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A marketing-number checker is useful only if it can see every place a claim appears—and reliably flags a deliberately wrong value. In one case described by developer Mustafa Tarabya, a script said template counts matched while missing contradictory figures in pricing copy and a generated page title. The failure was not just the wrong number; it was a false pass that made an unverified claim look settled.
How one count spread into four conflicting claims
Tarabya recounts a website whose materials presented four different template counts: 86 across 34 article files, 82 in nine places including pricing constants, “100+” on a landing page, and 96 in a marketing kit. He says none was the correct count. These are figures from his account, not independently verified measurements or evidence about how often this happens elsewhere. Read Tarabya’s account on DEV Community.
He also describes an earlier incident in which the site advertised “200+” templates against roughly 80. He says the claim had to be corrected as an honesty issue, not treated as a typo. In the later incident, a checker calculated a count from configuration and searched selected project files for matching claims. It reported that the counts matched, but missed a page title reading “96 ATS-Ready Designs” and pricing copy stating “92 premium templates.”
Why the checker gave a false pass
It did not scan every relevant location
The search-engine-facing strings were injected by a build script at scripts/prerender-head.mjs, but the checker’s configured root paths left out the scripts directory. A scan limited to selected content folders can miss claims created or inserted during a build.
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Its file filter excluded the script
Adding a directory is not enough if the scanner still ignores the file types inside it. The checker accepted selected extensions but not .mjs, the extension of the build script containing the generated title.
Its wording pattern was too narrow
The checker looked for numbers followed by “templates,” with an optional “premium templates” phrase. That could not recognize “96 ATS-Ready Designs,” even though it expressed the same kind of quantity claim. Marketing copy can describe one underlying count with different nouns and formats.
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Widening the scan exposed a self-scan
After the author expanded the scope and wording pattern, the checker also found historical wrong counts in its own docstring, where they appeared as explanatory examples. Tarabya says the script was changed to exclude itself. A broader scan improves coverage, but it also needs a rule for distinguishing live claims from examples, documentation, and test fixtures.
How to check that marketing numbers are consistent
Do not treat “the script passed” as proof until you have tested whether it can detect a known contradiction. Use a workflow that checks both the source of truth and the places where claims are published:
- Choose the authoritative value. Identify where the real count is maintained, such as configuration or a catalog, and determine how it is calculated. Do not use the largest number already appearing in marketing copy as the source of truth.
- Map the claim surface. List the directories and build steps that can contain or generate public-facing claims. Include pages, pricing constants, metadata, landing pages, and marketing materials when they live in the repository.
- Review file-type coverage. Confirm that the scanner accepts the relevant extensions and content types, including files used to generate page titles or other output. A directory can be included while important files inside it remain excluded.
- Account for wording variations. Search for the number in context, not only one noun phrase. Include likely alternatives such as “designs,” “items,” or other labels actually used by your site, as well as forms like “100+.” Avoid assuming that every claim ends with the word “templates.”
- Test with a known bad value. Temporarily put a deliberately incorrect claim in a representative location and run the checker. It should identify the value and location. If it still passes, the scan has not demonstrated that it can protect that part of the claim surface.
- Test exclusions, too. Confirm that documentation examples and the checker’s own source do not become false live claims. Keep exclusions narrow and intentional so that preventing self-matches does not hide real marketing copy.
- Review generated output. Where a build step injects or transforms copy, inspect the resulting page or metadata as well as the source files. The public claim is the final output, not merely the text the checker happened to read.
- Re-run after edits. When the count or scan rules change, run the checker again and review its findings before publishing. A clean result only speaks to the locations and forms the checker actually covers.
What a reliable check needs to cover
| Check | Question to answer | Failure it helps catch |
|---|---|---|
| Scan coverage | Are all relevant directories and generated-content sources included? | A claim in an omitted path, such as a build script |
| File coverage | Are the extensions and content types used by those sources accepted? | A relevant file silently skipped by the extension filter |
| Phrase coverage | Can the checker recognize different labels and formats for the same quantity? | A claim phrased as “designs” rather than “templates,” or as “100+” |
| Checker validation | Does a known wrong value trigger a finding in a representative location? | A confident pass from a checker that does not detect the contradiction |
| Exclusion validation | Are examples and the checker’s own documentation handled without masking live copy? | False findings after broadening the scan—or exclusions that hide real claims |
Why a false pass is more dangerous than a visible failure
A checker that reports an error when nothing is wrong creates work. A checker that reports success while missing a real contradiction can create unjustified confidence. As Tarabya puts it: “A checker that fails loudly is a minor annoyance. A checker that passes while the thing it checks is broken is worse than no checker, because it converts an open question into a settled one.”
That is why the known-bad-value test matters: it turns a checker’s apparent success into something you can evaluate. A passing run is meaningful only after you have shown that the checker can see relevant claims and raise an alert when one is wrong.
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