: about 2 months ago
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OpenClaw skill: Detect LinkedIn prospection/spam via Beeper MCP

• Host: GitHub
• URL: https://github.com/manthis/linkedin-spam-filter
• Owner: manthis
• License: mit
• Created: 2026-02-18T20:58:15.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2026-03-18T08:14:30.000Z (3 months ago)
• Last Synced: 2026-03-18T23:53:03.351Z (3 months ago)
• Topics: beeper, linkedin, openclaw, skill, spam-detection
• Language: Python
• Size: 49.8 KB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-beeper - linkedin-spam-filter

README

          
# 🎯 openclaw-skill-linkedin-spam-filter

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![OpenClaw Skill](https://img.shields.io/badge/OpenClaw-Skill-blue)](https://github.com/OpenAgentsInc/openclaw)

Detect LinkedIn spam and prospection messages via Beeper MCP. Auto-generates suggested responses (FR/EN) with human-in-the-loop confirmation.

## Quick Start

```bash

git clone https://github.com/manthis/openclaw-skill-linkedin-spam-filter.git

cd openclaw-skill-linkedin-spam-filter

# Test detection locally

python3 scripts/linkedin-spam-filter.py --test-text "Hi, I have an exciting opportunity for you"

# Full check (requires Beeper MCP)

export BEEPER_SERVER="beeper"

python3 scripts/linkedin-spam-filter.py --dry-run --json

```

## Configuration

| Variable | Default | Description |

|----------|---------|-------------|

| `BEEPER_SERVER` | `beeper` | Beeper MCP server name |

| `LINKEDIN_ROOM_PATTERN` | `linkedin` | Room name filter |

| `SPAM_PATTERNS` | (built-in) | Detection regex |

| `RESPONSE_TEMPLATES` | (built-in) | JSON response templates |

> ⚠️ **Security:** Never store MCP tokens or credentials in config files.

## Workflow

1. **Detect** spam via `linkedin-spam-filter.py --json`

2. **Review** the suggested response

3. **Send** the response via `scripts/send-response.py --chat-id  --message ""`

4. **Archive** automatically (done by send-response.py)

⚠️ **Important:** Always archive the chat after sending a response to keep LinkedIn inbox clean.

## Features

- 🔍 Pattern-based prospection detection (FR + EN)

- 💬 Auto-generated response suggestions

- 🌍 Language auto-detection (French/English)

- 🛡️ Human-in-the-loop — never auto-sends

- 📊 JSON output for automation

- 🧪 Standalone test mode (`--test-text`)

- 📥 Auto-archive after response

## Requirements

- Python 3.8+

- `mcporter` CLI with Beeper MCP (for live checks)

## License

MIT