Why My First PageSpeed Bot Missed 30% of Slow Pages – The 6‑Step Fix I Built in Python
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I was staring at my inbox on a rainy Saturday in Gulshan, Dhaka. A new client – a boutique tea‑shop that sells on Daraz – had just pinged me. Their WordPress store was loading in 9.8 seconds on a 3G connection, and the Google PageSpeed Insights score was a pitiful 42. They threatened to drop the site if I didn’t shave off at least 3 seconds within a week. My rate was $25 hour, and I had 12 hours left before the deadline. Panic? Sure. But also a spark: could I automate the audit so I never missed a slow page again?
The Hidden Problem
Most freelancers treat a PageSpeed audit as a one‑off checklist. Run Lighthouse, copy the suggestions, hand‑write a report, send it. That works for a single site, but it collapses when you juggle 5‑10 clients, each with dozens of URLs, localized assets, and flaky Bangladesh network conditions. The hidden problem is two‑fold:
- Manual audits are slow, error‑prone, and blind to incremental regressions.
- Most bots scrape only the homepage, ignoring category pages, product pages, and pagination that actually drive revenue.
My first bot was built in a hurry. It called Lighthouse on the homepage, parsed the JSON, emailed the client. It missed 30 % of the slow pages because I never told it to crawl the whole site. The result? The client’s conversion dropped another 12 % before I could fix it.
Technical Breakdown & Logic Flow
To stop repeating that mistake I designed a pipeline that does three things:
- Discovers every public URL on a domain (crawling with depth‑limit).
- Runs Lighthouse in headless Chrome for each URL, respecting Bangladesh’s typical 3G/4G speeds (set via
--preset=mobileand custom throttling). - Aggregates the results, flags any page below a threshold, and sends a templated email to the client with a one‑click “fix‑request” button.
Why not use an off‑the‑shelf SaaS? Those tools charge per scan and often block repeated runs from the same IP – a nightmare when you’re on a home broadband plan that costs BDT 1,200 a month. A self‑hosted Python script runs on my cheap Linode (just $5 / month) and gives me full control over throttling, concurrency, and storage.
Step‑by‑Step Logic
1. Seed URL collection: Start with https://example.com. Use requests to fetch the HTML, BeautifulSoup to extract a[href] links, normalize them, and store unique URLs in a set. Enforce a depth of 2 to catch category and product pages without crawling the entire blog.
2. URL filtering: Drop URLs that end with common static assets (.jpg, .png, .pdf) and any that contain query strings known to be tracking parameters. This keeps the run fast and cheap.
3. Lighthouse execution: Spawn a subprocess running chrome --headless --disable-gpu --remote-debugging-port=9222 and invoke lighthouse via node. Pass --output=json and --output-path=./reports/{hash}.json. Capture the performance.score and the first-contentful-paint metric.
4. Result aggregation: Load each JSON, pull the metrics, and push them into a SQLite DB. The schema is tiny: url TEXT PRIMARY KEY, score REAL, fcp REAL, timestamp DATETIME.
5. Threshold evaluation: Query the DB for any row where score < 0.80 OR fcp > 3.5 (seconds). Those are the pages I need to flag.
6. Email dispatch: Use smtplib to send a nicely formatted HTML email. The body lists each failing URL, its score, and a button that links to a private Trello board where the client can add a “fix” comment. The button is just a mailto: link with a pre‑filled subject – no extra services needed.
Code/Implementation
Below is the full script. I kept it in a single file for simplicity, but in production I split it into modules and added unit tests.
#!/usr/bin/env python3
import os, sys, json, hashlib, subprocess, sqlite3, smtplib, datetime
from urllib.parse import urljoin, urlparse
import requests
from bs4 import BeautifulSoup
# ---------- CONFIG ----------
BASE_URL = "https://myclientshop.com"
MAX_DEPTH = 2
THROTTLE_SCORE = 0.80
THROTTLE_FCP = 3.5 # seconds
DB_PATH = "page_speed.db"
SMTP_SERVER = "smtp.gmail.com"
SMTP_PORT = 587
SMTP_USER = os.getenv('SMTP_USER')
SMTP_PASS = os.getenv('SMTP_PASS')
FROM_EMAIL = SMTP_USER
TO_EMAIL = "client@example.com"
# ---------------------------
def init_db():
conn = sqlite3.connect(DB_PATH)
cur = conn.cursor()
cur.execute('''CREATE TABLE IF NOT EXISTS results (
url TEXT PRIMARY KEY,
score REAL,
fcp REAL,
scanned_at TEXT
)''')
conn.commit()
return conn
def crawl(seed, depth=0, visited=None):
if visited is None:
visited = set()
if depth > MAX_DEPTH:
return visited
try:
resp = requests.get(seed, timeout=10)
resp.raise_for_status()
except Exception as e:
print(f"Failed to fetch {seed}: {e}")
return visited
visited.add(seed)
soup = BeautifulSoup(resp.text, "html.parser")
for link in soup.find_all('a', href=True):
href = link['href']
# Resolve relative URLs
full_url = urljoin(seed, href)
# Strip fragments
full_url = full_url.split('#')[0]
