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 brcampidelli/chimera-agent --skill system-one-designgit clone --depth 1 https://github.com/brcampidelli/chimera-agentWrote 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/brcampidelli/chimera-agent/system-one-design)<a href="https://agentmods.dev/skills/brcampidelli/chimera-agent/system-one-design"><img src="https://agentmods.dev/badge/skills/brcampidelli/chimera-agent/system-one-design/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/brcampidelli/chimera-agent/system-one-design"><img src="https://agentmods.dev/badge/skills/brcampidelli/chimera-agent/system-one-design.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 | $0.00044 | $0.01123 |
| Opus 5.5 | $0.00018 | $0.00449 |
| Sonnet 5.5 | $0.00009 | $0.00225 |
| Haiku 4.5 | $0.00004 | $0.00112 |
Grade A, and why
system-one-design 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 21d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Trigger
A step asks a model a question whose answer is one of a few known values — yes or no, one of five labels, a level from low to high — and then reads that value out of the prose it wrote. Or the same question is asked of hundreds of items. That is a typed decision wearing a chat costume.
Do
- Ask it as a typed question (
chimera decide,POST /api/decide, or thedecidetool):noulfor yes/no,choicefor one of a set,scorefor ordered levels. A probability comes back. - One condition per question. "Is it an error and from the database?" is two questions; ask both and combine the answers in code, where the rule is visible.
- Give options names that do not carry the verdict (not "yes", "safe", "pass") and put the meaning
in the criteria. A catch-all option ("other") is unreliable in both directions — measure it on
your items, give it a narrow criterion, or replace it with a
noulper candidate when rejection matters. Make the options' first words differ: a local model reads the label from its first token, socodingandcoding_agentcan never be told apart (24 of 231 public JevBench items went unread this way). On the catch-all, measured on 1,000 commit subjects: a broad "other" took 78% of the items that belonged to a named option and a narrow one 58%; leaving it out lifted named-class F1 from 0.24 to 0.43, at the price that items truly outside the set land on a named option. The catch-all fails in either direction, by model and task: here it absorbed items it should not have, while a hosted decision model picked "None/Other" for only 7% of items whose right answer was missing (arXiv 2609.39496). When rejecting matters, ask anoulper candidate (99% rejected there) or set a threshold on the catch-all's probability from labelled examples (79% rejected, 97% of answerable items kept). - Show the model the thing being judged, alone: not the tool output around it, not a sentence that argues for an answer.
- Pick any threshold from labelled examples, never from a guess, and keep the number beside the decision it fed.
- Keep arithmetic, dates and counts in code: compute the fact, put it in the state, and ask about it. A decision model reads; it does not calculate (on JevBench's hard tier the local model got 1 of 15 date-and-number items right).
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.
- 21d ago Changed · +6 lines 99848d8b85c7
- 1mo ago Changed · +14 lines 347bfae9f826
- 1mo ago First seen · 62 lines · 44 tokens per session scan A 25fc5a6b7f92
system-one-design is a skill published in the GitHub repository brcampidelli/chimera-agent (29 stars, last pushed today), licensed Apache-2.0. It adds 44 tokens to every session and 1,123 once invoked, about $0.0002 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 2026-09-24.
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