f1-tyre-model — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited f1-tyre-model (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
| Compound | base_lap_delta_s | degradation_rate | safe_window_laps | crossover_lap |
|---|---|---|---|---|
| SOFT | -0.8 | 0.08 | 15 | 12 |
| MEDIUM | 0.0 | 0.05 | 25 | 20 |
| HARD | +0.6 | 0.03 | 40 | 35 |
| INTER | +3.0 | 0.10 | 20 | 15 |
| WET | +6.0 | 0.12 | 15 | 10 |
base_lap_delta_s: seconds vs MEDIUM reference. Negative = faster.
Default: 22s. Circuit-specific tuning is a Phase 3.1 follow-up.
Linear polyfit: lap_time = intercept + slope × tyre_age Outlier filter: drop laps > mean + 2σ (SC laps, in/out laps). Min 2 valid samples required; returns slope=0 if insufficient data.
net_gain_per_lap = fresh_tyre_delta_s − (attacker_pace − target_pace) laps_to_clear = ceil((gap_to_target + pit_loss) / net_gain_per_lap) Viable if laps_to_clear ≤ 10. Marginal if 5 < laps_to_clear ≤ 10.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.