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Lbo Model

Official
anthropics·vlatest·other·Apache-2.0

Analyzes text to identify latent biases and potential harms, promoting fairness and responsible AI development.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add anthropics/financial-services --skill lbo-model -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

anthropics

Matched to the vendor's own GitHub organisation.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned anthropics/financial-services-plugins at 574ed3624a.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
574ed3624a
Manifest
plugins/agent-plugins/pitch-agent/skills/lbo-model/SKILL.md
Scanned
3 files, 819 lines
Copies in repo
3 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The lbo-model skill provides capabilities for performing Leveraged Buyout (LBO) modeling. It can generate financial models, calculate key LBO metrics like IRR and MoIC, and perform sensitivity analysis based on provided inputs. This allows users to quickly assess the feasibility of potential leveraged buyout transactions.

When to use it

  • Quick Feasibility Assessment: Evaluate a potential acquisition target's suitability for an LBO structure without building a full model from scratch.
  • Sensitivity Analysis: Understand how changes in key assumptions (e.g., interest rates, exit multiple) impact the overall returns of a deal.
  • Scenario Planning: Explore different financing structures and operating models to optimize potential LBO outcomes.
  • Initial Model Creation: Generate a baseline LBO model that can be further refined with more detailed data and assumptions.

Key capabilities

  • Financial modeling for LBO scenarios
  • Calculation of IRR (Internal Rate of Return)
  • Calculation of MoIC (Multiple on Invested Capital)
  • Sensitivity analysis functionality

Example prompts

  • "Create an LBO model for a company with $10 million in EBITDA and a target purchase price multiple of 10x."
  • "Perform a sensitivity analysis on the IRR of my LBO model, varying the exit multiple between 8x and 12x."
  • "What is the MoIC if I increase debt by 20%?"

Tips & gotchas

The skill requires familiarity with LBO modeling concepts and terminology. Input data should be accurate and reflect realistic assumptions for best results.