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/dcf-modelnpx skills add anthropics/financial-services --skill dcf-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/dcf-model)<a href="https://agentmods.dev/skills/anthropics/financial-services/dcf-model"><img src="https://agentmods.dev/badge/skills/anthropics/financial-services/dcf-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 5.1 | $0.00085 | $0.12861 |
| Opus 5 | $0.00043 | $0.06431 |
| Sonnet 5 | $0.00017 | $0.02572 |
| Haiku 4.5 | $0.00009 | $0.01286 |
Grade A, and why
dcf-model scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to dcf-model — 61 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 1,264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DCF Model Builder
Overview
This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet).
Tools
- Default to using all of the information provided by the user and MCP servers available for data sourcing.
Critical Constraints - Read These First
These constraints apply throughout all DCF model building. Review before starting:
Environment: Office JS vs Python/openpyxl:
- If running inside Excel (Office Add-in / Office JS environment): Use Office JS directly — do NOT use Python/openpyxl. Write formulas via
range.formulas = [["=D19*(1+$B$8)"]]. No separate recalc step needed; Excel calculates natively. Userange.format.*for styling. The same formulas-over-hardcodes rule applies: set.formulas, never.valuesfor derived cells. - If generating a standalone .xlsx file (no live Excel session): Use Python/openpyxl as described below, then run
recalc.pybefore delivery. - The rest of this skill uses openpyxl examples — translate to Office JS API calls when in that environment, but all principles (formula strings, cell comments, section checkpoints, sensitivity table loops) apply identically.
⚠️ Office JS merged cell pitfall: When building section headers with merged cells, do NOT call .merge() then set .values on the merged range — Office JS still reports the range's original dimensions and will throw InvalidArgument: The number of rows or columns in the input array doesn't match the size or dimensions of the range. Instead, write the value to the top-left cell alone, then merge and format the full range:
// WRONG — throws InvalidArgument:
const hdr = ws.getRange("A7:H7");
hdr.merge();
hdr.values = [["MARKET DATA & KEY INPUTS"]]; // 1×1 array vs 1×8 range → fails
// CORRECT — value first on single cell, then merge + format the range:
ws.getRange("A7").values = [["MARKET DATA & KEY INPUTS"]];
const hdr = ws.getRange("A7:H7");
hdr.merge();
hdr.format.fill.color = "#1F4E79";
hdr.format.font.bold = true;
hdr.format.font.color = "#FFFFFF";
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.
- 6d ago First seen · 1,264 lines · 85 tokens per session scan A 2bb3ed672ab2
dcf-model is a skill published in the GitHub repository anthropics/financial-services (34,689 stars, last pushed 10d ago), licensed Apache-2.0. It adds 85 tokens to every session and 12,861 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to dcf-model, differing in 61 lines, and is treated as a copy.
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