Claude for Financial Services is a collection of agents, skills, commands, plugins, and data connectors for investment banking, equity research, private equity, and wealth-management workflows. Financial professionals use it to draft models, memos, research notes, and reconciliations for review by qualified people. The catalogue contains components from these workflows, including agents, skills, plugins, commands, and instructions.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/anthropics/financial-servicesnpx agentmods add skills/anthropics/financial-services/lbo-modelWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/anthropics/financial-services/lbo-model)<a href="https://agentmods.dev/skills/anthropics/financial-services/lbo-model"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/lbo-model/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/anthropics/financial-services/lbo-model"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/lbo-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00057 | $0.03734 |
| Opus 5 | $0.00028 | $0.01867 |
| Sonnet 5 | $0.00011 | $0.00747 |
| Haiku 4.5 | $0.00006 | $0.00373 |
Grade A, and why
lbo-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TEMPLATE REQUIREMENT
This skill uses templates for LBO models. Always check for an attached template file first.
Before starting any LBO model:
- If a template file is attached/provided: Use that template's structure exactly - copy it and populate with the user's data
- If no template is attached: Ask the user: "Do you have a specific LBO template you'd like me to use? If not, I can use the standard template which includes Sources & Uses, Operating Model, Debt Schedule, and Returns Analysis."
- If using the standard template: Copy
examples/LBO_Model.xlsxas your starting point and populate it with the user's assumptions
IMPORTANT: When a file like LBO_Model.xlsx is attached, you MUST use it as your template - do not build from scratch. Even if the template seems complex or has more features than needed, copy it and adapt it to the user's requirements. Never decide to "build from scratch" when a template is provided.
CRITICAL INSTRUCTIONS FOR CLAUDE - READ FIRST
Environment: Office JS vs Python
If running inside Excel (Office Add-in / Office JS environment):
- Use Office JS (
Excel.run(async (context) => {...})) directly — do NOT use Python/openpyxl - Write formulas via
range.formulas = [["=B5*B6"]]— Office JS formulas recalculate natively in the live workbook - The same formulas-over-hardcodes rule applies: set
range.formulas, neverrange.valuesfor anything that should be a calculation - Use
range.format.font.color/range.format.fill.colorfor the blue/black/purple/green convention - No separate recalc step needed — Excel handles calculation natively
- Merged cell pitfall: Do NOT call
.merge()then set.valueson the merged range (throwsInvalidArgument— range still reports original dimensions). Instead: write value to top-left cell alone (ws.getRange("A7").values = [["SOURCES & USES"]]), then merge + format the full range (ws.getRange("A7:F7").merge(); ws.getRange("A7:F7").format.fill.color = "#1F4E79";)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 275 lines · 57 tokens per session scan A 19749a568d69
lbo-model is a skill published in the GitHub repository anthropics/financial-services (34,747 stars, last pushed 14d ago), licensed Apache-2.0. It adds 57 tokens to every session and 3,734 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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