DEV Community: Lucy Green

Your Rankings Aren’t Broken—Your Technical SEO Debt Is

Lucy Green — Thu, 10 Sep 2026 11:27:07 +0000

You fixed the redirect chain, updated the sitemap, and resubmitted. Two weeks later, half your product pages are still sitting in "Discovered - currently not indexed" and a chunk of revenue-driving URLs return 404s from an old migration nobody documented.

This is the real cost of technical SEO debt: it hides in plain sight. Google quietly drops pages, crawl budget gets wasted on parameter junk, and duplicate title tags split relevance across near-identical URLs. You do not notice until traffic dips and the culprit is buried three layers deep in a crawl report.

SerpSpur Site Audit approaches this as a diagnostic pass, not a vanity score. Point it at your domain and it surfaces the issues that actually move rankings: broken internal and external links, indexing blockers like stray noindex tags and canonical conflicts, missing or duplicated metadata, thin pages that dilute topical authority, and orphaned URLs no internal link reaches.

What makes it practical is the ordering. Instead of dumping 40,000 warnings, it groups findings by impact so you fix the handful of problems affecting crawl efficiency and indexation first. Each issue comes with the affected URL, the reason it matters, and the fix. You can rerun the audit after deploying to confirm the count drops.

A useful routine: audit monthly, and always immediately after a migration, CMS upgrade, or bulk content publish. Most ranking recoveries are not new content plays. They are old technical debt finally cleared.

Run the audit on your site, fix the top ten findings, then rerun. That loop beats speculation every time.https://serpspur.com/

Stop Overpaying for SEO Tools: A Faster, Smarter Alternative to Expensive SEO Suites

Lucy Green — Tue, 08 Sep 2026 16:18:29 +0000

Tired of paying $200+ a month just to peek at your competitor’s keywords? You are not alone. The real pain point isn’t the data—it’s the bloat. Legacy SEO suites force you to navigate clunky dashboards and pay for modules you never touch, just to answer one simple question: “Why is my traffic flat?”

Here is the fix. Stop renting a legacy monolith and start using a modular, agile toolkit. SerpSpur gives you the exact metrics that matter—organic traffic volume, keyword difficulty, backlink gaps, and on-page health—without the enterprise bloat. Instead of waiting for a weekly PDF report, you get real-time site audits that flag technical issues like broken links or slow CLS scores instantly.

The actionable workflow? Run a domain audit first. SerpSpur crawls every page to expose thin content and duplicate meta tags. Then, pivot to the competitor gap analysis. Enter your top rival’s URL, filter by “keywords they rank for but you don’t,” and export that list to your content calendar. Done. No learning curve, no training videos.

Modern SEO does not require a legacy price tag. It requires speed and specificity. Pull up the live dashboard, spot the drop in branded traffic, and drill down to the exact referring page that lost its backlink.

Stop overpaying for complexity. Start analyzing with clarity. Try SerpSpur free and see your traffic blind spots today.

Google PageRank Is Dead: Stop Using Outdated Scores to Evaluate Domains

Lucy Green — Tue, 08 Sep 2026 07:52:30 +0000

Google killed the public PageRank API back in 2016, yet countless SEOs still chase that outdated 0-10 metric. You check an old directory, see a 4, and assume the domain is solid. But that number is frozen in time. It doesn't reflect current link velocity, content quality, or algorithmic penalties.

The real problem is trust. You cannot make a confident decision about a domain or competitor using a relic. You need a live signal that correlates with how Google actually evaluates authority today.

That is why we built the Google PageRank Checker with a modern twist. Instead of forcing you to interpret a dead number, it pulls the historical PageRank data for context and pairs it instantly with the SERPSpur Trust Rate. This is our proprietary, real-time score that analyzes backlink profiles, domain age, and current ranking signals.

The workflow is simple. Paste any URL, hit check, and you get a side-by-side view. See if the legacy authority matches the current reality. A high historical PageRank with a low Trust Rate exposes a decayed or penalized site. A low historical score with a high Trust Rate reveals a rising player you would otherwise ignore.

Stop guessing with outdated data. Get the historical context, but make your decision on the live metric that matters now. Check any domain at SERPSpur.https://serpspur.com/tool/google-pagerank-checker/

Stop Fighting Invoice Formats: Convert PDFs, Excel Files, and HTML to CSV Without the Headaches

Lucy Green — Thu, 06 Aug 2026 06:34:45 +0000

Anyone who's worked with financial data knows that invoice processing is rarely as simple as it sounds.

