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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/anthropics/financial-services/3-statement-modelnpx skills add anthropics/financial-services --skill 3-statement-modelgit clone --depth 1 https://github.com/anthropics/financial-servicesWrote 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/3-statement-model)<a href="https://agentmods.dev/skills/anthropics/financial-services/3-statement-model"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/3-statement-model.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00091 | $0.04689 |
| Opus 5 | $0.00046 | $0.02344 |
| Sonnet 5 | $0.00018 | $0.00938 |
| Haiku 4.5 | $0.00009 | $0.00469 |
Grade A, and why
3-statement-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
86% identical to 3-statement-model — 39 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 409 lines — stays where its author put it; the contents beside it link to each section on GitHub.
3-Statement Financial Model Template Completion
Complete and populate integrated financial model templates with proper linkages between Income Statement, Balance Sheet, and Cash Flow Statement.
⚠️ CRITICAL PRINCIPLES — Read Before Populating Any Template
Environment — Office JS vs Python:
- If running inside Excel (Office Add-in / Office JS): Use Office JS directly. Write formulas via
range.formulas = [["=D14*(1+Assumptions!$B$5)"]]— neverrange.valuesfor derived cells. No separate recalc; Excel computes natively. Usecontext.workbook.worksheets.getItem(...)to navigate tabs. - If generating a standalone .xlsx file: Use Python/openpyxl. Write
ws["D15"] = "=D14*(1+Assumptions!$B$5)", then runrecalc.pybefore delivery. - Office JS merged cell pitfall: Do NOT call
.merge()then set.valueson the merged range — throwsInvalidArgumentbecause the range still reports its pre-merge dimensions. Instead write value to top-left cell alone, then merge + format the full range:ws.getRange("A1").values = [["INCOME STATEMENT"]]; const h = ws.getRange("A1:G1"); h.merge(); h.format.fill.color = "#1F4E79"; - All principles below apply identically in either environment.
Formulas over hardcodes (non-negotiable):
- Every projection cell, roll-forward, linkage, and subtotal MUST be an Excel formula — never a pre-computed value
- When using Python/openpyxl: write formula strings (
ws["D15"] = "=D14*(1+Assumptions!$B$5)"), NOT computed results (ws["D15"] = 12500) - The ONLY cells that should contain hardcoded numbers are: (1) historical actuals, (2) assumption drivers in the Assumptions tab
- If you find yourself computing a value in Python and writing the result to a cell — STOP. Write the formula instead.
- Why: the model must flex when scenarios toggle or assumptions change. Hardcodes break every downstream integrity check silently.
Verify step-by-step with the user:
- After mapping the template → show the user which tabs/sections you've identified and confirm before touching any cells
- After populating historicals → show the user the historical block and confirm values/periods match source data
- After building IS projections → run the subtotal checks, show the user the projected IS, confirm before moving to BS
- After building BS → show the user the balance check (Assets = L+E) for every period, confirm before moving to CF
- After building CF → show the user the cash tie-out (CF ending cash = BS cash), confirm before finalizing
- Do NOT populate the entire model end-to-end and present it complete — break at each statement, show the work, catch errors early
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 409 lines · 91 tokens per session scan A bc2aeb423a3c
3-statement-model is a skill published in the GitHub repository anthropics/financial-services (34,627 stars, last pushed 10d ago), licensed Apache-2.0. It adds 91 tokens to every session and 4,689 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to 3-statement-model, differing in 39 lines, and is treated as a copy.
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