Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add DanielTomaro13/sportsdata-agents --skill calibrate_probabilitiesgit clone --depth 1 https://github.com/DanielTomaro13/sportsdata-agentsWrote 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/danieltomaro13/sportsdata-agents/calibrate_probabilities)<a href="https://agentmods.dev/skills/danieltomaro13/sportsdata-agents/calibrate_probabilities"><img src="https://agentmods.dev/badge/skills/danieltomaro13/sportsdata-agents/calibrate_probabilities/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/danieltomaro13/sportsdata-agents/calibrate_probabilities"><img src="https://agentmods.dev/badge/skills/danieltomaro13/sportsdata-agents/calibrate_probabilities.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 | $0.00029 | $0.00345 |
| Opus 5 | $0.00015 | $0.00172 |
| Sonnet 5 | $0.00006 | $0.00069 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
calibrate_probabilities 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
Calibrating probabilities
A model that says 70% should be right ~70% of the time. Calibration is measured, never assumed.
Measure (always on holdout)
calibration_metrics(pairs)where pairs = holdout{prob, outcome}rows.- Brier: mean squared error. 0 = oracle; 0.25 = coin flip on a balanced set; beating the market baseline matters more than the absolute number.
- Log-loss: punishes confident wrongness. If log-loss looks much worse than Brier, the model is overconfident in its tails.
Fix overconfidence (in run_python)
- Shrink toward the base rate:
p' = w * p + (1 - w) * base_rate, fit w on a validation slice (grid over w ∈ [0.5..1.0] minimising log-loss is fine). - Platt-style: fit logistic regression of outcome on logit(p) — two parameters, hard to overfit; refuse fancier recalibration without more than ~200 samples.
- Re-run
calibration_metricsAFTER rescaling and report both before/after.
Persist
save_model with the post-calibration metrics and the rescaling parameters in
params — the next session must be able to reproduce the pipeline from the row.
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 · 31 lines · 29 tokens per session scan A 2c4dd2a710fe
calibrate_probabilities is a skill published in the GitHub repository DanielTomaro13/sportsdata-agents (5 stars, last pushed 9d ago), licensed MIT. It adds 29 tokens to every session and 345 once invoked, about $0.0001 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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