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 unit-economicsgit 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/unit-economics)<a href="https://agentmods.dev/skills/ololand-ai/ololand-plugins/unit-economics"><img src="https://agentmods.dev/badge/skills/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/skills/ololand-ai/ololand-plugins/unit-economics"><img src="https://agentmods.dev/badge/skills/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 | $0.00059 | $0.00962 |
| Opus 5 | $0.00030 | $0.00481 |
| Sonnet 5 | $0.00012 | $0.00192 |
| Haiku 4.5 | $0.00006 | $0.00096 |
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 6d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unit Economics & Cohort Analysis
What this is (and isn't)
This is deterministic computation, not prose. The cohort triangle, retention metrics, and unit-economics ratios come from numpy/pandas in services/financial/cohort_analyzer.py. The LLM's job is interpretation, not calculation.
The killer feature is reconciliation: when the CIM claims 115% NDR but the cohort data shows 92%, that 23-point spread is the kind of finding that closes IC memos. The analyze_unit_economics MCP tool detects these automatically when you pass stated_* parameters.
When to use
- Reviewing a SaaS or recurring-revenue deal
- Validating CIM claims about retention, churn, or sales efficiency
- Building cohort triangles for a quality of revenue analysis
- Stress-testing the LBO base case (does the model assume retention the cohorts don't support?)
How to use
Step 1 — Get the stated narrative
Read what the seller claims. Look in:
- The CIM's "key metrics" or "unit economics" section
- Management presentations
- Data room metrics dashboards
Capture:
- NDR (e.g. "115%")
- GRR (e.g. "94%")
- CAC payback months
- LTV/CAC ratio
If the seller doesn't state these, that's itself a finding — note it.
Step 2 — Load transactions
Format: [{customer_id, period: 'YYYY-MM-DD', revenue}, ...] — one row per customer per month.
Sources, in order of preference:
- Customer-level revenue export from the seller (cleanest)
- Subscription/billing system export (Stripe, Chargebee, Recurly)
- Reconstructed from CRM + invoice data
- Aggregated cohort data (less ideal — you lose granularity)
If you only have aggregated cohort data, the raw cohort triangle CAN'T be recomputed; you'll have to trust the seller's triangle and just compare the headline metrics.
Step 3 — Estimate LTV/CAC inputs
Pull from financials:
sales_marketing_spend— last 12 monthsnew_customers_in_period— count of new logos in the same periodgross_margin— as decimal (0.75, not 75)new_arr_in_period— for magic number (uses quarter-annualized formula)ebitda_marginandrevenue_growth_yoy— for rule of 40
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.
- 6d ago First seen · 88 lines · 59 tokens per session scan A a39a25b37b4c
unit-economics is a skill published in the GitHub repository ololand-ai/ololand-plugins (0 stars, last pushed 5d ago), licensed Apache-2.0. It adds 59 tokens to every session and 962 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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