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 product-on-purpose/thinking-framework-skills --skill think-natural-frequency-bayesiangit clone --depth 1 https://github.com/product-on-purpose/thinking-framework-skillsWrote 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/product-on-purpose/thinking-framework-skills/think-natural-frequency-bayesian)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-natural-frequency-bayesian"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-natural-frequency-bayesian/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/product-on-purpose/thinking-framework-skills/think-natural-frequency-bayesian"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-natural-frequency-bayesian.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 | $0.00080 | $0.00954 |
| Opus 5 | $0.00040 | $0.00477 |
| Sonnet 5 | $0.00016 | $0.00191 |
| Haiku 4.5 | $0.00008 | $0.00095 |
Grade A, and why
think-natural-frequency-bayesian 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 11d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Natural-Frequency Bayesian Framing
People - including experts - reason badly about conditional probabilities stated as percentages, because they neglect the base rate. Re-expressing the same facts as natural frequencies over a concrete population makes the correct answer nearly visible: "Out of 1,000, 10 have it; 9 of those test positive; of the 990 without it, ~89 also test positive; so of ~98 positives, only 9 truly have it - about 9%." The format does the work by keeping the base rate in the counts. The output is a natural-frequency breakdown. Honest constraint: the base rate and hit rates must be real - the format makes correct reasoning tractable, it does not invent the inputs.
When to Use
- Interpreting a test or screening result (medical, fraud, security, lead-scoring, A/B).
- Any "given a positive signal, what is the actual probability the thing is true?" question.
- Communicating risk to others so they do not over-read a positive.
When NOT to Use
- When you do not have real input rates and would have to invent them.
- When there is no conditional-probability structure to the question.
- For general project forecasting (use reference-class forecasting).
- When a single point estimate is wanted and the base-rate structure is irrelevant.
Instructions
When asked to reason about a conditional probability, follow these steps:
- State the question precisely. What posterior is being asked - usually P(condition | positive signal). Distinguish it from P(positive | condition), which people confuse it with.
- Gather the real inputs. The base rate, the true-positive (hit) rate, and the false-positive rate. If any is unknown, say so and stop or clearly flag the estimate as illustrative - do not fabricate numbers.
- Build a frequency tree over a concrete population. Pick a round number (e.g., 1,000). Work out: how many have the condition; of those, how many test positive; of those without, how many also test positive.
- Compute the posterior as true positives / all positives, and state it plainly.
- Name the wrong intuition it corrects. State the answer most people give (usually near the hit rate) and why it is wrong (base-rate neglect).
- Emit the natural-frequency breakdown per
references/TEMPLATE.md.
What ships with it
6 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.
- 11d ago First seen · 63 lines · 80 tokens per session scan A 3888bee66271
think-natural-frequency-bayesian is a skill published in the GitHub repository product-on-purpose/thinking-framework-skills (15 stars, last pushed today), licensed Apache-2.0. It adds 80 tokens to every session and 954 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 2026-08-30.
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