audit.corewise.academy

Fleet audit: agentic engineering discipline

Deterministic, scored reports on AI-agent development discipline across ryanportfolio's repos, most private. The tool and pipeline are public and deterministic; reports on private repos are reproducible by the owner from the pinned commit. Unflattering scores stay in.

Scoreboard

RepoScoreReport
ryanportfolio/ryanportfolio78 Strongreport →
ryanportfolio/Truenote71.2 Developingreport →
ryanportfolio/Corewise.Academy69 Developingreport →
ryanportfolio/AI-Firmware68.6 Developingreport →
ryanportfolio/PixelSwarm68.4 Developingreport →
ryanportfolio/Local-CPU-only-PTT64.9 Developingreport →
ryanportfolio/range56.6 Earlyreport →
ryanportfolio/Extract-Video-Wisdom55.7 Earlyreport →
ryanportfolio/githelp52.1 Earlyreport →

What this score means

S-1

Process discipline, not code quality

This score measures process discipline, not code quality. The tool never reads the code. It reads only GitHub metadata: commits, pull requests, reviews, check runs, and merge events. It scores only what is recorded on GitHub; discipline that leaves no artifact there earns nothing.

S-2

One question

It answers one question: when AI agents help write the code, what does this repo's history prove about the checks standing between a change and the main branch?

S-3

Scoring and grades

Each dimension is a 0 to 100 answer to one concrete question, or 'could not verify' when the evidence is missing. The overall score is the weighted average of the verified dimensions, and grades band it: 90+ Elite, 75+ Strong, 60+ Developing, 40+ Early, under 40 Ad-hoc.

How these scores are made

M-1

Built by the pipeline it documents

Every change to the audit tool itself flows: plan → agent build → independent fresh-context AI review → CI test+eval gate → owner-authorized merge. The repo's own PR history is the living demo: read it.