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 oaustegard/claude-skills --skill deciding-with-confidencegit clone --depth 1 https://github.com/oaustegard/claude-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/oaustegard/claude-skills/deciding-with-confidence)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/deciding-with-confidence"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/deciding-with-confidence/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/oaustegard/claude-skills/deciding-with-confidence"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/deciding-with-confidence.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 | $0.00162 | $0.02638 |
| Opus 5.5 | $0.00065 | $0.01055 |
| Sonnet 5.5 | $0.00032 | $0.00528 |
| Haiku 4.5 | $0.00016 | $0.00264 |
Grade A, and why
deciding-with-confidence 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 7d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deciding with confidence
scripts/decide.py takes an OpenAI Decisions API request (input plus
questions of type predicate, choice or score) and returns its response
shape: a probability per option, the chosen value or score, and confidence.
Claude Haiku 5.5 answers. Its logits are not exposed, so the distribution is
estimated: each of k parallel samples states a probability per option with the
options in a different order, the samples are averaged, and the average is
temperature-scaled from a labelled eval. Disagreement between samples lowers
confidence and shows up in diagnostics.agreement.
Expect about 85% of what a purpose-built decision model gets: on the bundled
104-item eval, accuracy 0.846 against Jev's 0.894, with a calibration error
(ECE 0.041) that matches it. The whole accuracy gap was on eight-way intent
questions; yes/no and rubric questions tied. Disagreement between samples is
the strongest warning sign. references/method.md has the numbers.
When NOT to use this skill
| Situation | Use instead |
|---|---|
| The label set is too large to list in a prompt (a taxonomy, hundreds of tags) | hallucinating-labels |
| The answer needs reasoning, multi-step work or a written explanation | a normal Claude call; this skill forbids explanation |
| You need extracted fields or free-form JSON | structured outputs on the Messages API |
A real decision model is reachable (OpenAI /v1/decisions, Strands Decider, Jev) and latency matters |
that model: 0.1–0.3 s against ~0.7–4 s here |
| Picking which Claude tier runs a subagent | agent-routing |
| The evidence is an image | not supported; the transports send text only |
Abandon a run when diagnostics.agreement is below 1 on most of a batch: the
questions or option descriptions are ambiguous, and more samples will not fix
them. Rewrite the options (distinct, observable criteria) before sampling more.
Procedure
- Write the request as JSON in the OpenAI shape. Give every question a
unique
name. Options needdescriptions that say when each applies; add anotherchoice when the set is not exhaustive. Scorelevelsgo lowest first.
What ships with it
9 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.
- 7d ago First seen · 182 lines · 162 tokens per session scan A 67ed28b02022
deciding-with-confidence is a skill published in the GitHub repository oaustegard/claude-skills (153 stars, last pushed yesterday), licensed MIT. It adds 162 tokens to every session and 2,638 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-10-10.
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