Skills browser-cdp

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/0xcjl/browser-cdp" ~/.claude/skills/clawdbot-skills-browser-cdp && rm -rf "$T"

manifest: skills/0xcjl/browser-cdp/SKILL.md

source content

What is browser-cdp?

browser-cdp connects directly to your local Chrome via Chrome DevTools Protocol (CDP), giving the AI agent:

Full login state — your cookies and sessions are carried through
Anti-bot bypass — pages that block static fetchers (search results, video platforms)
Interactive operations — click, fill forms, scroll, drag, file upload
Dynamic content extraction — read JavaScript-rendered DOM
Screenshots — capture any page at any point

Architecture

Chrome (remote-debugging-port=9222)
    ↓ CDP WebSocket
CDP Proxy (cdp-proxy.mjs) — HTTP API on localhost:3456
    ↓ HTTP REST
OpenClaw AI Agent

Setup

1. Start Chrome with debugging port

# macOS — must use full binary path (not `open -a`)
pkill -9 "Google Chrome"; sleep 2
"/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" \
  --remote-debugging-port=9222 \
  --user-data-dir=/tmp/chrome-debug-profile \
  --no-first-run &

Verify:

curl -s http://127.0.0.1:9222/json/version

2. Start CDP Proxy

node ~/.openclaw/skills/browser-cdp/scripts/cdp-proxy.mjs &
sleep 3
curl -s http://localhost:3456/health
# {"status":"ok","connected":true,"sessions":0,"chromePort":9222}

API Reference

# List all tabs
curl -s http://localhost:3456/targets

# Open URL in new tab
curl -s "http://localhost:3456/new?url=https://example.com"

# Execute JavaScript
curl -s -X POST "http://localhost:3456/eval?target=TARGET_ID" \
  -d 'document.title'

# JS click (fast, preferred)
curl -s -X POST "http://localhost:3456/click?target=TARGET_ID" \
  -d 'button.submit'

# Real mouse click
curl -s -X POST "http://localhost:3456/clickAt?target=TARGET_ID" \
  -d '.upload-btn'

# Screenshot
curl -s "http://localhost:3456/screenshot?target=TARGET_ID&file=/tmp/shot.png"

# Scroll (lazy loading)
curl -s "http://localhost:3456/scroll?target=TARGET_ID&direction=bottom"

# Navigate
curl -s "http://localhost:3456/navigate?target=TARGET_ID&url=https://..."

# Close tab
curl -s "http://localhost:3456/close?target=TARGET_ID"

Tool Selection: Three-Layer Strategy

Scenario	Use	Reason
Public pages (GitHub, Wikipedia, blogs)	`agent-reach`	Fast, low token, structured
Search results (Bing/Google/YouTube)	`browser-cdp`	agent-reach blocked
Login-gated content	`browser-cdp`	No cookies in agent-reach
JS-rendered pages	`browser-cdp`	Reads rendered DOM
Simple automation, isolated screenshots	`agent-browser`	No Chrome setup
Large-scale parallel scraping	`agent-reach` + parallel	browser-cdp gets rate-limited

Decision flow:

Public content → agent-reach (fast, cheap)
Search results / blocked → browser-cdp
Still fails → agent-reach fallback + record in site-patterns

Known Limitations

Chrome must use a separate profile (
```
/tmp/chrome-debug-profile
```
)
Same-site parallel tabs may get rate-limited
Node.js 22+ required (native WebSocket)
macOS: use full binary path to start Chrome, not
```
open -a
```

Site Patterns & Usage Log

~/.openclaw/skills/browser-cdp/references/site-patterns/   # per-domain experience
~/.openclaw/skills/browser-cdp/references/usage-log.md    # per-use tracking

Origin

Adapted from eze-is/web-access (MIT) for OpenClaw. A bug in the original (

require()

in ES module, reported here) is fixed in this version.