One client sends PDFs.

Another sends old Excel spreadsheets.

Someone else exports HTML reports from a legacy accounting system.

Yet your accounting software, reporting dashboard, or Python script only wants one thing: a clean CSV file.

That's where the real work begins.

Why Invoice Parsing Is Harder Than It Looks

Developers usually start by writing a parser.

For PDFs, libraries like pdfplumber, camelot, or tabula-py are popular choices.

They work well—until they don't.

PDFs aren't designed for structured data extraction. Tables often contain:

Misaligned columns
Empty cells
Broken rows
Unexpected whitespace
Missing headers

A simple extraction frequently turns into a cleanup project.

Here's a basic example using pdfplumber.

import pdfplumber
import pandas as pd

with pdfplumber.open("invoice.pdf") as pdf:
page = pdf.pages[0]
table = page.extract_table()

clean_rows = []

for row in table:
clean_rows.append([
cell.strip() if cell else ""
for cell in row
])

df = pd.DataFrame(clean_rows)

df.to_csv("output.csv", index=False)

This works for one invoice.

Now imagine processing 100 invoices from multiple clients.

Suddenly you're maintaining different parsers for:

PDF
XLS
XLSX
HTML
Different invoice layouts
Various encodings

Instead of analyzing financial data, you're debugging file formats.

A Simpler Workflow

For one-off imports and bulk migrations, I prefer converting everything into a consistent CSV format first.

Once every file looks the same, the rest of the workflow becomes much easier.

Recently I tested the Invoice to CSV Converter from SERPSpur, which supports PDF, XLS, XLSX, and HTML files without requiring custom parsing logic.

Instead of spending time cleaning extracted tables, I received a structured CSV that was immediately usable.

Working with the Converted Data

Once the invoices are converted, you can focus on analysis instead of extraction.

For example:

import pandas as pd

df = pd.read_csv("converted_invoices.csv")

df["Total"] = (
df["Total"]
.str.replace("$", "", regex=False)
.astype(float)
)

vendor_totals = (
df.groupby("Vendor")["Total"]
.sum()
.sort_values(ascending=False)
)

print(vendor_totals)

Now you can build reports, automate bookkeeping, or import everything into your accounting system with minimal effort.

Why This Approach Saves Time

Converting invoices into a standardized CSV upfront has several advantages:

One consistent format regardless of the original file type
Less custom parsing code
Fewer data-cleaning issues
Easier automation with Pandas
Faster imports into databases or accounting software

For occasional migrations, this is often much more efficient than building a parser for every document format you'll encounter.

Final Thoughts

Writing parsers can be fun.

Cleaning messy invoice exports usually isn't.

If your goal is simply to move data from invoices into a spreadsheet, database, or analytics pipeline, it's often better to normalize the files first and spend your time working with the data—not fixing extraction errors.

For developers, that means fewer edge cases, cleaner code, and a workflow that's much easier to maintain.maintain.https://serpspur.com/tool/invoice-pdf-to-csv-converter/

Stop Missing Easy Backlinks: Find Competitor Link Opportunities with Backlink Gap Analysis 🔗📈

Lucy Green — Tue, 04 Aug 2026 06:37:12 +0000

Ever feel like your competitors are getting all the good backlinks while you’re stuck scraping the bottom of the barrel? I’ve been there. You publish great content, but your domain authority crawls while theirs skyrockets. The secret isn’t always creating better content—it’s finding the links you’re missing.

That’s where a backlink gap analysis comes in. It’s the SEO equivalent of finding a cheat code. The idea is simple: find websites that link to your competitors but not to you. Those are low-hanging fruit—sites already interested in your niche.

Here’s a quick, technical way to do this without burning hours in spreadsheets.

First, you need a list of your main competitors. Let’s say you run a SaaS for project management. Your competitors might be Asana, Trello, and Monday.com.

Next, you need a tool that exports link data. While many paid tools do this, I’ve been using a free backlink gap checker from SerpSpur (https://serpspur.com/tool/backlink-gap/). It pulls the intersection of domains linking to them but not you.

