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/ololand-ai/ololand-plugins/cmd-beneishnpx skills add ololand-ai/ololand-plugins --skill cmd-beneishgit 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/skills/ololand-ai/ololand-plugins/cmd-beneish)<a href="https://agentmods.dev/skills/ololand-ai/ololand-plugins/cmd-beneish"><img src="https://agentmods.dev/badge/skills/ololand-ai/ololand-plugins/cmd-beneish.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 | $0.00052 | $0.00544 |
| Opus 5 | $0.00026 | $0.00272 |
| Sonnet 5 | $0.00010 | $0.00109 |
| Haiku 4.5 | $0.00005 | $0.00054 |
Grade A, and why
ololand-forensic-qoe-beneish 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 2d 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 — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex wrapper for /beneish
Follow the OloLand command instructions below when the user asks for /beneish or the equivalent workflow in Codex.
Beneish M-Score
Compute the Beneish M-Score, an 8-variable model that estimates the probability of earnings manipulation. Originally designed for public companies; OloLand's implementation is private-company adjusted (handles missing market-cap inputs).
Usage
/beneish <deal_id>
Arguments
deal_id(required) — The deal whose financials to score. Requires at least two consecutive years of income statement + balance sheet data.
Execution
- Call
run_beneish(deal_id)from the MCP server. Do not pass aprimitivesargument toanalyze_forensic_qoe; that tool runs the full deal-scoped battery. - The engine pulls financial snapshots for the current and prior period from the deal's reconciled financial data.
- Computes the 8 Beneish variables: DSRI, GMI, AQI, SGI, DEPI, SGAI, LVGI, TATA.
- Aggregates to the M-Score using the original Beneish weights.
Interpretation
- M-Score < -2.22 — low likelihood of manipulation (green)
- -2.22 ≤ M-Score < -1.78 — borderline (yellow)
- M-Score ≥ -1.78 — high likelihood of manipulation (red — flag for management Q&A and consider full forensic review)
The output includes each variable's value, the historical distribution for similar industries, and the marginal contribution to the score. If any single variable is the dominant driver of a high score, that variable points at the specific area of concern (e.g. SGI = revenue growth too fast vs. peers; AQI = aggressive capitalization).
Why this matters
The Beneish M-Score is the most widely-cited forensic accounting heuristic. Big-4 QoE engagements run it routinely. Surfacing it pre-LOI lets you decide whether to advance the deal before committing to a $250K Big-4 engagement.
Example
/beneish deal_acme_2026
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.
- 2d ago First seen · 50 lines · 52 tokens per session scan A 937357364b9e
ololand-forensic-qoe-beneish is a skill published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed yesterday), licensed Apache-2.0. It adds 52 tokens to every session and 544 once invoked, about $0.0003 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.
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