DEV Community: Emma Watson

How I Turned a 50+ Domain Backlink Audit Into a 30-Minute Workflow

Emma Watson — Sat, 08 Aug 2026 05:36:34 +0000

Backlink analysis is one of those tasks that seems simple until you need to do it at scale. I recently needed to export backlink data for 50+ domains to identify link-building opportunities for a client, and manually copying data from various tools was painfully slow.

That's when I discovered the Bulk Backlink Exporter from SERPSpur. It lets you pull comprehensive backlink data in one go, which is perfect for competitive analysis or portfolio-wide audits.

The key feature is the ability to organize data by domain, anchor text, and link type—all exportable to CSV for further processing. Here's how I structured my analysis pipeline:

python
import pandas as pd

df = pd.read_csv('backlinks_export.csv')

Filter for high-value links

df['domain_authority'] = df['domain_authority'].astype(int)
high_value = df[df['domain_authority'] > 50]

Group by target domain

summary = high_value.groupby('target_domain').agg(
total_links=('url', 'count'),
avg_authority=('domain_authority', 'mean')
).reset_index()

print(summary.head(10))

This approach let me quickly identify which competitor domains had the strongest link profiles and where the gaps were in our own strategy.

One tip: when exporting large datasets, always filter by date range first. The tool supports this, and it saves you from processing irrelevant historical data that could skew your analysis.

I also found it useful for spotting toxic backlinks across multiple domains at once. By exporting everything and running a simple script to flag suspicious anchors, I could prioritize disavow actions without manually scanning each domain.

For anyone managing multiple sites or doing agency work, this kind of bulk export is a game-changer. It turns a weekend project into a 30-minute task.

How do you handle large-scale backlink audits? I'm always looking for ways to streamline the process further.

Bulk Backlink Exports with Python: Stop Clicking "Next" and Automate Your SEO Workflow

Emma Watson — Wed, 05 Aug 2026 05:33:29 +0000

If you've ever managed backlink audits for multiple websites, you've probably experienced the same frustration.

Most SEO dashboards let you export a few hundred backlinks at a time. That's fine for a quick overview, but it's nowhere near enough for serious analysis. Large websites can have tens of thousands of backlinks, and manually exporting page after page quickly becomes a tedious process.

A while ago, I got tired of downloading dozens of CSV files, merging them together, and cleaning everything in Excel before I could even begin my analysis.

So I automated the entire workflow with Python.

The Problem with Manual Exports

Most backlink tools are designed around their web interface.

That usually means:

Exporting limited rows per download
Clicking through multiple pages
Combining several CSV files
Cleaning inconsistent data formats
Repeating the process for every client

When you're handling multiple projects, this can easily consume hours every week.

Instead of working with backlink data, you're spending your time collecting it.

Automating the Process

The solution was surprisingly straightforward.

Rather than interacting with the dashboard, I queried structured backlink data programmatically and wrote everything directly into a single CSV file.

The exact provider isn't important—the workflow works with any SEO platform that exposes backlink data through an API.

Here's a simplified example.

import requests
import csv

API_KEY = "your_key_here"

DOMAINS = [
"example.com",
"client2.org",
"client3.net"
]

url = "https://serpspur.com/api/v1/bulk-backlinks"

with open("backlinks.csv", "w", newline="", encoding="utf-8") as file:
writer = csv.writer(file)

writer.writerow([
    "Domain",
    "Source URL",
    "Anchor Text",
    "Authority",
    "Status"
])

for domain in DOMAINS:

    params = {
        "api_key": API_KEY,
        "domain": domain,
        "limit": 5000
    }

    response = requests.get(url, params=params).json()

    for backlink in response["data"]:

        writer.writerow([
            domain,
            backlink["source"],
            backlink["anchor"],
            backlink["authority"],
            backlink["status"]
        ])

Instead of downloading dozens of files manually, the script exports everything into one clean dataset.

Why This Saves So Much Time

Once the backlinks are in CSV format, the real work begins.

I can instantly:

Filter only dofollow links
Sort by authority score
Find duplicate backlinks
Analyze anchor text distribution
Detect suspicious link patterns
Build outreach prospect lists

Because every project follows the same structure, the data is much easier to process with Python.

For example:

import pandas as pd

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

quality_links = df[
(df["Authority"] > 40) &
(df["Status"] == "dofollow")
]

print(quality_links.head())

With just a few lines of code, you can create a high-quality outreach list instead of scrolling through thousands of rows manually.

Don't Forget Pagination

The sample above assumes everything fits into one response.

In production, many APIs paginate large datasets.

A more scalable solution loops through each page until no additional results are returned.

Something like:

page = 1

while True:

params = {
    "domain": domain,
    "page": page
}

response = requests.get(url, params=params).json()

if not response["data"]:
    break

# Process backlinks...

page += 1

Pagination makes your exporter work equally well for websites with a few hundred backlinks or hundreds of thousands.

Taking It Further

Once you've collected backlink data, you can automate even more tasks:

Generate toxicity reports
Detect lost backlinks
Compare competitors
Monitor link growth over time
Build outreach opportunities
Create monthly SEO reports automatically

At that point, Python becomes far more valuable than another spreadsheet.

Final Thoughts

SEO isn't just about collecting data—it's about making that data useful.

Automating repetitive tasks gives you more time to focus on strategy instead of clicking export buttons all afternoon.

I built this workflow around SERPSpur's Bulk Backlink Exporter, but the overall approach works with any platform that provides structured backlink data through an API.

If you're still downloading backlink reports one page at a time, it's probably time to automate the process. Your future self—and your clients—will appreciate it.

Keyword Research Tool: Find High-Traffic, Low-Competition Keywords

Emma Watson — Mon, 03 Aug 2026 06:03:23 +0000

Keyword research is one of those tasks that seems simple until you actually try to do it properly. I've been refining my workflow recently, and the biggest game-changer was looking beyond just search volume. You really need to consider keyword difficulty, CPC, and ads competition to get a full picture. I built a small script that pulls data from multiple sources and aggregates it into a single CSV. The logic is straightforward: I query for a seed keyword, then expand it with long-tail variations, and finally score each one based on difficulty and potential ROI. The tricky part is filtering by country—search intent varies so much across markets. I found that grouping keywords by country and comparing the metrics side-by-side reveals opportunities you'd miss otherwise. If you want to skip the coding, SERPSpur's Keyword Research Tool does all of this in a few clicks. It gives you search volume, CPC, keyword difficulty, and ads competition for any country. Have you found any particular metric to be more reliable than others when evaluating keywords?https://serpspur.com/tool/keyword-research-tool/

Stop Guessing Domain Quality—Check Trust Signals Before You Build Links

Emma Watson — Sat, 01 Aug 2026 09:24:06 +0000

When I'm evaluating a domain for a project, I don't just look at backlinks. I want to know if the site is actually trustworthy from a technical standpoint. That means checking security headers, SSL configuration, and other behind-the-scenes signals that Google might use. Recently, I started using a dedicated trust rate checker to automate this process.

The SerpSpur Trust Rate Checker does a deep dive into a domain's technical reputation. It analyzes things like HSTS, content security policy, and domain age to give you a single score. This is incredibly useful when you're vetting a potential link partner or buying an expired domain. You can instantly see if the site has been neglected or if it's been maintained with security best practices.

Here's a quick script I use to batch-check multiple domains:

bash

!/bin/bash

Loop through a list of domains and check trust rates

for domain in $(cat domains.txt); do
echo "Checking $domain..."
curl -s "https://api.serpspur.com/trust-rate?domain=$domain" | jq '.score'
done

This saves me from manually visiting each site and inspecting the headers. It's a huge time-saver for outreach campaigns. If you're building a list of high-quality prospects, it's worth running them through this tool first. You can avoid wasting time on domains that have poor technical health. Try it yourself: https://serpspur.com/tool/serpspur-trust-rate-checker/

Find Available Domain Names Faster with an Instant Domain Suggestion Checker 🌐

Emma Watson — Mon, 27 Jul 2026 10:01:32 +0000

I'm always looking for domain names for new projects, and checking availability one by one on registrars is tedious. I found a Domain Suggestion Checker that lets you check availability instantly and even suggests alternatives. It's great for brainstorming – you type in a keyword, and it shows what's available. For automation, I wrote a small script to batch check names from a list:

python
import requests

def check_domain(domain):
url = f'https://serpspur.com/tool/domain-sugesstion-checker/?domain={domain}'
resp = requests.get(url)
if 'available' in resp.text.lower():
return f'{domain} is available'
return f'{domain} is taken'

domains = ['mycoolidea.com', 'mycoolidea.net', 'mycoolidea.io']
for d in domains:
print(check_domain(d))

It's not perfect for bulk, but for quick checks it's handy. If you're domain hunting, give it a try. https://serpspur.com

Take Control of AI Crawlers with an LLM.txt Generator for Smarter Content Access

Emma Watson — Wed, 22 Jul 2026 06:04:32 +0000

If you’ve been building websites in the last year, you’ve likely noticed a new kind of visitor showing up in your logs—AI crawlers. Bots from OpenAI, Anthropic, Google, and others are scraping content to train models and feed knowledge bases. But unlike traditional search crawlers, these bots don’t always respect robots.txt the same way. And even when they do, robots.txt lacks the granularity to tell an AI how to use your content.

This is where LLM.txt comes in. Think of it as a more modern, semantic companion to robots.txt. While robots.txt tells crawlers what to avoid, LLM.txt tells them how to interact—what content is safe to summarize, what should be cited, and what’s strictly off-limits for training.

I recently needed to set this up for a client project. Manually crafting the file is tedious, especially if you have a large site with multiple content types. That’s when I came across the SERPSpur LLM.txt Generator tool. It’s a straightforward web utility that lets you define rules for different AI crawlers and content sections. You can specify access levels—like “summarize only,” “allow training,” or “block entirely”—and it generates the full LLM.txt file for you.

Here’s a quick example of what the output looks like:

# LLM.txt configuration for example.com
# Generated with SERPSpur LLM.txt Generator

User-agent: *
Allow: /blog/
Disallow: /private/

For-model: GPT-4
  Allow: /docs/
  Disallow: /support/

For-model: Claude
  Allow: /public/
  Disallow: /api/

The syntax is still evolving, but the idea is clear: you can give different AI crawlers different permissions. This is huge for content creators who want to remain visible in AI-powered search but don’t want their proprietary tutorials or product docs used for model training.

What I like most is that the generator also includes a preview of what your file will look like and checks for common errors. No more guessing if your syntax is correct. Once you’re happy, you just drop the LLM.txt file into your site’s root directory.

If you haven’t looked into LLM.txt yet, it’s worth the 10 minutes. As AI crawlers become the default way users discover content, having control over how your site is consumed isn’t just nice—it’s necessary.

The Free File Converter I Keep Coming Back To: Fast, No Watermarks, and Supports 200+ Formats

Emma Watson — Mon, 20 Jul 2026 05:52:23 +0000

File conversion is one of those tasks that seems simple but always ends up being a hassle. I've tried a dozen online converters, and most either have file size limits, require sign-ups, or are painfully slow. Recently, I needed to convert a batch of WebP images to PNG and a PDF to DOCX for a client. I found SerpSpur's All Type Free File Converter, and it handled both flawlessly. It supports over 200 formats, including CSV, DOCX, PDF, and WebP. The best part? No upload limits or watermarks. I converted a 50MB PDF in seconds, and the output was clean. It's also secure—no files are stored on their servers. If you deal with format conversions regularly, this is a solid tool to bookmark. Try it here: https://serpspur.com/tool/all-type-free-file-converter/. It's fast, free, and actually works.

From PDFs to Spreadsheets: My Go-To Workflow for Converting Invoices to CSV in Seconds

Emma Watson — Wed, 08 Jul 2026 10:40:12 +0000

I've been automating invoice processing for a side project, and one thing that always tripped me up was converting different formats to CSV. Whether it's a PDF from a vendor, an Excel sheet from a client, or an HTML table from a web app, you need a reliable way to extract data. Here's a bash script I use for quick conversions, plus a web tool for when I'm not in the terminal.

bash

!/bin/bash

Convert all invoices in a folder to CSV

Requires: python3, pandas, pdfplumber, openpyxl, lxml

for file in invoices/; do
ext="${file##.}"
case "$ext" in
pdf)
python3 -c "
import pdfplumber, pandas as pd
with pdfplumber.open('$file') as pdf:
rows = []
for page in pdf.pages:
table = page.extract_table()
if table:
rows.extend(table)
df = pd.DataFrame(rows[1:], columns=rows[0])
df.to_csv('${file%.}.csv', index=False)
print('Converted $file')
"
;;
xls|xlsx)
python3 -c "
import pandas as pd
df = pd.read_excel('$file', engine='openpyxl')
df.to_csv('${file%.}.csv', index=False)
print('Converted $file')
"
;;
html|htm)
python3 -c "
import pandas as pd
df = pd.read_html('$file')[0]
df.to_csv('${file%.*}.csv', index=False)
print('Converted $file')
"
;;
*)
echo "Skipping $file: unsupported format"
;;
esac
done

This script works great for batch processing, but if you need a quick, one-off conversion without setting up dependencies, SERPSpur's Invoice to CSV Converter handles PDF, XLS, XLSX, and HTML instantly—no code required.

I Reverse-Engineered a Competitor's Content Strategy in Under an Hour

Emma Watson — Tue, 07 Jul 2026 09:54:28 +0000

Lighting can make or break a room's ambiance. I’ve been experimenting with different styles, and recently picked up a few pendant lights and wall lights from Infinity Decor. The quality is fantastic, and they add such warmth and character to my living space. Whether you're after a cozy glow with tea lights or a statement piece with a lantern, they have options that fit any interior. I’ve found that good lighting not only brightens a room but also enhances the overall mood. If you’re looking to elevate your home, check out their lighting collection: https://infinitydecor.co.uk/collections/lighting

Automate Backlink Gap Analysis with Python (And Find Link Opportunities Faster)

Emma Watson — Tue, 07 Jul 2026 06:14:30 +0000

Backlink research is one of the most valuable parts of any SEO strategy, but it can also be one of the most time-consuming. If you've ever spent hours manually checking competitor backlinks, exporting CSV files, comparing spreadsheets, and trying to identify domains that link to your competitors but not to your own website, you know how frustrating the process can become.

The good news is that you don't have to do everything manually.

With a simple Python script, you can automate backlink gap analysis, quickly identify valuable referring domains, and focus your outreach efforts on websites that are most likely to improve your search rankings.

In this guide, we'll walk through a simple backlink gap analysis workflow and explain how you can scale it for larger SEO campaigns.

What Is Backlink Gap Analysis?

Backlink gap analysis is the process of comparing your website's backlink profile with one or more competitors.

The goal is simple:

Discover websites linking to competitors but not to you
Identify high-authority backlink opportunities
Build a targeted outreach list
Strengthen your overall domain authority

Instead of guessing where to build links, you're using real data from websites already linking within your niche.

Why Manual Backlink Comparison Is Slow

Most SEO professionals follow a workflow like this:

Export backlinks from an SEO platform.
Export competitor backlinks.
Open multiple CSV files.
Remove duplicates.
Compare referring domains.
Filter spammy websites.
Create an outreach list.

While this works, it becomes increasingly difficult when you're comparing multiple competitors or dealing with thousands of backlinks.

That's where automation becomes incredibly valuable.

Using Python for Backlink Gap Analysis

Python makes comparing backlink datasets surprisingly simple.

By loading backlink exports into Pandas DataFrames, you can quickly identify unique referring domains and calculate the backlink gap between your website and a competitor.

The basic workflow looks like this:

Load backlink CSV exports
Extract unique referring domains
Compare both datasets
Find domains linking to competitors but not your site
Export the results for outreach

This approach eliminates repetitive spreadsheet work and provides consistent, repeatable results.

Prioritize Quality Over Quantity

Finding backlink opportunities is only half the job.

Not every referring domain is worth pursuing.