Here’s a sample of what the output logic looks like in Python if you were to build this yourself:

import requests

# Pseudocode for fetching and comparing backlinks
def get_backlinks(domain):
    # API call to your favorite index
    return set(link[&#039;source&#039;] for link in api_response)

competitors = [&#039;asana.com&#039;, &#039;trello.com&#039;, &#039;monday.com&#039;]
my_domain = &#039;yourproject.com&#039;

my_links = get_backlinks(my_domain)
gap_links = set()

for comp in competitors:
    comp_links = get_backlinks(comp)
    gap_links.update(comp_links - my_links)

print(f&quot;Found {len(gap_links)} potential prospects.&quot;)

Once you have that list, filter it. Look for:

Sites with real traffic (check via SimilarWeb)
Pages that are resource lists or "best tools" roundups
Blogs that recently wrote about your niche

Then, do the outreach. The key is to personalize. Don’t say, "I noticed you linked to Trello." Instead, say, "I saw your roundup on project management tools. I’ve built a comparison table that includes Trello and Asana, but I think adding [Your Tool] would give your readers a fresh perspective since we offer [unique feature]."

That’s it. Rinse and repeat monthly. The gap shrinks, and your authority climbs.

If you want to skip the coding, just use the tool I linked above—it does the heavy lifting and exports a CSV you can filter in Google Sheets. Happy hunting.

Uncover Hidden Internal Linking Issues with a Free SEO Audit Workflow 🔍

Lucy Green — Tue, 28 Jul 2026 06:18:26 +0000

Tired of burning cash on expensive SEO suites just to run a simple competitor analysis? Me too. That’s why I built a lean workflow using the Chrome DevTools Network tab and a lightweight, free Python script to map out any page’s internal link structure—no paid subscriptions required.

Most people don’t realize the DOM alone can be misleading. JavaScript frameworks often inject placeholder links that differ from the actual href attributes rendered after hydration. The real source of truth? The network waterfall. When you scrape the fetch/XHR requests from a live page, you get the raw data that drives the site’s navigation.

Here’s a quick snippet to grab internal links from a page’s response:

import requests
from bs4 import BeautifulSoup
from urllib.parse import urlparse, urljoin

def get_internal_links(url):
    try:
        response = requests.get(url, timeout=10)
        soup = BeautifulSoup(response.text, &#039;html.parser&#039;)
        domain = urlparse(url).netloc
        internal_links = set()
        for a_tag in soup.find_all(&#039;a&#039;, href=True):
            href = a_tag[&#039;href&#039;]
            full_url = urljoin(url, href)
            parsed = urlparse(full_url)
            if parsed.netloc == domain and parsed.scheme in (&#039;http&#039;, &#039;https&#039;):
                internal_links.add(full_url)
        return list(internal_links)
    except Exception as e:
        return f&quot;Error: {e}&quot;

# Usage
url = &quot;https://example.com&quot;
links = get_internal_links(url)
print(f&quot;Found {len(links)} internal links on {url}&quot;)

This script filters out external links, ads, and social media icons. But it still misses dynamic routes loaded via API calls. To catch those, you’d hook into the Network tab using a headless browser like Playwright—but that’s a tutorial for another day.

Why does this matter? SEO audits often get stuck on homepage metrics. But internal link depth signals content priority to search engines. If your “Services” page is three clicks deep and only linked from a footer, it’s practically invisible.

I’ve been using this approach to spot orphaned pages and broken breadcrumbs on client sites. If you want to scale this into a full crawl—including backlink gaps and keyword mapping—you’ll eventually need a tool that automates the heavy lifting. That’s where SerpSpur comes in as a solid Semrush alternative. It gives you the same site audit, backlink analyzer, and keyword tracking without the enterprise price tag.

But for today, just run the script on your own site. You’ll be surprised what you find. Drop your results in the comments—I’d love to see what you uncover.

Uncover High-Value Link Opportunities with Backlink Gap Analysis 🔗📈

Lucy Green — Thu, 23 Jul 2026 10:18:10 +0000

Building backlinks is hard, but finding opportunities shouldn't be. I recently started using a backlink gap analysis tool, and it's been a game-changer.

The idea is simple: you compare your domain with competitors and find websites that link to them but not to you. These are low-hanging fruit for outreach.

Here's how I approach it. First, I enter my domain and up to five competitors into the tool. It scans their backlink profiles and highlights links unique to them.

javascript
// Example: Fetching gap data
const apiUrl = "https://serpspur.com/tool/backlink-gap/";
const params = { domain: "mysite.com", competitors: ["comp1.com", "comp2.com"] };

fetch(apiUrl, {
method: "POST",
body: JSON.stringify(params)
})
.then(response => response.json())
.then(data => {
console.log("Missing links:", data.missing_links);
});

The output lists domains linking to competitors but not me. From there, I prioritize by domain authority and relevance. For each, I craft a personalized email explaining why my content is a better fit.

