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/ololand-ai/ololand-pluginsWrote 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/ololand-ai/ololand-plugins/benford)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/benford"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/benford.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 | $0.00024 | $0.00489 |
| Opus 5 | $0.00012 | $0.00244 |
| Sonnet 5 | $0.00005 | $0.00098 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
benford 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 3d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benford's Law
Benford's Law states that in many naturally-occurring datasets, the leading digit follows a logarithmic distribution: ~30% start with 1, ~17% with 2, etc. Significant deviation from this distribution is a classic forensic indicator of fabricated or selectively-entered transactions.
Usage
/benford <deal_id>
Arguments
deal_id(required) — The deal whose GL transactions to test. Requires uploaded GL export with at least 1,000 line items for statistical validity.
Execution
- If the user supplied a transaction list or GL file, parse it into transaction dictionaries and call
run_benford(transactions). If the user supplied onlydeal_id, callanalyze_forensic_qoe(deal_id)and extract the Benford section from the full battery. Do not pass aprimitivesargument. - The engine pulls all GL transactions from the deal's data room (or from
forensic_extraction_serviceif extraction has been run). - Computes observed first-digit distribution and compares to Benford expected via χ² goodness-of-fit and Mean Absolute Deviation (MAD).
- Flags account categories with the most significant deviations.
Interpretation
- MAD < 0.012 — close conformity (green)
- 0.012 ≤ MAD < 0.015 — acceptable conformity (yellow)
- MAD ≥ 0.015 — non-conformity (red — investigate the high-deviation accounts)
Output
For each account category that deviates significantly from Benford expected:
- Observed vs. expected first-digit distribution (table + chart)
- χ² statistic and p-value
- Top transactions contributing to the deviation
- Suggested follow-up: which specific journal entries to examine
Why this matters
Benford's Law is the deterministic statistical test most commonly cited in fraud forensics literature. It cannot be evaded by altering totals — it operates on the digit-level distribution of every transaction. Pre-LOI screening with Benford catches the deals where journal entries have been manipulated before you commit to fieldwork.
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.
- 3d ago First seen · 49 lines · 24 tokens per session scan A 484ad88f86a4
benford is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 2d ago), licensed Apache-2.0. It adds 24 tokens to every session and 489 once invoked, about $0.0001 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 2026-09-03.
Other commands, from other repositories
valuation-methods
Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction.
merit-reconcile
Preview or check the status of Stripe → Merit payout reconciliation (read-only).
audit-checklist
Perform an internal audit, review controls, or prepare for an external financial audit.
scan
Scan AWS account for cost optimization.
finops-status
Orientation — say where an opportunity or assignment sits in the five-step FinOps lifecycle and what unlocks next. Useful when a record has no active stage: an opportunity while its assignments do the work, an assignment whose plan has not been approved yet, or a rejected or archived assignment. Read-only; mutates…
audit
Scan for non-kernel money math. Rebuild critical flows as JournalEntrys. Replay and prove. Complements /ledger-verify.