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/AURORA-NEURO/aurora-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/commands/aurora-neuro/aurora-agent/compare)<a href="https://agentmods.dev/commands/aurora-neuro/aurora-agent/compare"><img src="https://agentmods.dev/badge/commands/aurora-neuro/aurora-agent/compare/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/commands/aurora-neuro/aurora-agent/compare"><img src="https://agentmods.dev/badge/commands/aurora-neuro/aurora-agent/compare.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 | $0.00026 | $0.00299 |
| Opus 5.5 | $0.00010 | $0.00120 |
| Sonnet 5.5 | $0.00005 | $0.00060 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
compare 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 1mo 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
Run the baseline comparison and report it with its honesty semantics intact.
- Resolve the aurora-agent checkout (
$AURORA_AGENT_ROOT→~/aurora-agent→~/bioprism); CLI at<root>/target/release/bioprism(.exe). - Defaults if
$ARGUMENTSis empty: the reference fixture pair (fixtures/fiber-v0.1/radiogenomic_world.json,fixtures/fiber-v0.1/leakage_query.json). - Run from the checkout root:
bioprism --json context compare --world <world> --query <query> - Report per strategy: facts exposed, fraction of world, status,
verdict_preserving, admissible, protected recall, missing witnesses.
CRITICAL: a
"valid"status from a strategy whoseverdict_preservingis false is a FALSE verdict — judge strategies by verdict preservation, never by the status column alone. Name thecheapest_admissible_strategyverbatim, including when it is a baseline rather than FIBER — that honest headline is the point of the tool.
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.
- 1mo ago First seen · 22 lines · 26 tokens per session scan A fb9b122e8ccd
compare is a command published in the GitHub repository AURORA-NEURO/aurora-agent (1 stars, last pushed 17d ago), licensed Apache-2.0. It adds 26 tokens to every session and 299 once invoked, about $0.0001 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 2026-08-31.
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checklist
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clarify
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specify
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converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.