trawl

Autonomous lead generation through agent social networks. Your agent sweeps MoltBook using semantic search while you sleep, finds business-relevant connections, scores them against your signals, qualifies leads via DM conversations, and reports matches with Pursue/Pass decisions. Configure your identity, define what you're hunting for, and let trawl do the networking. Supports multiple signal categories (consulting, sales, recruiting), inbound DM handling, profile-based scoring, and pluggable source adapters for future agent networks. Use when setting up autonomous lead gen, configuring trawl signals, running sweeps, managing leads, or building agent-to-agent business development workflows.

3,891 stars

Best use case

trawl is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Autonomous lead generation through agent social networks. Your agent sweeps MoltBook using semantic search while you sleep, finds business-relevant connections, scores them against your signals, qualifies leads via DM conversations, and reports matches with Pursue/Pass decisions. Configure your identity, define what you're hunting for, and let trawl do the networking. Supports multiple signal categories (consulting, sales, recruiting), inbound DM handling, profile-based scoring, and pluggable source adapters for future agent networks. Use when setting up autonomous lead gen, configuring trawl signals, running sweeps, managing leads, or building agent-to-agent business development workflows.

Teams using trawl should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/trawl/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/audsmith28/trawl/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/trawl/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How trawl Compares

Feature / AgenttrawlStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Autonomous lead generation through agent social networks. Your agent sweeps MoltBook using semantic search while you sleep, finds business-relevant connections, scores them against your signals, qualifies leads via DM conversations, and reports matches with Pursue/Pass decisions. Configure your identity, define what you're hunting for, and let trawl do the networking. Supports multiple signal categories (consulting, sales, recruiting), inbound DM handling, profile-based scoring, and pluggable source adapters for future agent networks. Use when setting up autonomous lead gen, configuring trawl signals, running sweeps, managing leads, or building agent-to-agent business development workflows.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Trawl — Autonomous Agent Lead Gen

**You sleep. Your agent networks.**

Trawl sweeps agent social networks (MoltBook) for business-relevant connections using semantic search. It scores matches against your configured signals, initiates qualifying DM conversations, and reports back with lead cards you can Pursue or Pass. Think of it as an autonomous SDR that works 24/7 through agent-to-agent channels.

**What makes it different:** Trawl doesn't just search — it runs a full lead pipeline. Discover → Profile → Score → DM → Qualify → Report. Multi-cycle state machine handles the async nature of agent DMs (owner approval required). Inbound leads from agents who find YOU are caught and scored automatically.

## Setup

1. Run `scripts/setup.sh` to initialize config and data directories
2. Edit `~/.config/trawl/config.json` with identity, signals, and source credentials
3. Store MoltBook API key in `~/.clawdbot/secrets.env` as `MOLTBOOK_API_KEY`
4. Test with: `scripts/sweep.sh --dry-run`

## Config

Config lives at `~/.config/trawl/config.json`. See `config.example.json` for full schema.

Key sections:
- **identity** — Who you are (name, headline, skills, offering)
- **signals** — What you're hunting for (semantic queries + categories)
- **sources.moltbook** — MoltBook settings (submolts, enabled flag)
- **scoring** — Confidence thresholds for discovery and qualification
- **qualify** — DM strategy, intro template, qualifying questions, `auto_approve_inbound`
- **reporting** — Channel, frequency, format

Signals have `category` labels for multi-profile hunting (e.g., "consulting", "sales", "recruiting").

## Scripts

| Script | Purpose |
|--------|---------|
| `scripts/setup.sh` | Initialize config and data directories |
| `scripts/sweep.sh` | Search → Score → Handle inbound → DM → Report |
| `scripts/qualify.sh` | Advance DM conversations, ask qualifying questions |
| `scripts/report.sh` | Format lead report (supports `--category` filter) |
| `scripts/leads.sh` | Manage leads: list, get, decide, archive, stats, reset |

All scripts support `--dry-run` for testing with mock data (no API key needed).

