Borrowing it
Nothing to install: this file belongs to daloopa/investing. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/daloopa/investing/main/.claude/skills/unit-economics/SKILL.mdgit clone --depth 1 https://github.com/daloopa/investingWrote 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/daloopa/investing/unit-economics)<a href="https://agentmods.dev/skills/daloopa/investing/unit-economics"><img src="https://agentmods.dev/badge/skills/daloopa/investing/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/daloopa/investing/unit-economics"><img src="https://agentmods.dev/badge/skills/daloopa/investing/unit-economics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00015 | $0.03120 |
| Opus 5 | $0.00008 | $0.01560 |
| Sonnet 5 | $0.00003 | $0.00624 |
| Haiku 4.5 | $0.00002 | $0.00312 |
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 10d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- unit-economics — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Perform a bottoms-up unit economics decomposition for the company specified by the user: $ARGUMENTS
Before starting, read ../data-access.md for data access methods and ../design-system.md for formatting conventions. Follow the data access detection logic and design system throughout this skill.
Follow these steps:
1. Company Lookup
Look up the company by ticker using discover_companies. Capture:
company_idlatest_calendar_quarter— anchor for all period calculations below (see../data-access.mdSection 1.5)latest_fiscal_quarter- Firm name for report attribution (default: "Daloopa") — see
../data-access.mdSection 4.5
2. Series Discovery & Business Archetype Detection
Cast a wide net to discover ALL available series for this company. Search with multiple keyword sets to maximize coverage:
- Financial: "revenue", "income", "profit", "margin", "eps", "cost"
- Operating KPIs: "subscriber", "user", "customer", "unit", "arpu", "retention", "churn"
- Segment/Product: "segment", "product", "service", "geographic"
- Business-specific: "store", "gmv", "order", "booking", "backlog", "premium", "loan", "aum", "room", "seat", "bed", "acreage"
Collect all unique series IDs. Read every series name and description returned. This is how you learn what kind of business this is and what unit-level KPIs Daloopa tracks for it.
Based on series availability, classify the business into one of these archetypes (or a hybrid). This classification drives the entire report structure:
| If you find series like... | Archetype | Unit = |
|---|---|---|
| ARR, MRR, net dollar retention, customers, ACV, churn, CAC, LTV | SaaS / Subscription | Customer or subscription |
| Store count, same-store sales, AUV, restaurant-level margin, new openings | Unit-based retail / Restaurant | Store or unit |
| GMV, take rate, orders, AOV, active buyers/sellers | Marketplace / E-commerce | Order or transaction |
| Subscribers, ARPU, churn, content spend per sub | Consumer subscription (media/streaming) | Subscriber |
| Premiums written, loss ratio, combined ratio, policies in force | Insurance | Policy |
| NIM, loans, deposits, provision for credit losses, NCOs | Banking / Lending | Loan or account |
| ASP, units shipped, cost per unit, gross margin per unit | Hardware / Manufacturing | Unit shipped |
| AUM, management fee rate, performance fees, fund flows | Asset Management | Dollar of AUM |
| Revenue per available room (RevPAR), occupancy, ADR | Hospitality / Lodging | Room night |
| RPM, RASM, CASM, load factor, ASMs | Airlines / Transportation | Available seat mile |
| Revenue per user, DAU, MAU, ARPU, engagement | Digital platform / Advertising | User |
| Beds, admissions, revenue per admission, case mix | Healthcare facilities | Admission or bed |
| Acreage, production per acre, realized price per unit | Commodity / E&P | Unit of production |
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.
- 10d ago First seen · 204 lines · 15 tokens per session scan A 90ae87543ab0
unit-economics is a skill published in the GitHub repository daloopa/investing (487 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 15 tokens to every session and 3,120 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 2026-08-30.
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