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/unit-economics)<a href="https://agentmods.dev/commands/ololand-ai/ololand-plugins/unit-economics"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/unit-economics/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/commands/ololand-ai/ololand-plugins/unit-economics"><img src="https://agentmods.dev/badge/commands/ololand-ai/ololand-plugins/unit-economics.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 | $0.00029 | $0.00620 |
| Opus 5 | $0.00015 | $0.00310 |
| Sonnet 5 | $0.00006 | $0.00124 |
| Haiku 4.5 | $0.00003 | $0.00062 |
Grade A, and why
unit-economics 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 8d 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unit Economics Analysis
Deterministic cohort and unit-economics analysis. Not LLM prose — actual computation that flags discrepancies between what the seller claims and what the data shows.
Usage
/unit-economics <deal_id> [transactions.csv]
Arguments
deal_id(required) — the OloLand deal ID.transactions.csv(optional) — customer-month revenue file with columns:customer_id,period(YYYY-MM-DD),revenue. If omitted, the tool will try to load transaction data extracted from the deal's financial documents.
Execution
Load the unit-economics skill, then:
- Pull stated narrative — call
mcp__ololand__get_financial_snapshotandget_dealto find the seller's claimed NDR/GRR/CAC payback/LTV-CAC. If the deal materials state these, capture them. - Load transactions — read the CSV (or use deal-extracted transactions) and convert to the
[{customer_id, period, revenue}]format. - Estimate inputs for LTV/CAC if available from the deal:
sales_marketing_spend(last 12 months)new_customers_in_periodgross_margin(decimal, e.g. 0.75)new_arr_in_period(for magic number)ebitda_margin,revenue_growth_yoy(for rule of 40)
- Run the analysis —
mcp__ololand__analyze_unit_economicswith all inputs including anystated_*claims. - Surface anomalies — anything in
result.anomaliesis a finding. High severity (>10pp NDR/GRR spread, >50% payback ratio deviation) belongs in the IC memo as a red flag.
Output
Report:
- Computed weighted NDR / GRR with cohort count
- LTV / CAC / payback / LTV-CAC ratio
- Magic number, rule of 40
- Exponential decay half-life (logo retention curve fit) + R²
- Anomalies table — metric, stated, computed, severity. If empty, state "narrative reconciles."
Then suggest:
/risk-report <deal_id>if anomalies are high severity/valuation <deal_id> read allto compare cohort findings with the governed model without changing it. Do not claim the cohort findings were incorporated. A fresh candidate requires a separate explicit/valuation <deal_id> refresh dcf, and only the returned model lineage can prove whether those inputs were applied.
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.
- 8d ago First seen · 48 lines · 29 tokens per session scan A 7683bcbf72b9
unit-economics is a command published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 4d ago), licensed Apache-2.0. It adds 29 tokens to every session and 620 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 2026-08-31.
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