LBO Model Builder
Build, validate, and format private-equity LBO models from Excel templates.
The skill requires using the user's template first, requesting confirmation at major stages, and performing formula and integrity checks, which reduces unauthorized modeling risk. It does not address sensitive financial-data handling, permission boundaries, data-flow disclosure, rollback, or dependency security, so points are deducted.
The instructions are detailed and internally organized across Office JS, openpyxl, formulas, circularity, debt paydown, and verification. However, the referenced standard template and /mnt/skills/public/xlsx/recalc.py are not supplied in the evidence, and there are no key-path tests or robust abnormal-input failure messages; under static calibration the score stays below 10.
The target audience and scenarios are clear: LBO models for private-equity transactions, deal materials, and investment-committee presentations. Non-fit cases, precise trigger exclusions, input/output contracts, Chinese-language support, and the required Excel/Python environment are not adequately specified, so points are deducted.
The document has clear sections covering templates, formatting, common errors, verification, and user checkpoints. The repository supplies an Apache-2.0 license, official publisher provenance, and general plugin CI, but the skill lacks its own version, changelog, named maintenance responsibility, update path, and complete dependency/install guidance.
The skill gives concrete guidance for dynamic LBO formulas, formatting, debt schedules, returns, and sensitivity tables, so the core value proposition is plausible. Templates, scripts, and representative outputs are not verified in the supplied files, and human review remains necessary; the static ceiling therefore supports only a moderate score.
The revision is pinned, the repository includes plugin-validation and secret-scanning workflows, and the skill contains auditable formula rules and checklists. There is no skill-specific test suite, third-party execution evidence, or multi-source corroboration, so verifiability remains limited.
- Do not treat this as a validated investment or accounting model; qualified professionals should review formulas, assumptions, sign conventions, circularity, and sensitivities before delivery.
- The skill depends on a standard template and recalc.py script that are not included in the supplied evidence; confirm availability, compatibility, and safe execution before use.
- Handling, storage, transmission, and isolation of sensitive financial data are unspecified, as are failure recovery and rollback procedures.
- Chinese-language output conventions and mainland-China network compatibility are not documented.
What it does & when to use it
This skill completes leveraged buyout models in Excel for private-equity transactions, deal materials, and investment committee presentations. It prioritizes an attached user template and, when none is provided, directs the user toward a standard template covering Sources & Uses, the operating model, debt schedule, and returns analysis. Calculations are maintained as dynamic Excel formulas rather than hardcoded outputs, with distinct font colors for inputs, formulas, same-tab links, and cross-tab links. It also validates balances, debt paydown logic, returns calculations, sensitivity tables, and professional formatting.
It first inspects an attached template's structure, timeline, existing formulas, labels, and input areas. It then populates Sources & Uses, operating projections, debt schedules, returns analysis, and sensitivity tables section by section. The instructions cover formula-driven calculations, circularity avoidance for interest, debt priority and cash sweeps, IRR, MOIC, odd-dimension sensitivity tables, number formats, font colors, fills, and alignment. For standalone xlsx generation, it can use Python/openpyxl to write formulas and run the specified recalc.py validation script.
- A private-equity analyst has transaction assumptions and an existing LBO template that needs to be populated through exit returns.
- An investment-banking team needs a structured LBO workbook for deal materials or an investment committee presentation.
- A model reviewer needs to test Sources & Uses balancing, debt floors, cash sweep priority, and return formula signs.
- An investment professional needs IRR or MOIC sensitivity tables centered on the base case with varied formula-driven outputs.
Pros & cons
- Explicitly preserves and adapts the user's template structure.
- Emphasizes dynamic formulas, circularity management, debt cash sweeps, and return-sign checks.
- Provides concrete conventions for font colors, fills, number formats, and sensitivity tables.
- Uses section-by-section checkpoints to catch errors earlier.
- Depends on an available LBO template; the referenced standard template's contents are not included in the supplied material.
- Standalone xlsx workflows require Python, openpyxl, and the specified recalc.py script, whose availability is not verified here.
- The source provides no test suite, sample output, or independent platform validation.
- It does not supply transaction data or investment advice; assumptions and professional review remain the user's responsibility.
How to install
Add the repository as a Claude plugin marketplace: claude plugin marketplace add anthropics/financial-services; then install the core financial-analysis plugin: claude plugin install financial-analysis@claude-for-financial-services. The source does not provide a separate installation command for lbo-model alone.
How to use
After installation, use /lbo, or provide a prompt such as: “Use my attached LBO_Model.xlsx as the template. Populate Sources & Uses, the operating model, debt schedule, and returns analysis section by section, showing verification results and waiting for confirmation after each section.” If no template is attached, the skill should ask whether the user has a specific template.
Compared to similar skills
Compared with building an LBO model from scratch, this skill places more emphasis on preserving an existing template, formula-driven updates, section checkpoints, and professional formatting. The source does not provide a direct comparison with any named competing LBO tool or skill.