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 agentmods add skills/aurora-neuro/aurora-agent/equal-engineering-baselinesnpx skills add AURORA-NEURO/aurora-agent --skill equal-engineering-baselinesgit 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/skills/aurora-neuro/aurora-agent/equal-engineering-baselines)<a href="https://agentmods.dev/skills/aurora-neuro/aurora-agent/equal-engineering-baselines"><img src="https://agentmods.dev/badge/skills/aurora-neuro/aurora-agent/equal-engineering-baselines.svg" alt="Measured on agentmods" 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 | $0.00095 | $0.01259 |
| Opus 5 | $0.00048 | $0.00629 |
| Sonnet 5 | $0.00019 | $0.00252 |
| Haiku 4.5 | $0.00010 | $0.00126 |
Grade A, and why
equal-engineering-baselines 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.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Note: the crate paths, documents, and measured numbers below are illustrations from the aurora-agent workspace where these methods were developed and tested. The methods themselves apply to any comparison or benchmarking effort.
Equal-engineering baselines
A comparison is only evidence if the baselines were engineered as seriously as the system being
sold. The workspace learned this from its own upstream distribution, and encoded the lesson in a
harness (crates/baseline) whose design decisions are worth copying anywhere.
Tune the baseline where it wins, not where it loses
The distribution's own comparison script measured its graph baseline at depth 7 and unbounded
only — the two settings where the walk returns the entire 761-fact world. It never measured
depths 5 or 6, where the identical code returns 11 facts, matching the compiler exactly. The
published comparison therefore showed a 69x advantage that disappears entirely under equal
tuning (docs/FINDINGS.md, "The distribution's own baseline script is a strawman").
The method:
- Sweep every tuning knob the baseline has — depth for walks, k for retrievers — and report the baseline at its best setting, not at the setting that flatters your system.
- Give each family its strongest member. The workspace's panel runs graph walks at four
depths, a connected-component upper bound, two lexical budgets, two embedding budgets, and an
unbounded directed walk (
crates/baseline,default_panel()), alongside full-context as the recall ceiling. - Label proxies as proxies. The panel's "embedding" retriever is a fixed-basis hashed-trigram model and every report line says so: "a lexical proxy for embedding retrieval, not a neural model." A baseline that impersonates a stronger method inflates your win over the real thing.
- If a baseline stays competitive under equal optimization, report that result. On the reference world, three differently-engineered baselines select the identical eleven facts the compiler selects — not the same count, the identical set — and the comparison document says the cheapest admissible strategy is the graph walk, not the compiler.
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 · 94 lines · 95 tokens per session scan A f3a5a33fc846
equal-engineering-baselines is a skill published in the GitHub repository AURORA-NEURO/aurora-agent (1 stars, last pushed 3d ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,259 once invoked, about $0.0005 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 2026-08-31.
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