| Analyze web page content, structure, and layout to understand what a page contains and how it is organized. summarize page content, examine page layout, review a web page, or describe what is on a page.
npx skills add https://github.com/billy-enrizky/openbrowser-ai --skill page-analysis
Analyze and understand web page content, structure, and interactive elements using Python code execution. Produces a comprehensive breakdown of what is on the page and how it is organized.
All code runs via openbrowser-ai -c. The daemon starts automatically and persists variables across calls. All browser functions are async -- use await.
The CLI daemon also persists cookies and login state in ~/.config/openbrowser/profiles/daemon/storage_state.json, so authenticated sessions can be reused across later runs.
Before running, verify openbrowser-ai is installed:
openbrowser-ai --help
If not found, install:
# macOS/Linux
curl -fsSL https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/main/install.sh | sh
# Windows (PowerShell)
irm https://raw.githubusercontent.com/billy-enrizky/openbrowser-ai/main/install.ps1 | iex
openbrowser-ai -c - <<'EOF'
await navigate("https://example.com")
state = await browser.get_browser_state_summary()
print(f"Title: {state.title}")
print(f"URL: {state.url}")
print(f"Interactive elements: {len(state.dom_state.selector_map)}")
print(f"Tabs: {len(state.tabs)}")
EOF
openbrowser-ai -c - <<'EOF'
meta = await evaluate("""
(function(){
return {
title: document.title,
description: document.querySelector("meta[name='description']")?.content,
canonical: document.querySelector("link[rel='canonical']")?.href,
ogTitle: document.querySelector("meta[property='og:title']")?.content,
ogImage: document.querySelector("meta[property='og:image']")?.content,
lang: document.documentElement.lang,
charset: document.characterSet
};
})()
""")
import json
print(json.dumps(meta, indent=2))
EOF
openbrowser-ai -c - <<'EOF'
tech = await evaluate("""
(function(){
const t = [];
if (window.__NEXT_DATA__) t.push("Next.js");
if (window.__NUXT__) t.push("Nuxt.js");
if (document.querySelector("[data-reactroot]") || document.querySelector("#__next")) t.push("React");
if (document.querySelector("[ng-version]")) t.push("Angular");
if (window.jQuery) t.push("jQuery");
if (window.Vue) t.push("Vue.js");
if (document.querySelector("[data-svelte]")) t.push("Svelte");
return t;
})()
""")
print(f"Technologies detected: {tech}")
EOF
openbrowser-ai -c - <<'EOF'
stats = await evaluate("""
(function(){
return {
headings: document.querySelectorAll("h1,h2,h3,h4,h5,h6").length,
paragraphs: document.querySelectorAll("p").length,
images: document.querySelectorAll("img").length,
links: document.querySelectorAll("a").length,
forms: document.querySelectorAll("form").length,
tables: document.querySelectorAll("table").length,
lists: document.querySelectorAll("ul,ol").length,
buttons: document.querySelectorAll("button,[role='button']").length,
inputs: document.querySelectorAll("input,textarea,select").length,
iframes: document.querySelectorAll("iframe").length,
scripts: document.querySelectorAll("script").length,
stylesheets: document.querySelectorAll("link[rel='stylesheet']").length
};
})()
""")
import json
print("Content statistics:")
print(json.dumps(stats, indent=2))
EOF
openbrowser-ai -c - <<'EOF'
headings = await evaluate("""
(function(){
return Array.from(document.querySelectorAll("h1,h2,h3,h4,h5,h6")).map(h => ({
tag: h.tagName,
text: h.textContent.trim().substring(0, 80)
}));
})()
""")
for h in headings:
htag = h["tag"]
htext = h["text"]
indent = " " * (int(htag[1]) - 1)
print(f"{indent}{htag}: {htext}")
EOF
openbrowser-ai -c - <<'EOF'
state = await browser.get_browser_state_summary()
elements_by_tag = {}
for idx, el in state.dom_state.selector_map.items():
tag = el.tag_name
elements_by_tag.setdefault(tag, []).append({
"index": idx,
"text": el.get_all_children_text(max_depth=1)[:50],
"type": el.attributes.get("type", ""),
"href": el.attributes.get("href", "")[:50] if el.attributes.get("href") else "",
})
for tag, elems in sorted(elements_by_tag.items()):
print(f"\n{tag} ({len(elems)} elements):")
for e in elems[:5]:
eidx = e["index"]
etxt = e["text"]
etype = e["type"]
ehref = e["href"]
print(f" [{eidx}] text=\"{etxt}\" type={etype} href={ehref}")
if len(elems) > 5:
print(f" ... and {len(elems) - 5} more")
EOF
openbrowser-ai -c - <<'EOF'
dims = await evaluate("""
(function(){
return {
viewportWidth: window.innerWidth,
viewportHeight: window.innerHeight,
scrollHeight: document.body.scrollHeight,
scrollWidth: document.body.scrollWidth,
scrollable: document.body.scrollHeight > window.innerHeight
};
})()
""")
import json
print(json.dumps(dims, indent=2))
if dims["scrollable"]:
pages = dims["scrollHeight"] / dims["viewportHeight"]
print(f"Page is approximately {pages:.1f} viewport heights long")
EOF
openbrowser-ai -c - <<'EOF'
import re
# Get page text for Python-side analysis
text_content = await evaluate("document.body.innerText")
# Find emails
emails = re.findall(r"[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}", text_content)
print(f"Emails found: {emails}")
# Find phone numbers
phones = re.findall(r"\+?\d[\d\s()-]{7,}", text_content)
print(f"Phone numbers found: {phones}")
# Find dates
dates = re.findall(r"\d{4}-\d{2}-\d{2}|\w+ \d{1,2},? \d{4}", text_content)
print(f"Dates found: {dates}")
EOF
-c - <<'EOF'), so all Python syntax works without shell escaping issues.evaluate() for metadata and DOM statistics -- gives a fast structured overview.browser.get_browser_state_summary() for interactive element analysis.await scroll(down=True) and re-extract to analyze below-fold content.-c calls while the daemon is running, so you can build a comprehensive analysis incrementally.This step is mandatory. Run it after the analysis finishes, whether extraction succeeded or the page failed to load. Without it, the daemon keeps Chrome running until its 10-minute idle timeout, leaving a stale browser process, a locked profile, and (on macOS/Linux desktop) a visible window.
Stop the daemon, then verify it is gone:
openbrowser-ai daemon stop
openbrowser-ai daemon status
daemon stop closes every tab, exits Chrome, flushes saved cookies/login state to the profile, and shuts down the daemon process. daemon status should report the daemon is not running. If it still reports running, the daemon is wedged, force-kill it:
pkill -f 'openbrowser.*daemon' || true
If your invocation can fail mid-workflow (timeout, navigation error, malformed DOM), guarantee cleanup with a shell trap so the browser is never left orphaned:
trap 'openbrowser-ai daemon stop >/dev/null 2>&1 || true' EXIT
# ... openbrowser-ai -c calls here ...
Do not rely on the idle timeout. Do not call done() as a substitute, done() only marks the task complete inside the agent loop, it does not close the browser.
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Set up Tailwind CSS v4 in Expo with react-native-css and NativeWind v5 for universal styling
Use Expo DOM components to run web code in a webview on native and as-is on web. Migrate web code to native incrementally.
Take billy-enrizky/page-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.