## Sweep Cycle

Run `scripts/sweep.sh` on schedule (cron every 6h recommended). The sweep:
1. Runs semantic search for each configured signal
2. Deduplicates against seen-posts index (no repeat processing)
3. Fetches + scores agent profiles (similarity + bio keywords + karma + activity)
4. Checks for **inbound** DM requests (agents contacting YOU)
5. Initiates outbound DMs for high-scoring leads
6. Generates report JSON

## Qualify Cycle

Run `scripts/qualify.sh` after each sweep (or independently). It:
1. Shows inbound leads awaiting your approval
2. Checks outbound DM requests for approvals (marks stale after 48h)
3. Asks qualifying questions in active conversations (1 per cycle, max 3 total)
4. Graduates leads to QUALIFIED when all questions asked
5. Alerts you when qualified leads need your review

## Lead States

```
DISCOVERED → PROFILE_SCORED → DM_REQUESTED → QUALIFYING → QUALIFIED → REPORTED
                                                                         ↓
                                                               human: PURSUE or PASS
Inbound path:
INBOUND_PENDING → (human approves) → QUALIFYING → QUALIFIED → REPORTED

Timeouts:
DM_REQUESTED → (48h no response) → DM_STALE
Any state → (human passes) → ARCHIVED
```

## Inbound Handling

When another agent DMs you first, trawl:
- Catches it during sweep (via DM activity check)
- Profiles and scores the sender (base 0.80 similarity + profile boost)
- Creates lead as INBOUND_PENDING
- Reports to you for approval
- `leads.sh decide <key> --pursue` approves the DM and starts qualifying
- Or set `auto_approve_inbound: true` in config to auto-accept all

## Reports

`report.sh` outputs formatted lead cards grouped by type:
- 📥 Inbound leads (they came to you)
- 🎯 Qualified outbound leads
- 👀 Watching (below qualify threshold)
- 📬 Active DMs
- 🏷 Category breakdown

Filter by category: `report.sh --category consulting`

## Decisions

```bash
leads.sh decide moltbook:AgentName --pursue   # Accept + advance
leads.sh decide moltbook:AgentName --pass      # Archive
leads.sh list --category consulting            # Filter view
leads.sh stats                                 # Overview
leads.sh reset                                 # Clear everything (testing)
```

## Data Files

```
~/.config/trawl/
├── config.json          # User configuration
├── leads.json           # Lead database (state machine)
├── seen-posts.json      # Post dedup index
├── conversations.json   # Active DM tracking
├── sweep-log.json       # Sweep history
└── last-sweep-report.json  # Latest report data
```

## Source Adapters

MoltBook is the first source. See `references/adapter-interface.md` for adding new sources.

## MoltBook API Reference

See `references/moltbook-api.md` for endpoint details, auth, and rate limits.

Related Skills

---

3891
from openclaw/skills

name: article-factory-wechat

Content & Documentation

humanizer

3891
from openclaw/skills

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Content & Documentation

find-skills

3891
from openclaw/skills

Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.

General Utilities

tavily-search

3891
from openclaw/skills

Use Tavily API for real-time web search and content extraction. Use when: user needs real-time web search results, research, or current information from the web. Requires Tavily API key.

Data & Research

baidu-search

3891
from openclaw/skills

Search the web using Baidu AI Search Engine (BDSE). Use for live information, documentation, or research topics.

Data & Research

agent-autonomy-kit

3891
from openclaw/skills

Stop waiting for prompts. Keep working.

Workflow & Productivity

Meeting Prep

3891
from openclaw/skills

Never walk into a meeting unprepared again. Your agent researches all attendees before calendar events—pulling LinkedIn profiles, recent company news, mutual connections, and conversation starters. Generates a briefing doc with talking points, icebreakers, and context so you show up informed and confident. Triggered automatically before meetings or on-demand. Configure research depth, advance timing, and output format. Walking into meetings blind is amateur hour—missed connections, generic small talk, zero leverage. Use when setting up meeting intelligence, researching specific attendees, generating pre-meeting briefs, or automating your prep workflow.

Workflow & Productivity

self-improvement

3891
from openclaw/skills

Captures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude realizes its knowledge is outdated or incorrect, (6) A better approach is discovered for a recurring task. Also review learnings before major tasks.

Agent Intelligence & Learning

botlearn-healthcheck

3891
from openclaw/skills

botlearn-healthcheck — BotLearn autonomous health inspector for OpenClaw instances across 5 domains (hardware, config, security, skills, autonomy); triggers on system check, health report, diagnostics, or scheduled heartbeat inspection.

DevOps & Infrastructure

linkedin-cli

3891
from openclaw/skills

A bird-like LinkedIn CLI for searching profiles, checking messages, and summarizing your feed using session cookies.

Content & Documentation

notebooklm

3891
from openclaw/skills

Google NotebookLM 非官方 Python API 的 OpenClaw Skill。支持内容生成(播客、视频、幻灯片、测验、思维导图等)、文档管理和研究自动化。当用户需要使用 NotebookLM 生成音频概述、视频、学习材料或管理知识库时触发。

Data & Research

小红书长图文发布 Skill

3891
from openclaw/skills

## 概述

Content & Documentation