Agent skill

Financial Modeling

by seb1n in seb1n/awesome-ai-agent-skills

Build financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data.

MITAuto-check passedBusiness, Finance & HR

Install Financial Modeling

skills CLI
$ npx skills add seb1n/awesome-ai-agent-skills --skill financial-modeling -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install seb1n/awesome-ai-agent-skills financial-modeling --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/finance-and-accounting/financial-modeling .claude/skills/financial-modeling && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
financial-modeling
GitHub stars
206
Token cost
~2k tokens
SKILL.md length
987 words
Files
1
Skills in repo
101
Repo updated
First seen
Licence
MIT

At a glance

Build financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data.

  • Works in 6 steps: Define Core Assumptions → Build the Revenue Model → Project Operating Expenses → …
  • The user requests financial modeling
  • SKILL.md covers Workflow, Usage, Examples and Best Practices, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Financial Modeling is an agent skill from seb1n/awesome-ai-agent-skills. Build financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data. Use when the user requests financial modeling or provides relevant inputs for this workflow.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Financial modeling. The repository describes itself as: 103 ready-to-use AI agent skills for Claude Code, OpenAI Codex, Gemini CLI, Cursor, GitHub Copilot, Windsurf, and other Agent Skills-compatible tools. Complete SKILL.md… The licence is MIT.

When your agent uses it

  • The user requests financial modeling
  • Provides relevant inputs for this workflow

Example prompts

  • “/financial-modeling”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Define Core Assumptions
  2. Build the Revenue Model
  3. Project Operating Expenses
  4. Calculate Free Cash Flows
  5. Compute Valuation Metrics
  6. Stress Test and Summarize

What it can do on your machine

Read from SKILL.md and the folder at commit 75865a5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Financial Modeling loads about 2k tokens when it runs. Until then it costs about 57 tokens; SKILL.md has 987 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~57
When it runs · the whole SKILL.md, loaded when a task matches
~2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from seb1n/awesome-ai-agent-skills at commit 75865a5, republished under its MIT licence (© seb1n). 987 words, ~1,978 tokens.

Download SKILL.mdSave it as .claude/skills/financial-modeling/SKILL.md (or your agent's skills folder).
name
financial-modeling
description
Build financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data. Use when the user requests financial modeling or provides relevant inputs for this workflow.
license
MIT
metadata.author
community
metadata.version
1.0

Financial Modeling

Build structured financial projections including income statements, discounted cash flow (DCF) models, and valuation analyses. This skill takes a set of business assumptions and transforms them into multi-period financial forecasts with key metrics like NPV, IRR, EBITDA margins, and revenue growth rates. Suitable for startup fundraising, acquisition analysis, budgeting, and strategic planning.

Workflow

  1. Define Core Assumptions Gather all foundational inputs: revenue growth rates, pricing tiers, customer acquisition rates, churn, cost structures, tax rates, discount rates, and terminal growth rates. Validate that assumptions are internally consistent — for example, headcount growth should align with projected revenue capacity. Document each assumption with its source or rationale.

  2. Build the Revenue Model Construct a bottoms-up or top-down revenue forecast depending on available data. For subscription businesses, model MRR by cohort with expansion and churn. For transactional businesses, model volume × average transaction value. Break revenue into segments if the business has multiple product lines or geographies.

  3. Project Operating Expenses Forecast COGS, gross margin, and operating expenses by category: personnel, marketing, R&D, G&A, and infrastructure. Use a mix of fixed and variable cost assumptions. Tie headcount plans to compensation benchmarks. Model economies of scale where applicable — hosting costs per user should decline as volume grows.

  4. Calculate Free Cash Flows Derive EBITDA from the projected P&L, then adjust for capital expenditures, changes in working capital, and taxes to arrive at unlevered free cash flow (UFCF) for each period. Clearly separate operating cash flow from investing and financing activities.

  5. Compute Valuation Metrics Discount projected cash flows using WACC to compute enterprise value via DCF. Calculate terminal value using either a perpetuity growth model or an exit multiple approach. Derive NPV, IRR, and payback period. Run sensitivity tables across discount rate and growth rate ranges.

  6. Stress Test and Summarize Run bear/base/bull scenarios by varying 2-3 key assumptions. Present results in a summary table showing the range of outcomes. Highlight which assumptions have the most impact on valuation.

Usage

Provide your business assumptions, historical financials (if available), and the type of model you need. Specify the projection period and any specific metrics you want calculated.