# Simple filter: same domain, not static asset
if urlparse(full_url).netloc != urlparse(BASE_URL).netloc:
continue
if any(full_url.lower().endswith(ext) for ext in ['.jpg','.png','.gif','.pdf']):
continue
if full_url not in visited:
crawl(full_url, depth+1, visited)
return visited
def run_lighthouse(url):
# Hash the URL to generate a stable filename
url_hash = hashlib.sha256(url.encode()).hexdigest()[:8]
out_path = f"reports/{url_hash}.json"
cmd = [
"node", "./node_modules/.bin/lighthouse",
url,
"--output=json",
f"--output-path={out_path}",
"--preset=mobile",
"--quiet",
"--chrome-flags=--headless"
]
try:
subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
with open(out_path) as f:
data = json.load(f)
perf = data['categories']['performance']['score']
fcp = data['audits']['first-contentful-paint']['numericValue'] / 1000 # ms to s
return perf, fcp
except subprocess.CalledProcessError as e:
print(f"Lighthouse failed for {url}: {e}")
return None, None
except Exception as e:
print(f"Parsing error for {url}: {e}")
return None, None
def store_result(conn, url, score, fcp):
cur = conn.cursor()
cur.execute('''INSERT OR REPLACE INTO results (url, score, fcp, scanned_at)
VALUES (?,?,?,?)''',
(url, score, fcp, datetime.datetime.utcnow().isoformat()))
conn.commit()
def fetch_issues(conn):
cur = conn.cursor()
cur.execute('''SELECT url, score, fcp FROM results
WHERE score < ? OR fcp > ?''',
(THROTTLE_SCORE, THROTTLE_FCP))
return cur.fetchall()
def send_email(issues):
if not issues:
print("No issues to report.")
return
body = "PageSpeed Audit – Issues Detected
"
for url, score, fcp in issues:
body += f"- {url} – Score: {score:.2f}, FCP: {fcp:.2f}s "
body += f"Request Fix
"
body += "
"
msg = f"From: {FROM_EMAIL}\nTo: {TO_EMAIL}\nSubject: PageSpeed Audit Report\nContent-Type: text/html\n\n{body}"
with smtplib.SMTP(SMTP_SERVER, SMTP_PORT) as server:
server.starttls()
server.login(SMTP_USER, SMTP_PASS)
server.sendmail(FROM_EMAIL, TO_EMAIL, msg)
print("Email sent.")
def main():
os.makedirs('reports', exist_ok=True)
conn = init_db()
urls = crawl(BASE_URL)
print(f"Discovered {len(urls)} URLs.")
for u in urls:
score, fcp = run_lighthouse(u)
if score is not None:
store_result(conn, u, score, fcp)
issues = fetch_issues(conn)
send_email(issues)
if __name__ == "__main__":
main()
Notice the choices I made:
- I used
node_modules/.bin/lighthousedirectly to avoid a global install – keeps the CI container tidy. - SQLite is sufficient for a handful of clients; no need for a heavy Postgres instance.
- Instead of a fancy HTML templating engine I built the email body with string interpolation – less overhead.
Business Application
When I ran the pipeline for the tea‑shop, it uncovered 27 URLs under the /product/ path that were all below 0.70. Those pages were the top‑selling items. I sent the client the email, they approved a quick image‑lazy‑load fix, and within 48 hours the average score jumped to 0.84 and sales rose by 9 % – roughly BDT 150 k extra revenue. Because the audit was automated, I could schedule it nightly on a cheap cron job and bill the client a flat $150 per month for “performance monitoring”. That turned a one‑off $300 job into a recurring stream.
Common Pitfalls & Edge Cases
1. Rate‑limiting on the client’s server: Some Bangladeshi hosts block rapid requests. I added a 500 ms sleep between each Lighthouse run and respected robots.txt. If you hit a 429, back off exponentially.
2. Dynamic routes that require authentication: My script skips pages behind a login. For SaaS clients I inject a pre‑login step using puppeteer to obtain a session cookie and reuse it in Lighthouse via --extra-headers.
3. False positives from CDN‑cached assets: A page may score low because a third‑party script is slow in Bangladesh but fast elsewhere. I added a --disable-device-emulation flag to isolate network‑only throttling, then manually inspected the offending script.
Counterintuitive Insight
Running Lighthouse on every URL is slower than you think – the real bottleneck is Chrome startup. By reusing a single headless Chrome instance via the chrome-remote-interface library I cut total runtime from 45 minutes to 12 minutes for a 50‑page site. The trick is to start Chrome once, keep the debugging port open, and send multiple Page.navigate commands. I haven’t published that snippet yet because it’s a bit messy, but it saved me dozens of dollars on my Linode bill.
Conclusion & CTA
If you’re still manually copying Lighthouse scores into Google Docs, you’re leaving money on the table. Build a tiny audit pipeline like the one above, schedule it, and watch client satisfaction – and your recurring revenue – climb.
Action step: Clone the repo, replaceBASE_URLwith your own client’s domain, runpython3 audit.py, and send the first automated email today.
Did you run into a weird edge case? Did the bot miss a page you thought was critical? Drop a comment below, share your experience, and let’s iterate together. Also, check out my other guides on automating SEO checks and PWA offline‑first conversions on aitipseveryday.com.
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