dcf-model

dcf-model is a skill for Claude Code, Codex from leecyno1/boutique-skills. It costs 85 tokens per session (12,861 once invoked), scanned A, a copy of dcf-model, MIT.

A workflow for building discounted cash flow models in Excel to estimate a company's value from its expected future cash flows.

In plain words
What is it for?
Use it to create equity-valuation models with cash-flow projections, WACC calculations, sensitivity analysis, and an executive summary.
Why use it?
It organizes financial research, forecasts, discount-rate calculations, and sensitivity analysis so valuation assumptions can be examined systematically.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to create equity-valuation models with cash-flow projections, WACC calculations, sensitivity analysis, and an executive summary.

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Install with agentmods
npx agentmods add skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-dcf-model
Install

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.

Any agent
npx skills add leecyno1/boutique-skills --skill anthropic-fs-financial-analysis-dcf-model
Clone the repo
git clone --depth 1 https://github.com/leecyno1/boutique-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for dcf-model

README.md
[![agentmods](https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-dcf-model/github.svg)](https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-dcf-model)
Your own site
<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-dcf-model"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-dcf-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.

agentmods 80×15 button for dcf-model

Your own site · 80×15
<a href="https://agentmods.dev/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-dcf-model"><img src="https://agentmods.dev/badge/skills/leecyno1/boutique-skills/anthropic-fs-financial-analysis-dcf-model.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 85 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 12,861 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 97% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 2bb3ed672ab2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/validate_dcf.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

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.

skills/default/anthropic-fs-financial-analysis-dcf-model/SKILL.md · 1,264 lines

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. Use range.format.* for styling. The same formulas-over-hardcodes rule applies: set .formulas, never .values for derived cells.
  • If generating a standalone .xlsx file (no live Excel session): Use Python/openpyxl as described below, then run recalc.py before 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";

Read the full file on GitHub · 1,264 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 1,264 lines · 85 tokens per session scan A 2bb3ed672ab2

Subscribe to this mod's changes

dcf-model is a skill published in the GitHub repository leecyno1/boutique-skills (5 stars, last pushed today), licensed MIT. 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.