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Self-audit checklist

Run this against your own tool. Every item maps to a principle or an archetype technique. Aim for "yes" or "not applicable," never "hidden."

Principles

  • Every agent-facing file is public and reachable at a well-known URL.
  • A person can find those files from the site (a footer link is enough).
  • What agents read matches what humans are told (parity).
  • No hidden text, invisible instructions, comment-only directives, or cloaking anywhere.
  • robots.txt states which AI user-agents may use the content.
  • Agent-facing files are generated from source at build time, not hand-maintained.
  • User-generated content is treated as untrusted in anything exposed to agents.
  • Your license is machine-readable (schema.org license, SPDX, LICENSE, CITATION.cff), with attribution guidance.

Website / docs site

  • /llms.txt exists, is curated (not a sitemap dump), and is current.
  • robots.txt names the AI agents you allow or disallow.
  • JSON-LD (Organization, and SoftwareApplication for a tool) is in every page head.
  • Markdown mirrors are available for key pages.
  • sitemap.xml has real <lastmod> dates.

Command-line tool

  • --help is complete and example-led on every subcommand.
  • A machine-readable help or schema form exists.
  • AGENTS.md covers install, key commands, and a worked example.
  • Exit codes are meaningful and a --json output mode exists.
  • No required interactive prompt lacks a non-interactive flag.

Library / toolbox

  • Every public function has an accurate, example-led docstring with argument types and units.
  • Signatures are explicit; defaults are documented.
  • The docs site has an llms.txt.
  • Worked notebooks or scripts cover the common workflows.

Web app / API

  • An OpenAPI specification is published and reachable without auth.
  • JSON-LD SoftwareApplication is on the landing page.
  • Core actions are exposed via an MCP server or a documented API.
  • Auth, rate limits, and error shapes are documented.

Data archive

  • Each dataset page emits schema.org Dataset JSON-LD with a license and identifier.
  • A machine-readable catalog of all datasets exists.
  • An agent can search or query programmatically (documented API with examples).
  • Metadata is fetchable in machine-readable form without downloading the data.
  • Bulk and programmatic data download is documented (API, bucket, or CLI), not just a browser button.
  • A top-level llms.txt points at the catalog and search, not every dataset.

Research lab / project site

  • JSON-LD marks up the lab, its people, software, datasets, and papers.
  • Persistent identifiers are exposed (ORCID, ROR, DOI).
  • A canonical index links each output to its own home.
  • llms.txt maps people, software, datasets, and key papers.

Standard / specification

  • The specification is published as a machine-readable schema.
  • A validator exists and is callable (command-line, library, or API/MCP).
  • The specification is versioned and citable.
  • Conformant examples (and a few labeled non-conformant ones) are provided.