LBO Model Builder
Build, validate, and format private-equity LBO models inside Excel templates.
The skill requires using the user-provided template first, obtaining section-by-section confirmation, and making formulas, formatting, and validation rules explicit. However, it does not address sensitive financial-data handling, backup/rollback, recovery isolation, or dependency security, so points are deducted.
The instructions are internally coherent and include template analysis, formula rules, common errors, and extensive checks. However, they depend on an external /mnt/skills/public/xlsx/recalc.py path and provide no key-path test evidence; abnormal-input handling and failure feedback remain limited, so points are deducted.
The target scenario is clear: completing LBO Excel templates, with separate Office JS and standalone-xlsx guidance and an interaction path when no template is supplied. Input formats, non-fit boundaries, trigger precision, and Chinese-language support are not sufficiently specified, so points are deducted.
The document has clear layering and includes template assumptions, example paths, naming/formatting conventions, limitations, and troubleshooting checks. The repository supplies an Apache-2.0 license and verified official provenance, but the skill lacks its own versioning, changelog, explicit maintenance owner, and update path, so points are deducted.
The intended output is a dynamically updating, professionally formatted LBO model with validation checks, which is directly useful for the stated workflow. Static review cannot verify formula correctness, template usability, or delivered outputs, and human review remains necessary; the score therefore respects the static ceiling and deducts for unverified results.
The skill contains detailed criteria for formula, balance, debt, returns, and sensitivity checks, while repository CI provides generic plugin validation and secret scanning. There is no committed test suite or third-party execution evidence covering the skill's key calculation paths, so points are deducted.
- Do not treat the checklist as evidence that validation was executed; manually review formulas, circularity, sensitivity tables, error values, and Sources/Uses balancing before delivery.
- recalc.py is referenced through an external absolute path and may be unavailable in the deployment environment; confirm the actual tooling, Excel calculation engine, and template files exist.
- The skill does not specify storage, transmission, cleanup, or access controls for sensitive financial data, nor a backup or rollback process.
What it does & when to use it
This skill completes leveraged buyout models for private-equity transactions, deal materials, and investment committee presentations. It prioritizes an attached Excel template and uses a standard template only when no user template is available. Calculations are written as Excel formulas and cover common sections such as Sources & Uses, operating projections, debt schedules, returns, and sensitivities. It also applies financial-model formatting conventions and performs structured validation checks.
Examines an attached LBO template to map sections, timelines, labels, existing formulas, and input conventions; populates Excel formulas from user assumptions instead of hardcoding calculated results; handles Sources & Uses, operating models, debt paydown, interest, IRR, MOIC, and sensitivity tables; distinguishes hardcodes, formulas, same-tab links, and cross-tab links with font colors; applies currency, percentage, and multiple formats; and, for standalone .xlsx workflows, uses Python/openpyxl with a recommended validation run through /mnt/skills/public/xlsx/recalc.py.
- A private-equity analyst needs to populate transaction assumptions into an existing LBO Excel template.
- An investment-banking team needs a dynamic LBO model for deal materials or an investment committee presentation.
- A model reviewer needs to test Sources & Uses, debt balances, cash flows, IRR, or MOIC for consistency.
- A modeling professional needs a base-case-centered 5×5 or 7×7 returns sensitivity table.
Pros & cons
- Explicitly preserves the user's template, making it suitable for established firm formats.
- Emphasizes dynamic Excel formulas, balance checks, circularity management, and debt-paydown priority.
- Provides detailed sensitivity-table and professional financial-model formatting guidance.
- The repository is Apache-2.0 licensed and is documented as deployable through Claude plugins or Managed Agent templates.
- Mandatory checkpoints after major sections limit unattended end-to-end modeling.
- Results depend on the template structure and user assumptions; ambiguous methods require clarification.
- The source provides no test suite, platform validation results, or standalone skill package.
- The documentation covers both Office JS and Python/openpyxl workflows, so the execution environment must support the relevant path.
How to install
In Claude Code, run: claude plugin marketplace add anthropics/financial-services; then run: claude plugin install pitch-agent@claude-for-financial-services. The skill is bundled under the Pitch Agent's skills/lbo-model/ directory. The README also supports adding https://github.com/anthropics/financial-services through Cowork's Settings → Plugins → Add plugin, or uploading a zip of plugins/agent-plugins/pitch-agent/. No standalone installation command for this individual skill is provided.
How to use
Example prompt: "Use the attached LBO_Model.xlsx and populate the transaction assumptions, Sources & Uses, operating projections, debt schedule, and IRR/MOIC while preserving the template. Show each major section for confirmation before continuing." If no template is attached, first ask whether the user has a specific LBO template; otherwise start from examples/LBO_Model.xlsx. The skill requires section-by-section checkpoints rather than presenting a fully built model without interim confirmation.