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How one template count turned into four different claims
Mustafa Tarabya describes a website whose marketing materials gave four different template counts: 86 across 34 article files; 82 in nine places, including pricing constants; “100+” on a landing page; and 96 in the marketing kit. He says none was the real 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.
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He also recounts an earlier incident in which the site advertised “200+” templates against roughly 80. He says the copy had to be corrected as an honesty issue, not dismissed as a typo. That distinction matters: a numeric claim can shape a customer’s understanding of what is being offered, so consistency is not merely a copy-editing concern.
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The script calculated a count from configuration and scanned selected parts of the project for wording that matched a narrow pattern. Its pass was misleading because the scan did not cover the full set of places and forms in which the number appeared.
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It omitted a directory
Search-engine-facing strings were injected by a build script at scripts/prerender-head.mjs, but the checker’s configured roots left out scripts. As a result, it could not see the generated page title “96 ATS-Ready Designs.”
It excluded the script’s file extension
Adding the directory alone did not solve the problem. The scanner accepted only selected extensions and did not include .mjs, the extension used by the build script.
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It expected one way of phrasing the claim
The pattern looked for a number followed by “templates” or “premium templates.” It therefore missed “96 ATS-Ready Designs,” even though “designs” referred to the same kind of offering in that context. The checker also missed pricing copy that said “92 premium templates.”
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Once the scan scope and pattern were broadened, the checker began finding the historical wrong values written in its own explanatory docstring. Tarabya says the script was changed to skip itself. A scan should find real claims in the project without mistaking documentation about the checker for live marketing copy.
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How to check that marketing numbers are consistent
Use the failure modes in this case as a practical review framework. A check is only useful if it can see the relevant claims, recognize their wording, and prove it will reject a known contradiction.
1. Define the claim and its source of truth
Decide exactly what the number means: for example, whether it counts all templates, only premium templates, or another defined subset. Identify the configuration or maintained record that supplies the authoritative value. If two pages use different definitions, they may show different numbers without either being technically wrong—but the distinction needs to be explicit to readers.
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2. Map every place the claim can appear
Include source files, pricing content, articles, landing pages, marketing kits, and build scripts that inject metadata or other generated text. A search over the obvious content directory is not enough if a build step creates a page title elsewhere.
3. Check file and content coverage
Review the scanner’s allowed extensions and formats against the files in the claim map. Include relevant script and content types rather than assuming that the visible website’s text all lives in ordinary page files.
4. Account for wording variants
List plausible ways a reader might encounter the same claim: “templates,” “designs,” a branded phrase, or a number embedded in a title or pricing description. Search logic that only recognizes one noun can miss a semantically identical claim. Where phrasing varies too much for a reliable pattern, consider making the claim a shared value inserted into each asset rather than duplicating literal numbers.
5. Test the checker with a known bad value
Temporarily place a deliberately incorrect number in a representative file that the scan is supposed to cover. Run the checker and verify that it fails or reports that exact location. A green result without this test does not demonstrate that the checker would catch a contradiction; it may simply mean the relevant claim was invisible to it.
6. Keep the checker from treating its own examples as claims
If documentation or test fixtures contain sample numbers, ensure they cannot be mistaken for production copy. Exclude the checker’s own source when appropriate, or separate examples from the files and patterns used to detect live claims. Then rerun the known-bad-value test after changing the scan.
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What a passing result does—and does not—prove
A pass means the checker found no contradiction among the files and phrasings it actually examines. It does not establish consistency across omitted directories, unsupported extensions, generated content, or language patterns the scanner cannot recognize. Nor does a scan prove that the authoritative count itself is accurate; that depends on how the count is defined and maintained.
Tarabya’s account illustrates the difference between a tool reporting success and a claim being correct. As he 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.”
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