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 onfire7777/universal-ai-skills-library --skill bayesian-reasoning-calibrationgit clone --depth 1 https://github.com/onfire7777/universal-ai-skills-libraryWrote 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/onfire7777/universal-ai-skills-library/bayesian-reasoning-calibration)<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/bayesian-reasoning-calibration"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/bayesian-reasoning-calibration/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/onfire7777/universal-ai-skills-library/bayesian-reasoning-calibration"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/bayesian-reasoning-calibration.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 | $0.00078 | $0.01790 |
| Opus 5 | $0.00039 | $0.00895 |
| Sonnet 5 | $0.00016 | $0.00358 |
| Haiku 4.5 | $0.00008 | $0.00179 |
Grade A, and why
bayesian-reasoning-calibration 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bayesian Reasoning & Calibration
Table of Contents
- Purpose
- When to Use This Skill
- What is Bayesian Reasoning?
- Workflow
- Common Patterns
- Guardrails
- Quick Reference
Purpose
Apply Bayesian reasoning to systematically update probability estimates as new evidence arrives. This helps make better forecasts, avoid overconfidence, and explicitly show how beliefs should change with data.
When to Use This Skill
- Making forecasts or predictions with uncertainty
- Updating beliefs when new evidence emerges
- Calibrating confidence in estimates
- Testing hypotheses with imperfect data
- Evaluating risks with incomplete information
- Avoiding anchoring and overconfidence biases
- Making decisions under uncertainty
- Comparing multiple competing explanations
- Assessing diagnostic test results
- Forecasting project outcomes with new data
Trigger phrases: "What's the probability", "update my belief", "how confident", "forecast", "prior probability", "likelihood", "Bayes", "calibration", "base rate", "posterior probability"
What is Bayesian Reasoning?
A systematic way to update probability estimates using Bayes' Theorem:
P(H|E) = P(E|H) × P(H) / P(E)
Where:
- P(H) = Prior: Probability of hypothesis before seeing evidence
- P(E|H) = Likelihood: Probability of evidence if hypothesis is true
- P(E|¬H) = Probability of evidence if hypothesis is false
- P(H|E) = Posterior: Updated probability after seeing evidence
Quick Example:
# Should we launch Feature X?
## Prior Belief
Before beta testing: 60% chance of adoption >20%
- Base rate: Similar features get 15-25% adoption
- Our feature seems stronger than average
- Prior: 60%
## New Evidence
Beta test: 35% of users adopted (70 of 200 users)
## Likelihoods
If true adoption is >20%:
- P(seeing 35% in beta | adoption >20%) = 75% (likely to see high beta if true)
If true adoption is ≤20%:
- P(seeing 35% in beta | adoption ≤20%) = 15% (unlikely to see high beta if false)
## Bayesian Update
Posterior = (75% × 60%) / [(75% × 60%) + (15% × 40%)]
Posterior = 45% / (45% + 6%) = 88%
## Conclusion
Updated belief: 88% confident adoption will exceed 20%
Evidence strongly supports launch, but not certain.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 183 lines · 78 tokens per session scan A a531ea5d1e6a
bayesian-reasoning-calibration is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed 1mo ago), licensed MIT. It adds 78 tokens to every session and 1,790 once invoked, about $0.0004 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 2026-09-03.
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