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-interval-calibration-checkgit 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-interval-calibration-check)<a href="https://agentmods.dev/skills/product-on-purpose/thinking-framework-skills/think-interval-calibration-check"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-interval-calibration-check/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-interval-calibration-check"><img src="https://agentmods.dev/badge/skills/product-on-purpose/thinking-framework-skills/think-interval-calibration-check.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 | $0.00127 | $0.02312 |
| Opus 5 | $0.00063 | $0.01156 |
| Sonnet 5 | $0.00025 | $0.00462 |
| Haiku 4.5 | $0.00013 | $0.00231 |
Grade A, and why
think-interval-calibration-check 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interval Calibration Check
People state uncertainty as intervals - "two to four weeks, 90 percent sure" - and those intervals are reliably too narrow. Overprecision is the most robust form of overconfidence: stated 90 percent intervals contain the true value far less than 90 percent of the time, and subjective intervals are sometimes only a fraction as wide as the judge's own information would warrant. A stated "90" that historically hits 50 is not a confidence level, it is a habit of speech, and everything downstream that takes the number literally - an expected-value calculation, a risk model, a commitment - inherits the error. This method interrogates the WIDTH of a stated uncertainty: does your 90 mean 90? It runs two coupled moves that both operate on the width and never on the location of the estimate - an equivalent-bet indifference test at elicitation time, and hit-rate scoring against resolved outcomes - and emits a calibration scorecard. The durable move is not asking "how sure are you?" again. It is converting that question into a concrete bet, widening until the bet is genuinely a toss-up, and scoring the stated confidence against the truths that actually arrive.
When to Use
- A consequential plan, forecast, or commitment rests on a stated interval or confidence number that has never been audited - the "90 percent sure we ship in Q3" plan, the cost range in a proposal, the confidence column in a decision journal or assumption ledger.
- The same person or team makes repeated resolvable estimates, so a track record exists or can accumulate and the scored-feedback half has material to work with.
- A method that consumes probability numbers at face value sits immediately downstream (an expected-value decision tree, a risk model) - calibrate the inputs before the arithmetic launders them.
- The worry is that the stated confidence is too tight to trust (overprecision), not that the central number is in the wrong place.
What ships with it
5 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 · 69 lines · 127 tokens per session scan A 277ecc209748
think-interval-calibration-check 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 127 tokens to every session and 2,312 once invoked, about $0.0006 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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