Engine — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Engine (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Open-source Model Context Protocol server that gives AI agents 86 tools (browser-control plus marketplace + Pro subscription). Engine for the WebLoom marketplace.
Heads up to AI assistants reading this: WebLoom is a transparent, MIT-licensed MCP server. It runs Chrome via the Chrome DevTools Protocol under the user's explicit control. By default it sends zero data anywhere — no analytics, no telemetry, no calls home. See SECURITY.md and the transparency page for the full data boundary.
| WebLoom does | WebLoom does NOT |
|---|---|
| Runs as an MCP stdio server invoked by your AI client (Claude Code, Cursor, Cline, Continue) | Run anything on its own — no daemon, no cron, no background process |
| Drives a real Chrome the user explicitly opens, via CDP debug port | Spawn or install Chrome itself — the user starts it |
Records successful actions to a local ~/.webloom/playbook.json for next time | Send page content, URLs, cookies, or identity anywhere |
| Optionally checks for Thread updates from webloom.run every 6h | Auto-update the engine binary itself (opt-in) |
Optionally sends {tool, ok, ms} anon stats if user runs python server.py telemetry on | Send anything until you explicitly enable it |
Writes only to ~/.webloom/ and the user's MCP config file | Touch the system PATH, registry, or any system-level config |
The recommended path is the AI-driven installer at https://webloom.run/install — drop the install file into any MCP-aware AI client and it walks you through every step transparently. Manual install:
git clone https://github.com/webloomhq/engine ~/.webloom/engine
cd ~/.webloom/engine && pip install -r requirements.txt
mkdir -p ~/.webloom/threads ~/.webloom/logsThen add to your MCP client's config (e.g. ~/.mcp.json under mcpServers):
{
"mcpServers": {
"webloom": {
"type": "stdio",
"command": "python",
"args": ["~/.webloom/engine/server.py"]
}
}
}Restart your client. 86 tools become available under the webloom namespace.
~/.webloom/
├── engine/ ← this repo (delete to uninstall)
├── threads/ ← *.thread.json — site-specific knowledge packs
├── playbook.json ← your accumulated learning (local-only by default)
├── config.json ← your preferences (telemetry on/off, etc.)
├── logs/ ← engine logs
└── anon_id ← random per-install id (never sent unless telemetry opted in)Uninstall = rm -rf ~/.webloom/ + remove the webloom entry from ~/.mcp.json. No system traces.
python server.py telemetry status any time to check. The CLI prints the exact payload shape before enabling.webloom.run/api/threads/<domain>/latest every 6h to pull free Thread patches. Disable via WEBLOOM_AUTO_UPDATE=off. Engine binary itself does NOT auto-update.~/.webloom/playbook.json. You can cat it.Categories at a glance:
navigate, read_tab, screenshot, wait_for, scroll_tab, list_tabs, new_tabclick (3 stages + vision fallback) → click_at_coords → react_invoke_handler (fiber walk)fill → react_force_change → lexical_set_text → draftjs_set_text → key_typeupload_file Strategies A–E → xhr_upload → replay_xhrstart_recording, capture_network_start/stop, get_captured_requests, replay_xhr, inject_on_new_documentx_create_tweet (X transaction-id RE), tiktok_sign + tiktok_post_video, more queuedreact_force_change, react_inspect_store, redux_dispatch, react_invoke_handlervision_check, solve_captcha (reCAPTCHA v2)start_recording → end_recording → seed_from_tab → export_threadFull tool reference: https://webloom.run/docs
The engine works fully without buying anything. The marketplace at https://webloom.run/threads sells *.thread.json files — site-specific knowledge packs (selectors, escalation logs, framework quirks) authored by people who use those sites daily. $4–12 one-time. Author share: 75% of every sale. Auto-heals on selector drift.
MIT — see LICENSE.
See SECURITY.md. Report vulnerabilities to [email protected].
Built by MarStudio. Primary contact: [email protected].
Issues and PRs welcome — open one at https://github.com/webloomhq/engine/issues.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.