AI Mastery is served as a public static site and its source repository is available for inspection.
Inspect repository ↗Public implementation audit · 2026-08-27
Make the work
inspectable.
This is AI Mastery’s bounded self-audit of what its public website and repository show at one date in time. It records implementation evidence, names its limits, and leaves outcomes unclaimed when no attributable evidence was inspected.
Audit boundary
Not a success story.
A release record.
The audit inspects public HTML, linked learning routes, source and discovery files, repository history, and GitHub Pages publication records. It does not measure search demand, reader behavior, commercial results, security, legal compliance, accessibility certification, or the quality of any third-party claim.
What the record shows
Public implementation, linked to its evidence.
These observations are factual statements about the property’s published code and routes at the audit checkpoint. They are not claims about effectiveness, adoption, or quality.
The public revision at the audit checkpoint includes the Knowledge Index, an ARM pathway, and Trust Infrastructure lessons.
Inspect revision ↗The site publishes `llms.txt` and `sitemap.xml`, alongside local-only structural validation scripts that can be inspected in the repository.
Read llms.txt →What remains unknown
No outcome finding
without an evidence record.
Unknown is a first-class result in this audit. It prevents technical implementation observations from becoming vague claims about market, readership, reliability, or trust.
Search and citation outcomes
No attributed search-console, referral, ranking, citation, or independent crawl record was reviewed.
Reader comprehension and use
No consented reader research, learner-completion record, or outcome study was reviewed.
Conversion, revenue, and customer results
No financial, customer, or conversion evidence belongs to this implementation audit.
Security, compliance, and certification
Static-route and structural checks are not professional assessments in any of those disciplines.
Self-audit method
Inspect. Record. Correct. Recheck.
This is an AI Mastery framework for accountable self-documentation. It organizes review work; it does not certify the audited property or guarantee a result.
Use public, stable sources.
Start with live routes, repository revisions, public build records, and directly inspectable source files.
Classify every statement.
Keep facts, interpretations, frameworks, and unknowns distinct. Link material facts to their source and review date.
Invite a specific challenge.
Give a reader a route to identify a wrong link, stale record, missing boundary, or overbroad conclusion.
Revisit after material change.
Update the record after a route, curriculum, discovery asset, publication process, or source set changes.
Primary sources
Follow the evidence, not the summary.
- 01Self-audit evidence ledgerDetailed observation register and explicit unknowns for this audit.
- 02Machine-readable claim registerClaim class, source list, review date, status, and caveat for every proposed case-study statement.
- 03Audited published revisionThe repository revision used as the public implementation checkpoint.
- 04
Release + maintenance
A transparent record needs a way to change.
This case study was drafted with AI assistance from the linked first-party repository and live-site sources. It is not an independent audit. The site identifies Mason Nguyen as its creator; a material change to routes, learning content, discovery files, or public publication practice requires this page and its evidence ledger to be rechecked.
Publishing boundary: any new statement about audience, rankings, citations, revenue, user outcomes, security, accessibility, legality, certification, or product availability requires a dated and inspectable source plus an accountable human review. Until then, it remains absent or unknown.