llms.txt Checker, Generator & Monitor: AI Search Readiness

Combines all three llms.txt jobs in one tool: audit finds the gap, generate fixes it, monitor keeps it fixed.

Pick the mode per call — a score today, a ready file when you need one, and a standing check that flags the moment something breaks.

One Actor to schedule instead of juggling three separate tools for the same job.

Getting llms.txt right is three separate jobs: find out where you stand, produce the file, and notice when it drifts. Doing them with three different tools means three formats and no continuity — the file you write has no baseline to be measured against later.

Because all three modes share one output contract, the score you generate against is the score you are later monitored on. Monitoring compares against a stored baseline for that site: a score and a content hash, both derived from your own public file.

Monitoring distinguishes between the file going missing, its links breaking, the content going stale, and a plain change — so an alert tells you what happened rather than only that something did.

Charging happens per completed mode and never on a failed run. A run that could not finish returns its reason and costs nothing.

Link validation samples the links your file lists rather than fetching every one, so a clean result is strong evidence rather than exhaustive proof. Sampling is what keeps a monitoring run cheap enough to schedule frequently.

The first monitoring run on a site has nothing to compare against and simply records the baseline. From then on you get a difference rather than a fresh score to interpret.

Every mode honours the site’s robots.txt by default and touches public pages only. The single piece of state kept between runs is the monitoring baseline, derived entirely from the site’s own public file.

Auditing, generating and monitoring share one input shape, so moving between them costs nothing to learn. Pick the mode; everything else about the call stays the same.