citation-ready 0.1.0

Check a page for the structural signals observed on content AI answer engines tend to cite. Observed correlations, not guarantees.

citation-ready

Does AI cite you — and is your page even built to be cited? Check a page for the structural signals our AI Visibility Index observes on content that AI engines (ChatGPT, Perplexity, Claude, Gemini, Google AI) tend to quote and recommend.

These are observed correlations, not guarantees. A high score means your page looks like the pages that get cited — it does not promise a citation. The only way to know whether AI actually cites you is to measure it (see below).

• Zero dependencies — Python standard library only.
• Mock mode by default — runs offline against a built-in demo page.
• Honest framing — every report says these are correlations from measurement, not a ranking guarantee.

Usage

Offline demo (no network needed):

python citation_ready.py --mock

Check a live URL (also looks for /llms.txt):

python citation_ready.py --url https://example.com

Check a local HTML file:

python citation_ready.py --file page.html

Machine-readable output:

python citation_ready.py --url https://example.com --json

Example output

Strong signals — 100/100
(built-in demo page)

  ✓  Valid schema (JSON-LD) for AI parsing
  ✓  Answer-first structure (direct summary + lists/tables)
  ✓  Clear heading outline (one H1, descriptive H2s)
  ✓  Freshness signal (dateModified or a recent visible date)
  ✓  Consistent entity (Organization + sameAs / profiles)
  ✓  llms.txt present / referenced

These are observed correlations from our AI Visibility Index measurement — signals
that tend to accompany pages AI engines cite. They are not guarantees; the measured
answer for your domain comes from a teardown.
Measure the real thing: https://clearcited.com/free-teardown/

What it checks

Six structural signals, each with the honest reason it correlates with getting cited:

Signal	Why it correlates
Valid schema (JSON-LD)	Structured data lets engines extract entities and claims cleanly.
Answer-first structure	Engines quote pages that state the answer early and in extractable chunks.
Clear heading outline	A clean outline maps to the sub-questions engines answer.
Freshness signal	Engines favour pages that show they are current.
Consistent entity	A stable, cross-linked entity is easier to attribute and trust.
llms.txt	An llms.txt gives agents a curated map of your canonical content.

The score is a weighted sum of the signals present, banded into Strong / Developing / Weak. Detection is plain regex over the HTML — no headless browser, no JavaScript execution.

How this connects to real measurement

This CLI is a heuristic. It reads your page and predicts, from structure alone, whether it looks like content AI engines cite. It never sends a prompt to an engine, so it cannot tell you whether you are actually cited today.

The measured answer is a teardown: we run real buyer prompts across every major engine, several runs each, and report where you show up versus competitors — with a confidence interval, the same way every time. The signals this tool checks are drawn from what our AI Visibility Index observes on pages that earn those citations.

• How we measure: https://clearcited.com/methodology/
• Get the measured answer for your domain: https://clearcited.com/free-teardown/

Honest distinction: citation-ready is instant-but-predictive (structure only, offline); the teardown is measured-but-async (real prompts, every engine, human QC).

License

MIT © Clear Cited

---

This is a free, lite tool. The full Clear Cited service measures citations across every major engine (ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews), with human QC and a fix roadmap. Get a free teardown →