No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is wacrawl safe to install? View the security audit →
🧾 wacrawl WhatsApp archaeology with encrypted receipts. Read-only local archive and search for the macOS WhatsApp Desktop app. copies WhatsApp Desktop's local SQLite databases into a temporary snapshot, imports the useful chat data into its own SQLite archive, and gives you scriptable commands for status, chat listing, message listing, and full-text search. It is for local inspection. It does not send messages, decrypt backups, talk to WhatsApp Web, or write back into WhatsApp's app container. Install Homebrew is the easiest path. Install directly from the OpenClaw tap: After that, upgrades stay simple: Or from source: Check the installed binary: Quick Start First, check whether can see the local WhatsApp Desktop data: Sync a fresh local archive: Inspect what was imported. Read commands sync automatically by default, so , , , and refresh the archive before reading when the local WhatsApp Desktop source is newer: Search message text: bash wacrawl search "rele
| Stars | 138 |
| Forks | 18 |
| Language | Go |
| Category | AI Tool |
| License | MIT |
| Quality Score | 66.4118451586435/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-28 |
| Created | 2026-04-25 |
| Platforms | go |
| Est. Tokens | ~11k |
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wacrawl is 🧾 WhatsApp archaeology with encrypted receipts. It is categorized as a AI Tool with 138 GitHub stars.
wacrawl is primarily written in Go.
You can find installation instructions and usage details in the wacrawl GitHub repository at github.com/steipete/wacrawl. The project has 138 stars and 18 forks, indicating an active community.
wacrawl is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to wacrawl on Agent Skills Hub include MedgeClaw, SciToolAgent, redesigned-pancake. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: