Topic Coverage Completeness
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❖ NLP & Text Analysis

Topic Coverage Completeness Checker

v1.0 documentation

Compare your content's subtopic coverage against an ideal topic map built from top-ranking competitors.

XLSX export
topic_coverage_completeness.py94 lines4 paramsPython 3.8+
Quick start
1

Install

terminal
pip install -r requirements.txt
2

Run

terminal
python topic_coverage_completeness.py --my-url https://mysite.com/post --competitor-urls https://a.com https://b.com --keyword "crm software" --output coverage.xlsx
3

Export

Add --output report.xlsx to save results as a spreadsheet.

Parameters
FlagDescription
--my-url requiredMy url
--competitor-urls requiredCompetitor urls. Multiple values allowed
--keywordKeyword
--outputOutput
help
python topic_coverage_completeness.py --help
Use cases
Content quality audit
Writer evaluation
Client deliverable

Run across all your blog posts to score quality. Sort by score in the XLSX export, then prioritize rewrites for the lowest-scoring pages.

Before publishing freelance content, run this tool to check quality signals. Use specific metrics as concrete feedback for writers.

Include the analysis in your SEO audit report. Clients appreciate data-backed recommendations over subjective opinions.

Dependencies

Requires: beautifulsoup4, numpy, pandas, requests, scikit-learn. All included in requirements.txt.

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AAIO Inc — aaioinc.com/tools/topic_coverage_completeness/