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.
How do you install this skill?
- 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.
- Local filesystem
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.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/anthropics/financial-services.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/plugins/vertical-plugins/private-equity/skills/unit-economics" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
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.
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 does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Private Equity Unit Economics this page ✓ Anthropic · Official | 52 · Use with care | ★ 40k | 1mo ago | Apache-2.0 |
| PE Investment Committee Memo Writer ✓ Anthropic · Official | 54 · Use with care | ★ 40k | 1mo ago | Apache-2.0 |
| Private Equity Deal Screen ✓ Anthropic · Official | 51 · Use with care | ★ 40k | 1mo ago | Apache-2.0 |
| LBO Model Builder ✓ Anthropic · Official | 50 · Use with care | ★ 40k | 1mo ago | Apache-2.0 |
| Charlie CFO Skill | 53 · Use with care | ★ 325 | 8mo ago | MIT |
How did FollowSkills review this skill?
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.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →