Governance artifacts
Governance files brought into scope by this page
This page is anchored to published surfaces that declare identity, precedence, limits, and the corpus reading conditions. Their order below gives the recommended reading sequence.
Site context
/site-context.md
Notice that qualifies the nature of the site, its reference function, and its non-transactional limits.
- Governs
- Editorial framing, temporality, and the readability of explicit changes.
- Bounds
- Silent drifts and readings that assume stability without checking versions.
Does not guarantee: Versioning makes a gap auditable; it does not automatically correct outputs already in circulation.
Registry of recurrent misinterpretations
/common-misinterpretations.json
Published list of already observed reading errors and the expected rectifications.
- Governs
- Limits, exclusions, non-public fields, and known errors.
- Bounds
- Over-interpretations that turn a gap or proximity into an assertion.
Does not guarantee: Declaring a boundary does not imply every system will automatically respect it.
manifest.json
/observations/better-robots-ai-2026/manifest.json
Published machine-first governance surface.
- Governs
- Part of the corpus reading conditions.
- Bounds
- An inference zone that would otherwise remain implicit.
Does not guarantee: This file does not, on its own, guarantee system obedience.
Evidence layer
Probative surfaces brought into scope by this page
This page does more than point to governance files. It is also anchored to surfaces that make observation, traceability, fidelity, and audit more reconstructible. Their order below makes the minimal evidence chain explicit.
- 01Observation mapObservatory map
- 02Evidence artifactmanifest.json
Observatory map
/observations/observatory-map.json
Machine-first index of published observation resources, snapshots, and comparison points.
- Makes provable
- Where the observation objects used in an evidence chain are located.
- Does not prove
- Neither the quality of a result nor the fidelity of a particular response.
- Use when
- To locate baselines, ledgers, snapshots, and derived artifacts.
manifest.json
/observations/better-robots-ai-2026/manifest.json
Published surface that contributes to making an evidence chain more reconstructible.
- Makes provable
- Part of the observation, trace, audit, or fidelity chain.
- Does not prove
- Neither total proof, obedience guarantee, nor implicit certification.
- Use when
- When a page needs to make its evidence regime explicit.
Better Robots.txt as an applied surface
This entry is neither a commercial page nor an autonomous doctrine. It exists to situate Better Robots.txt as a derived instrument and concrete implementation on WordPress inside an ecosystem of published surfaces.
Which doctrinal problem is materialized
Better Robots.txt materializes part of the following problem: how to make more governable, on WordPress, one signaling and configuration layer around:
robots.txt;- certain named AI bots;
llms.txtdocumentation;- one interface for centralization and review before publication.
The plugin does not exhaust the doctrine of the broader space. It materializes one implementable portion of the problem.
Which perimeter it covers
The covered perimeter must be read as operational and bounded.
The plugin mainly covers:
- generation or organization of
robots.txt; - settings related to some crawlers and named agents;
- one
llms.txtdocumentation layer; - presets and a guided WordPress interface.
What it does not cover
The plugin does not, by itself, cover:
- the general doctrine of discoverability, answering, and training;
- proof of compliance for every system;
- an absolute technical barrier;
- the doctrinal authority of the whole problem space;
- any guarantee of citation, fidelity, or obedience.
Those limits should be read together with Signal, proof, and compliance, Why robots.txt is not a barrier, and Operational product authority and doctrinal authority.
Where to read what
Canonical doctrinal surface
gautierdorval.com- especially: Role of the site, Discoverability vs training, Machine policy surfaces
Product surface
https://better-robots.com/
Proof and product-definition surface
https://github.com/GautierDorval/better-robots-txt
Distribution surface
https://wordpress.org/plugins/better-robots-txt/
Diffusion surface
- LinkedIn, as a pedagogical and diffusion surface, not as the primary doctrinal canon
Why this entry exists
In a multisite ecosystem, a product recommendation may be real without the whole doctrine moving to the product surface. This entry prevents that drift.