A citable stats page from your own data

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# A citable stats page from your own data

Writers, researchers and AI assistants cite numbers they can trace to a source. the production site has data nobody else has: its own usage and product data (ask me which numbers I am comfortable publishing). Build a public, honest stats page from it, with every figure dated and explained, so it becomes the page others link to and assistants quote.

Site: not given. Find the production URL in the project config, or ask me.

## Ground rules

1. Read before you write. Explore the parts of the codebase this job touches and tell me what is already in place.
2. Plan, then stop. Give me a numbered plan: what you will change, in which files, and why. Wait for my approval before editing anything.
3. Fix things at their source (the shared layout, the config, the generator), not with one-off patches page by page.
4. Keep the project's conventions: its framework APIs, file layout, naming and code style. Add no new dependency without asking.
5. Where something is already done well, say so and leave it alone.

## You will not

- Edit `.env` files, secrets or API keys, or print their values.
- Deploy, push, merge or publish anything.
- Delete data, or run migrations or scripts against a production database.
- Add tracking, third-party scripts or links to any site I did not ask for.
- Change copy, design or behaviour that this job does not need.
- Publish any figure I have not approved.
- Expose raw rows, emails or anything that identifies a customer.

## Your stack

- Framework: Work out the framework, router and build output from the project files (package.json, lockfiles, framework config) before planning, and say which you found.
- Host: Work out where the site is hosted from the config files (vercel.json, netlify.toml, wrangler config, Dockerfile, fly.toml) and say which you found.

## Steps

### 1. Choose the numbers with me

From the database and analytics, propose six to twelve figures that are interesting to outsiders and safe to publish: totals, growth, averages, distributions. Nothing that identifies a customer, nothing that a competitor could misuse that I have not approved. Show me the list with sample values and how each is computed before building.

### 2. Compute them honestly

Write one query or function per figure, with the method in a comment: what is counted, what is excluded (test accounts, bots, refunds), and the time window. Use aggregated counts only. Cache the results (an hour to a day is fine) so the page is fast and cheap.

### 3. Build the page

- A clear H1 and a one-paragraph summary with the two most important figures.
- Each figure in its own section with an `id` anchor (so people can link to `#average-payment-time`), the number, the date range, and one sentence of method.
- A "How we count" section and a "last updated" timestamp from the data, not the deploy.
- A JSON (and optionally CSV) download of the same figures at a stable URL.

### 4. Make it citable

Add `Dataset` JSON-LD (name, description, `dateModified`, `creator` as the Organization, `distribution` pointing at the JSON file), a canonical URL, share tags, and a suggested citation line ("Source: Product name, stats page, date"). Link the page from the footer and from the about page.

## Done means

- [ ] I approved every figure before it was built.
- [ ] Each figure has its own anchor, date range and method sentence.
- [ ] No figure can identify a customer or person.
- [ ] A JSON download of the same figures exists at a stable URL.
- [ ] The page has Dataset JSON-LD and a last-updated time from the data.

## Report back

When you finish, give me:

- what you changed, file by file;
- anything you skipped, and why;
- anything I must do by hand (DNS records, dashboards, accounts, deploys);
- how to check each change on the live site.