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 AURORA-NEURO/aurora-agent --skill honest-figuresgit 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/honest-figures)<a href="https://agentmods.dev/skills/aurora-neuro/aurora-agent/honest-figures"><img src="https://agentmods.dev/badge/skills/aurora-neuro/aurora-agent/honest-figures/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/aurora-neuro/aurora-agent/honest-figures"><img src="https://agentmods.dev/badge/skills/aurora-neuro/aurora-agent/honest-figures.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 | $0.00102 | $0.01267 |
| Opus 5 | $0.00051 | $0.00633 |
| Sonnet 5 | $0.00020 | $0.00253 |
| Haiku 4.5 | $0.00010 | $0.00127 |
Grade A, and why
honest-figures 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 12d 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 — 97 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 figure or table built from measured data.
Honest figures
A figure is a claim with better production values. Everything the workspace enforces about claims — verifiability, refusal-honesty, negative results as results — applies with more force to figures, because figures are what readers remember and what gets screenshotted out of context.
Every figure carries its source digest
A figure should be reproducible from a retained computation, and should say so on its face:
- Derive figures from digest-bearing artifacts — a certified output's own content hash, a deterministic sweep table (same grid + seed gives byte-identical bytes, asserted by test), a fixture with a pinned digest. Print the digest and the generating command in the caption or the margin.
- Make regeneration a test with the claim in its name —
a_sweep_figure_is_byte_stable_for_a_fixed_table— so a figure that drifts from its data fails a build rather than surviving as a stale image. - The workspace's findings document applies the same rule to tables: every number is asserted by a named test, and where a table is transcribed rather than pinned, the document says which rows are reproduced, not pinned and names the command that reprints the source table. A transcription that does not disclose it is a transcription that cannot be audited.
Refused and absent are drawn as refused and absent, never as zero
The comparison harness's JSON omits the judgement keys entirely on a refused row — absence is
semantic — and the scoring plane keeps scored, unscored (with a reason), and inapplicable
as three distinct states, where an inapplicable cell "is not a zero and does not lower a
fixed-input model's average for an action it was never designed to take"
(docs/BIOEVAL_PLANE_AUDIT.md). A renderer is where that discipline usually dies: a chart
library coerces a missing value to zero, and a refusal becomes the shortest bar — visually,
the cheapest attempt.
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.
- 12d ago First seen · 97 lines · 102 tokens per session scan A 7950844ed3df
honest-figures is a skill published in the GitHub repository AURORA-NEURO/aurora-agent (1 stars, last pushed 2d ago), licensed Apache-2.0. It adds 102 tokens to every session and 1,267 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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