Private Equity Unit Economics
Assess revenue quality and customer economics in software and subscription businesses.
The skill provides analysis guidance only and does not claim external writes, transaction execution, or privileged operations; it does request raw customer-level data. However, it lacks sensitive-data handling, least-privilege, user-confirmation, data-flow, rollback, and source-verification guidance, so points are deducted.
The workflow is internally coherent across business-model identification, metrics, benchmarks, scoring, and outputs. It provides no abnormal-input handling, failure feedback, dependency notes, or reproducible tests, and some formulas and thresholds lack applicability conditions; therefore the static score is limited.
Audience, use cases, trigger phrases, and SaaS, services, transaction-based, and hybrid models are clearly described. Non-fit boundaries, input formats, industry variation, and Chinese-language support remain underspecified, so points are deducted. No core dependence on unreachable overseas services is shown.
The document has clear structure, tables, examples, limitation notes, an Apache-2.0 license, and repository-level CI evidence. The skill itself lacks versioning, changelog, maintenance ownership, update path, installation notes, and FAQs, so points are deducted.
ARR bridges, cohort analysis, LTV/CAC, retention, margin waterfalls, and red flags provide a directly useful analytical framework. However, the promised Excel workbook and summary slide lack output specifications or validated examples, leaving substantial manual work; the score follows the static-review cap.
The skill text is auditable, and the repository includes plugin-validation and secret-scanning workflows. There is no skill-specific test suite, third-party execution evidence, citation set, or corroborating analysis, so evidence coverage is limited.
- Do not treat the LTV:CAC, NDR, Rule of 40, or CAC-payback thresholds as universal conclusions; validate business model, period, denominator definitions, and data quality.
- Raw customer-level data may be sensitive; establish access control, redaction, retention, and human-review procedures before use.
- The skill promises an Excel workbook and summary slide but does not define templates, fields, formula checks, or delivery formats, so manual reconstruction may be required.
What does this skill do, and when should you use it?
This skill is designed for private-equity analysis of target companies, covering ARR, retention, LTV/CAC, CAC payback, revenue quality, and margin waterfalls. It supports SaaS, subscription, recurring-services, transaction or usage-based, and hybrid revenue models. The workflow includes ARR bridges, annual cohort analysis, retention and expansion decomposition, concentration analysis, and revenue-type segmentation. Its outputs are aimed at investment or ongoing diligence: a metrics workbook, summary slide, red flags, and follow-up diligence areas.
Identifies the business model; builds a beginning-ARR-to-ending-ARR bridge; analyzes annual ARR cohorts with both absolute-dollar and Year-0-indexed views; calculates CAC, LTV, LTV:CAC, and CAC payback; separates gross retention, net retention, logo churn, dollar churn, and expansion; evaluates customer concentration, contract structure, and recurring versus non-recurring revenue; builds a revenue-to-gross-profit-to-contribution-margin-to-EBITDA waterfall; benchmarks Rule of 40, SaaS Magic Number, NDR, LTV:CAC, gross retention, and CAC payback; produces a revenue-quality score, Excel workbook, summary slide, red flags, and further diligence items.
- A private-equity deal team evaluates a SaaS target’s ARR quality and retention during screening or diligence.
- An investment professional uses customer-level data to test whether annual cohorts retain and expand revenue.
- A deal team compares CAC, LTV, and payback across enterprise, mid-market, and SMB segments.
- A portfolio team monitors NDR, churn, expansion, and margin changes after investment.
What are this skill's strengths and limitations?
- Covers ARR, cohorts, retention, expansion, customer economics, and margin analysis in one workflow.
- Explicitly distinguishes gross retention from net retention, contracted ARR from recognized revenue, and professional-services revenue from recurring revenue.
- Includes usable benchmark ranges, a revenue-quality scoring table, and concrete diligence outputs.
- The source provides no test suite, sample input files, or demonstrated generated outputs.
- The Excel workbook and summary slide are specified as outputs, but no templates, file-level details, or generation tools are documented.
- Metrics such as LTV depend heavily on the quality of customer-level or aggregate inputs; the skill does not supply external data.
How do you install this skill?
In Cowork, open “Settings → Plugins → Add plugin,” paste https://github.com/anthropics/financial-services, and select the private-equity vertical from the marketplace list. The README does not provide a standalone Claude Code installation command for this vertical.
How do you use this skill?
After installation, use /unit-economics or enter trigger phrases such as “unit economics,” “cohort analysis,” “ARR analysis,” “LTV CAC,” “net retention,” “revenue quality,” or “customer economics.” Provide raw customer-level data when available, along with the revenue model, analysis period, and desired outputs.