Score bands and gate policy
The score
Section titled “The score”The audit gives one verdict per rule, then weights each rule by its priority. The weight halves at each tier:
| Priority | Weight |
|---|---|
| P0, critical | 32 |
| P1 | 16 |
| P2 | 8 |
| P3 | 4 |
| P4 | 2 |
| P5 | 1 |
score = round(100 * weight of rules passed / weight of rules assessed)- One verdict per rule, not per file. A rule fails if any file fails it, so a project with four route files does not count the rule four times.
- Rules that do not apply are left out of both sides. A skipped rule neither earns nor costs anything, since scoring a project against, for example, a rule for the other stack would be meaningless.
- A file that cannot be parsed counts against the score, since leaving it out would make an unreadable file the cheapest way to raise a score.
Halving keeps one P0 rule worth more than every P5 rule in the ruleset combined. The weights are a recorded choice; no source document specified them.
The bands
Section titled “The bands”| Score | Band |
|---|---|
| 90 to 100 | Production Ready |
| 70 to 89 | Nearly Ready |
| 40 to 69 | Needs Work |
| 0 to 39 | Not Ready |
The gate policy
Section titled “The gate policy”| Condition | Value |
|---|---|
| Minimum score | 90 |
| Critical (P0) rules failing | 0 |
| Model estimate | At or above the operating point stored with the model, 0.369 in 1.0.0 and 1.1.0 |
| Retry budget | 3 attempts per issue, across the whole run |
| Cycle ceiling | 5 |
All three conditions must hold. Why each exists is on The scoring gate.
The operating point is read from the model file, not written in the code, so the model and the point it is judged against cannot drift apart. A model that fails to load, or was trained on a different feature list, gives no estimate, and the gate stays shut.
What 1.x keeps stable
Section titled “What 1.x keeps stable”The gate’s three conditions are promised for every 1.x release. The score a given project receives is not: rules get sharper, so a project may score differently between releases. The model may be retrained, which changes its estimates and its operating point. See stability.md.