Newsroom AI Crawler Policy Example
A publisher-focused example for separating search visibility, licensed archives, and AI training crawler access.
Read exampleExample library
BotAccess Lab practical examples
AI crawler policy tools, robots.txt examples, llms.txt maps, and operational crawler access playbooks. Each example starts with a real user problem, then shows the checks, workflow, and tools to use.
Real workflows
Choose a situation close to yours
These examples exist to make the tools easier to evaluate. They describe when a page is useful, what input to prepare, what result to keep, and what limitation to remember before publishing or relying on the output.
SaaS Documentation LLMs.txt Map Example
A documentation-site example for publishing an llms.txt map that helps assistants find official docs without guessing.
Read exampleEcommerce Product Pages Bot Policy Example
An ecommerce example for deciding which product, search, cart, and account paths should be open to crawlers.
Read exampleWordPress Blog Robots.txt Example
A WordPress blog example for managing AI crawler access without accidentally blocking useful public posts.
Read exampleResearch Library AI Crawler Boundary Example
A research-library example for distinguishing public abstracts, licensed PDFs, and metadata pages before writing crawler rules.
Read exampleHow to use these examples
Open the example that matches your task, then follow the workflow with your own realistic details. Do not treat a generated output as finished until you have checked the final file, policy text, crawler rule, or review record against the actual destination requirement.
Detailed operating notes
How to evaluate BotAccess Lab practical examples
This section turns the page into a practical crawler access workflow. It gives the reader a way to prepare inputs, judge the output, and keep a useful record instead of leaving with a shallow summary.
1. Prepare the real requirement
Before using this page, separate public discovery pages, licensed content, private paths, dynamic filters, and files that should never be crawled. The more precise the requirement is, the easier it is to decide whether the generated result is ready to use or needs another pass.
For a real project, write the requirement in one sentence and keep it next to the result. That simple note helps future reviewers understand why a specific setting, wording, rule, file format, or checklist item was chosen.
2. Review the output carefully
The expected outcome is a robots.txt rule set, llms.txt map, crawler test list, or change log entry. A useful result should be specific enough that another person can inspect it, repeat it, or compare it with the original requirement.
After generating an output, test representative URLs after publishing so the rule behavior matches the written crawler policy. If the output is vague, missing a key field, or does not match the destination requirement, revise the inputs and run the workflow again.
3. Avoid the common failure
The most common mistake is using one broad allow or block rule for the whole domain when different URL groups need different crawler treatment. This site is designed to reduce that risk by keeping tool actions visible and by linking guides, scenarios, and examples back to a concrete workflow.
When the page involves public publishing, compliance, or access rules, keep the final result separate from the draft. That makes it easier to rollback, correct, or explain the decision later.
Quality checklist before you leave
- Confirm that the page you used matches the actual situation, not just a similar title.
- Check every generated recommendation, file, rule, or notice against the requirement you wrote down first.
- Save a copy of the final output with the date, source page, and owner of the decision.
- Use the example library when you need to see how the same workflow behaves in a complete real-world case.
- Return to the main workflow when the requirement changes, instead of editing old output by guesswork.