This method has already earned me several high-quality backlinks. If you want to try it, the tool is free to use. Visit SERPSpur for more details.

# How I Automated Search Engine Penalty Checks to Catch SEO Issues Before They Hurt Traffic

Lucy Green — Tue, 21 Jul 2026 04:59:26 +0000

One of the most stressful moments for anyone running a website is waking up to a sudden drop in organic traffic.

I've been there.

My first instinct was to open Google Search Console, expecting to find a manual action or security warning. Everything looked normal. No errors. No notifications.

After several hours of digging through logs and deployment history, I found the culprit: a robots.txt change made during a site migration had unintentionally blocked search engines from crawling important pages.

It was a small mistake with a big impact.

That experience convinced me to stop relying on manual checks. Instead, I wanted something that would tell me immediately if a site's indexing status changed or if search engines started flagging problems.

So I put together a small Python script that checks a domain's status using SERPSpur's Search Engine Penalty Radar API.

import requests

API_KEY = "your_api_key"
DOMAIN = "example.com"

def check_penalty_status(domain):
    url = "https://api.serpspur.com/v1/penalty-radar"

    response = requests.get(
        url,
        headers={
            "Authorization": f"Bearer {API_KEY}"
        },
        params={
            "domain": domain,
            "include_blacklists": True
        }
    )

    response.raise_for_status()
    return response.json()


status = check_penalty_status(DOMAIN)

if status["indexed_pages"] == 0:
    print("⚠️ Possible deindexing detected.")

if status["blacklisted"]:
    print(f"⚠️ Listed on {len(status['blacklists'])} blacklist(s).")

The script is intentionally simple, but it's enough to monitor a few signals that matter:

Indexed page count
Blacklist status
Search engine penalty indicators

Instead of running it manually, I scheduled it with cron so it executes every morning.
https://serpspur.com/tool/search-engine-penalty-radar/

0 8 * * * /usr/bin/python3 /home/user/check_penalty.py

If something changes, I get notified immediately.

The biggest advantage isn't automation—it's shortening the feedback loop.

Without monitoring, you might not notice an indexing issue until rankings have already dropped. With an automated check, you can often spot the problem within hours.

I also like combining this with server log analysis.

If Googlebot activity suddenly disappears while indexed pages start dropping, it's usually a strong signal that something needs attention. Sometimes it's a deployment issue. Sometimes it's an accidental noindex tag. Sometimes it's a server configuration problem.

Either way, having multiple data points makes debugging much easier than relying on traffic reports alone.

This isn't meant to replace Google Search Console or other SEO tools. It's simply another layer of monitoring that has saved me more than once.

If you manage several websites, automating these kinds of health checks is one of those small improvements that pays for itself the first time it catches a problem before your rankings do.

Simplify Keyword Research with Search Volume, CPC & Difficulty

Lucy Green — Tue, 07 Jul 2026 09:34:28 +0000

Keyword research is the backbone of any SEO strategy. But with so many metrics—search volume, CPC, keyword difficulty, ads competition—it can get overwhelming. The Keyword Research Tool simplifies this by providing all these metrics in one place, filtered by country. For example, you can quickly compare 'best coffee maker' across the US and UK to see where the opportunity lies. Here's a quick Python script to fetch keyword data programmatically:

python
import requests

url = 'https://serpspur.com/tool/keyword-research-tool/'
params = {'keyword': 'best coffee maker', 'country': 'US'}
response = requests.get(url, params=params)
data = response.json()
print(f'Search Volume: {data["search_volume"]}, Difficulty: {data["difficulty"]}')

Check it out at https://serpspur.com/tool/keyword-research-tool/ to refine your keyword strategy.

How I Use Competitor Traffic Analysis to Discover New SEO Opportunities

Lucy Green — Mon, 06 Jul 2026 06:47:08 +0000

If you're serious about SEO, you need to know what your competitors are doing. The SERPSpur Traffic & Competitor Explorer makes it easy to analyze website traffic, organic keywords, and competitor performance across different search markets. I wrote a small PHP script to automate competitor analysis:

This helps me identify which keywords I'm missing and where to focus my efforts. Head over to https://serpspur.com to start exploring.

