Borrowing it
Nothing to install: this file belongs to Ninjabeam20/SportIQ-MCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Ninjabeam20/SportIQ-MCP/main/.agents/skills/f1-tyre-model/SKILL.mdgit clone --depth 1 https://github.com/Ninjabeam20/SportIQ-MCPWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ninjabeam20/sportiq-mcp/f1-tyre-model)<a href="https://agentmods.dev/skills/ninjabeam20/sportiq-mcp/f1-tyre-model"><img src="https://agentmods.dev/badge/skills/ninjabeam20/sportiq-mcp/f1-tyre-model/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ninjabeam20/sportiq-mcp/f1-tyre-model"><img src="https://agentmods.dev/badge/skills/ninjabeam20/sportiq-mcp/f1-tyre-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00402 |
| Opus 5 | $0.00022 | $0.00201 |
| Sonnet 5 | $0.00009 | $0.00080 |
| Haiku 4.5 | $0.00004 | $0.00040 |
Grade A, and why
f1-tyre-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
F1 Tyre Model Skill
Tyre compounds and static constants
| 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.
Pit lane loss (static seeds, Phase 3)
Default: 22s. Circuit-specific tuning is a Phase 3.1 follow-up.
Degradation model
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.
Undercut formula
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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 33 lines · 44 tokens per session scan A a1e0c34c7ef5
f1-tyre-model is a skill published in the GitHub repository Ninjabeam20/SportIQ-MCP (10 stars, last pushed 9d ago), licensed MIT. It adds 44 tokens to every session and 402 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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