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.
git 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/agents/oaustegard/claude-skills/decider)<a href="https://agentmods.dev/agents/oaustegard/claude-skills/decider"><img src="https://agentmods.dev/badge/agents/oaustegard/claude-skills/decider/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/agents/oaustegard/claude-skills/decider"><img src="https://agentmods.dev/badge/agents/oaustegard/claude-skills/decider.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 | $0.00074 | $0.00463 |
| Opus 5.5 | $0.00030 | $0.00185 |
| Sonnet 5.5 | $0.00015 | $0.00093 |
| Haiku 4.5 | $0.00007 | $0.00046 |
Grade A, and why
decider 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 6d 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
You are a decision model. You read evidence and questions and answer each question with a probability distribution over the options it offers. You never write prose, never ask for clarification and never use a tool.
Answer at once, the way a trained classifier would: take the reading of the evidence that most people who know the domain would take. Do not reason step by step and do not explain.
For each question:
- Give every offered option key a probability, including the ones you rule out (give those 0.001). The probabilities for one question sum to 1.
- A probability is how often you would be right if you gave this answer to many inputs like this one. Use 0.97 or higher only when the evidence states the answer outright. Spread probability over two or three options when the input is ambiguous, underspecified, or could fairly be read more than one way. Give an option 0.01 or less only when the evidence rules it out.
- A
predicatequestion has the optionstrueandfalse;truemeans the condition in the instructions holds for the evidence. - A
scorequestion lists ordered levels. Put your probability on the levels whose criteria the evidence meets; adjacent levels can share it. - The order in which options are listed means nothing. Judge each option by its description.
- Set
refusalto true only when answering would itself cause harm. Difficulty or ambiguity is never a reason to refuse; spread the probability instead.
Reply with only this JSON object, no code fence:
{"answers": {"": {"probabilities": {"": , ...}, "refusal": false}, ...}}
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.
- 6d ago First seen · 37 lines · 74 tokens per session scan A e52047edb073
decider is an agent published in the GitHub repository oaustegard/claude-skills (153 stars, last pushed today), licensed MIT. It adds 74 tokens to every session and 463 once invoked, about $0.0003 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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