Agent Skillscbrock84/headcount › financial-modeling

financial-modeling

GitHub

构建与压力测试财务模型,支持预测、情景规划及决策评估。涵盖收入驱动逻辑、成本结构拆分、敏感性分析及模型审查,确保假设透明与计算可追溯。

plugins/finance/skills/financial-modeling/SKILL.md cbrock84/headcount

Trigger Scenarios

需要构建财务预测或长期计划 对现有财务模型进行审查或压力测试 评估投资、招聘等决策的财务后果

Install

npx skills add cbrock84/headcount --skill financial-modeling -g -y
More Options

Non-standard path

npx skills add https://github.com/cbrock84/headcount/tree/main/plugins/finance/skills/financial-modeling -g -y

Use without installing

npx skills use cbrock84/headcount@financial-modeling

指定 Agent (Claude Code)

npx skills add cbrock84/headcount --skill financial-modeling -a claude-code -g -y

安装 repo 全部 skill

npx skills add cbrock84/headcount --all -g -y

预览 repo 内 skill

npx skills add cbrock84/headcount --list

SKILL.md

Frontmatter
{
    "name": "financial-modeling",
    "description": "Builds and stress-tests financial models for forecasting, scenario planning, and decision support — revenue build, cost structure, driver logic, and the sensitivities that show where a plan breaks. Use this to model a decision's financial consequence, build a forecast or long-range plan, evaluate an investment or hire, or pressure-test someone else's model before relying on it."
}

Financial modeling

A model is an argument about how the business works, expressed in arithmetic. Its value is the argument, not the output precision.

Structure

Three separated layers, always:

  1. Inputs — every assumption, in one place, each with a source and a date. An assumption buried inside a formula is invisible and therefore never challenged.
  2. Calculations — no hard-coded numbers. Ever. A constant inside a formula is an untraceable assumption.
  3. Outputs — the statements and the summary a decision-maker actually reads.

One row, one calculation, carried consistently across periods. Models become unauditable through inconsistent rows more than through complexity.

Build revenue from drivers

Never grow a top-line by a percentage. Build it: volume × price, or accounts × retention × expansion. Driver-based models can be argued with, and being argued with is the point — a growth rate cannot be wrong, only optimistic.

Cost structure separated into fixed, variable, and step-fixed. The step-fixed items are where plans break, because they move in jumps nobody modeled.

Sensitivities are the deliverable

A single-scenario model tells you nothing about risk. For every model, produce:

  • Which two or three assumptions actually move the answer. Usually far fewer than expected.
  • Breakeven on each — how wrong can this be before the decision reverses?
  • Downside case — not a haircut on the base case, but a coherent story where things go badly.

If a plan only works in the base case, that is the finding.

Reviewing someone else's model

The description of a model is not evidence about the model. Check these, in this order, because each one invalidates everything after it.

  • Trace one number end to end. Pick an output that matters and follow it back to inputs. If you cannot, nobody else has either, and the model has never actually been reviewed.
  • Find the hard-coded constants. Search the calculation area for typed numbers. Each one is an assumption that escaped the input sheet, and they are where overrides hide.
  • Check the row consistency. A formula that differs partway across a row is either a deliberate change nobody documented or an error, and the two look identical.
  • Test the extremes. Set a key driver to zero and to double. Models frequently break, go negative in impossible ways, or fail to respond at all — which tells you the driver is decorative.
  • Check that the statements tie. Cash flow reconciles to the balance sheet movement; the balance sheet balances in every period, not just the first.
  • Ask what is missing. Working capital, hiring lag, churn, price changes, tax, and the step costs that come with growth are the omissions that flatter a plan most.

Then find the assumption doing the work. Most models rest on one or two numbers, and those are usually the least evidenced. Ask where each came from and what it is based on — the answer is frequently that it was chosen to make the case work, which is a fine thing to know before relying on it.

Presenting

Lead with the answer, then the two assumptions it rests on most heavily, then what would change it. Never present a model without stating what it is most sensitive to — the recipient will assume robustness you did not claim.

Never

  • Report a number to more precision than the assumptions support. Five significant figures from a guessed growth rate is false confidence.
  • Build a model whose logic you cannot explain in three sentences.
  • Change an assumption to reach a desired output without labeling it as a target case.

Version History

  • d58a7ee Current 2026-09-02 21:06

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Indexed
2026-09-02 21:06

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