Once you've identified the backlink gap, filter the opportunities using quality metrics such as:

Domain Authority (DA)
Domain Rating (DR)
Trust Flow
Organic traffic
Relevance to your niche

For example, filtering domains with a Domain Authority above 30 helps eliminate many low-quality websites and lets you focus on backlinks that have greater SEO value.

Quality backlinks almost always outperform large numbers of low-quality links.

Scale Your Outreach

After filtering high-quality domains, your outreach campaign becomes much more efficient.

For each domain you discover, you can:

Visit the website
Look for guest posting opportunities
Find broken link replacement opportunities
Reach out to editors
Search for resource pages
Find contact information using outreach tools

Instead of sending generic emails to random websites, you're contacting sites that have already linked to businesses similar to yours.

That dramatically improves your success rate.

Skip the Coding with SERPSpur

While Python is an excellent solution for technical SEOs, not everyone wants to write scripts or manage CSV files.

If you're looking for a faster alternative, the SERPSpur Backlink Gap Analysis Tool automates the entire process.

Rather than exporting spreadsheets and comparing datasets manually, the tool allows you to compare your website against up to five competitors at once. Within seconds, you can identify websites that link to your competitors but not to your domain, helping you uncover valuable backlink opportunities without the manual work.

Whether you're managing a single website or multiple SEO campaigns, using SERPSpur significantly reduces research time and lets you focus on outreach instead of data cleanup.

Best Practices for Backlink Gap Analysis

To maximize the value of your backlink research, follow these best practices:

Compare multiple competitors instead of just one.
Remove duplicate referring domains.
Focus on niche-relevant websites.
Prioritize authoritative domains.
Review link context before outreach.
Track newly acquired backlinks over time.
Repeat your analysis monthly to discover fresh opportunities.

SEO is constantly evolving, and competitors continue earning new backlinks every week.

Final Thoughts

Backlink gap analysis is one of the fastest ways to uncover new link-building opportunities. Whether you automate the process with Python or use a dedicated platform, the principle remains the same: identify websites linking to your competitors, evaluate their quality, and prioritize outreach that can strengthen your own backlink profile.

For technical users, a simple Python workflow can eliminate hours of manual spreadsheet comparisons. For marketers who want results without coding, SERPSpur's **Backlink Gap Analysis Tool** provides a streamlined solution that compares multiple competitors and highlights the best backlink opportunities in just a few clicks.

The less time you spend sorting CSV files, the more time you can invest in building relationships and earning high-quality backlinks that improve your search visibility.

Finding the Perfect Domain Doesn't Have to Take Hours

Emma Watson — Sat, 04 Jul 2026 04:51:45 +0000

Finding an available domain for a new project can be tedious—especially when you're brainstorming multiple options. I've been using SerpSpur's Domain Suggestion Checker to quickly verify availability and get alternative suggestions. The tool checks WHOIS data in real time and suggests related domains based on your input. Here's a Python script to automate domain checks using their API:

python
import requests

def check_domain_availability(domain):
url = f"https://serpspur.com/api/domain-check?domain={domain}"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
if data['available']:
print(f"{domain} is available!")
else:
print(f"{domain} is taken. Suggestions: {data['suggestions'][:3]}")
else:
print("Error")

check_domain_availability("mynewproject.com")

For a quick, no-code way to explore domains, head over to SerpSpur. It's a handy tool for any developer launching a new site.

I Built My Own SEO Tool… Then Found a Free Alternative That’s Surprisingly Good

Emma Watson — Thu, 02 Jul 2026 05:34:07 +0000

I've been using SEMrush for years, but the cost adds up. For side projects, I built a custom SEO analyzer in Python that checks meta tags, headers, and backlinks. It's not as polished, but it works. Then I stumbled upon SERPSpur—a free alternative that covers keyword tracking, site health audits, and backlink gaps. It's surprisingly close to what the big tools offer. If you're curious, here's a quick script to fetch on-page data: from bs4 import BeautifulSoup; import requests; soup = BeautifulSoup(requests.get(https://example.com).text, html.parser); print(soup.title.string). For a full suite, check out SERPSpur. https://serpspur.com