How I Verify Website Traffic Before Buying a Domain or Building Backlinks

Lucy Green — Fri, 03 Jul 2026 05:59:31 +0000

Before buying a domain on Flippa or investing in link building, I always check traffic authenticity. Fake bot traffic can ruin your ROI. I built a simple Python script to analyze user-agent patterns, but it's not enough. That's why I use the SERPSpur Bot Traffic Detector—it identifies fake bot and low-quality traffic sources, giving you confidence in your investment.

python
import re

def detect_bot_traffic(log_entry):
bot_patterns = [r'bot', r'crawler', r'spider', r'scraper']
user_agent = log_entry.get('user_agent', '')
for pattern in bot_patterns:
if re.search(pattern, user_agent, re.IGNORECASE):
return True
return False

Example log entry

log = {'user_agent': 'Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)'}
print('Bot detected' if detect_bot_traffic(log) else 'Human traffic')

This snippet is a starting point, but the tool does a triple-signal audit for accuracy. Check it out: https://serpspur.com/tool/bot-traffic-detector/

Automating SEO Audits with Python (and Taking Them Further)

Lucy Green — Sat, 27 Jun 2026 04:37:56 +0000

If you've ever managed more than a handful of websites, you've probably spent far too much time checking the same things over and over again:

Is the title tag missing?
Does every page have a meta description?
Are images missing alt attributes?
Is there exactly one <h1>?
Are canonical tags configured correctly?

Doing this manually gets old fast.

I wanted a lightweight way to catch the obvious issues before running a full SEO audit, so I put together a simple Python script that scans a page and reports common on-page problems.

Install the Dependencies

pip install requests beautifulsoup4

Basic SEO Audit Script

import requests
from bs4 import BeautifulSoup

def audit_page(url):
    response = requests.get(url, timeout=10)
    soup = BeautifulSoup(response.text, "html.parser")

    issues = []

    title = soup.title.string.strip() if soup.title and soup.title.string else ""
    if not title:
        issues.append("Missing title tag")
    elif len(title) > 60:
        issues.append("Title exceeds recommended length")

    description = soup.find("meta", attrs={"name": "description"})
    if not description or not description.get("content"):
        issues.append("Missing meta description")

    h1_tags = soup.find_all("h1")
    if len(h1_tags) == 0:
        issues.append("Missing H1")
    elif len(h1_tags) > 1:
        issues.append("Multiple H1 tags detected")

    images = soup.find_all("img")
    missing_alt = [img.get("src") for img in images if not img.get("alt")]

    if missing_alt:
        issues.append(f"{len(missing_alt)} images missing alt text")

    canonical = soup.find("link", rel="canonical")
    if not canonical:
        issues.append("Missing canonical tag")

    return issues

url = "https://example.com"

for issue in audit_page(url):
    print("-", issue)

For quick technical checks, this script works surprisingly well.

But after using it for client projects, I realized something important:

An on-page audit is only one piece of the puzzle.

SEO Doesn't Stop at HTML

Even when every page has perfect metadata, rankings can still suffer because of:

Weak backlink profiles
Poor keyword targeting
Slow Core Web Vitals
Indexing issues
Broken internal links
Missing structured data
Competitor content gaps

Checking all of those manually quickly becomes overwhelming.

Extending the Workflow

Instead of writing separate scripts for every SEO task, I now use this script as the first validation step and then follow it with a more comprehensive audit using SERPSpur.

It gives me additional insights such as:

On-page SEO analysis
Backlink reports
Keyword research
Competitor comparisons
Domain authority metrics
Rank tracking
Technical SEO diagnostics

That means I can identify both HTML-level issues and broader SEO problems from a single workflow.

Ideas for Improvements

If you're extending this script, here are a few useful additions:

Validate Open Graph tags
Check Twitter Card metadata
Detect broken internal links
Verify structured data (JSON-LD)
Audit robots meta directives
Measure page response time
Detect duplicate meta descriptions
Export results as CSV
Crawl an entire sitemap
Generate HTML reports

Final Thoughts

Automation won't replace a proper SEO strategy, but it can eliminate a lot of repetitive work.

A simple Python script is perfect for catching common on-page issues before they become bigger problems. Pairing it with a comprehensive SEO platform lets you move beyond HTML validation and understand how your site performs in search.

Whether you're maintaining your own projects or auditing client websites, combining lightweight automation with a full SEO toolkit can save hours every week.

If you're looking for an all-in-one SEO platform to complement your own scripts, take a look at SERPSpur: https://serpspur.com