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 ololand-ai/ololand-plugins --skill cmd-lapping-checkgit 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-lapping-check)<a href="https://agentmods.dev/skills/ololand-ai/ololand-plugins/cmd-lapping-check"><img src="https://agentmods.dev/badge/skills/ololand-ai/ololand-plugins/cmd-lapping-check.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 | $0.00057 | $0.00571 |
| Opus 5 | $0.00028 | $0.00285 |
| Sonnet 5 | $0.00011 | $0.00114 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade A, and why
ololand-forensic-qoe-lapping-check 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex wrapper for /lapping-check
Follow the OloLand command instructions below when the user asks for /lapping-check or the equivalent workflow in Codex.
AR Lapping Detection
Lapping is one of the oldest receivables-fraud schemes: customer A's payment is applied to customer B's overdue invoice; later, customer C's payment is applied to A; etc. The cycle hides bad debts and (in more aggressive variants) fictitious revenue. Detection requires looking at the timing and pattern of cash applications, not just totals.
Usage
/lapping-check <deal_id>
Arguments
deal_id(required) — The deal to test. Requires AR aging schedule + cash receipts journal with customer-level detail and date stamps.
Execution
- Call
analyze_forensic_qoe(deal_id)and extract the lapping-detection section from the full battery. Do not pass aprimitivesargument. If AR aging or cash-receipt detail is missing, classify the result as a diligence gap rather than a clean finding. - The engine cross-references cash receipts to AR aging movements and looks for:
- Application gaps — payment received from customer A but applied to customer B's invoice
- Aging cycles — invoices that move from 60-day to 30-day aging after a non-payment cash receipt
- Round-trip patterns — a customer's invoice paid by another customer's payment, with the original customer paying a third party's overdue invoice within N days
- Returns suspect cycles with timing diagrams and the dollar amount in motion.
Output
For each detected cycle:
- The customers involved and the cash flow path
- The "true" aged balance after correcting the misapplications
- Estimate of bad-debt exposure that AR aging is currently hiding
- Severity (suggestive / probable / strong evidence)
Why this matters
Lapping is invisible to standard AR aging reviews because aging looks correct on the surface — the application is wrong, not the totals. Big-4 QoE catches lapping by tracing customer-to-cash. OloLand's lapping detector runs the same trace deterministically, in seconds, on the GL + AR aging exports already in the data room.
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 · 52 lines · 57 tokens per session scan A e17cee08cd23
ololand-forensic-qoe-lapping-check is a skill published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 2d ago), licensed Apache-2.0. It adds 57 tokens to every session and 571 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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