Example prompt:

Build a 3-year monthly P&L projection for a B2B SaaS startup. Starting MRR is $45K, growing 8% month-over-month for year 1, decelerating to 5% in year 2 and 3% in year 3. Gross margin is 78%. Opex starts at $60K/month and grows 4% monthly. Show EBITDA and cash position.

Examples

Example 1: SaaS Startup 3-Year P&L Projection

Input: B2B SaaS, $45K starting MRR, 8%/5%/3% MoM growth by year, 78% gross margin, $60K starting opex growing 4% monthly.

Output (Year-End Summary):

MetricYear 1Year 2Year 3
Annual Revenue$853,971$1,753,589$2,754,401
COGS (22%)$187,874$385,790$605,968
Gross Profit$666,097$1,367,799$2,148,433
Operating Expenses$901,548$1,443,408$2,310,943
EBITDA($235,451)($75,609)($162,510)
EBITDA Margin-27.6%-4.3%-5.9%
Cumulative EBITDA Deficit($235,451)($311,060)($473,570)

This convention treats month 1 as the stated $45K MRR, applies 8% growth to months 2–12, 5% to months 13–24, and 3% to months 25–36. Monthly EBITDA remains negative because gross-profit dollars never overtake opex; the cumulative EBITDA deficit reaches about $474K. Actual cash position cannot be calculated without starting cash, working-capital, capex, tax, and financing assumptions. The plan therefore needs an explicit cash buffer and slower opex growth, and it does not support a conclusion about financing sufficiency on its own.

Show full SKILL.md (429 more words)Show less
Example 2: DCF Model for Small Business Acquisition

Input: Target business generates $500K annual UFCF, growing 6% per year for 5 years. WACC is 12%. Terminal growth rate 2.5%. Acquisition price $2.1M.

Output:

YearUFCFDiscount FactorPV of UFCF
1$530,0000.893$473,214
2$561,8000.797$447,864
3$595,5080.712$423,871
4$631,2380.636$401,163
5$669,1130.567$379,673
  • PV of Forecast Period: $2,125,785
  • Terminal Value: $669,113 × 1.025 / (0.12 − 0.025) = $7,219,377
  • PV of Terminal Value: $7,219,377 / 1.12⁵ = $4,096,468
  • Enterprise Value: $6,222,253
  • Enterprise value less stated purchase price: $4,122,253 before debt, cash, transaction costs, taxes, and diligence adjustments

The modeled enterprise value exceeds the stated purchase price under these assumptions, but that is not enough to call the transaction accretive or a clear buy. Calculate equity value, financing cash flows, transaction costs, taxes, and an explicit exit or holding-period cash-flow schedule before reporting NPV or IRR. Stress-test WACC, terminal growth, operating performance, and downside cases.

Best Practices

  • Always separate assumptions from calculations so stakeholders can adjust inputs without modifying formulas.
  • Use monthly granularity for the first 1-2 years and quarterly or annual thereafter to balance detail with readability.
  • Anchor assumptions in comparable company data or historical performance wherever possible.
  • Include a sensitivity analysis on at least two key variables (e.g., growth rate and discount rate).
  • Label all units clearly — distinguish between monthly and annual figures, and between thousands and actuals.
  • Cross-check the model: net income plus D&A should reconcile to operating cash flow before working capital changes.

Safety Boundaries

  • Treat the output as analytical support, not individualized financial, tax, investment, or accounting advice.
  • Preserve source data and expose assumptions, formulas, units, and reconciliation checks so a reviewer can reproduce the result.
  • Do not initiate payments, transactions, journal entries, filings, or account changes without explicit user authorization.
  • Require a qualified professional to review material decisions, regulated filings, or conclusions based on incomplete data.

Edge Cases

  • Pre-revenue startups: Use a bottoms-up TAM/SAM/SOM approach with conversion funnels rather than historical growth rates. Flag that projections are highly speculative.
  • Negative free cash flow in all periods: DCF still works but terminal value dominates. Note the sensitivity and consider using a revenue multiple as a sanity check.
  • Hyper-growth distortion: When MoM growth exceeds 15%, compounding over 36 months produces unrealistic figures. Cap growth or switch to an S-curve model with a saturation point.
  • Currency mismatch: If revenues and costs are in different currencies, model exchange rate assumptions explicitly and show impact of ±10% FX moves.
  • Seasonal businesses: Monthly models must account for seasonality. Use historical monthly revenue distribution percentages rather than flat growth assumptions.

© seb1n, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in finance-and-accounting/financial-modeling of seb1n/awesome-ai-agent-skills.

Open the folder on GitHubat commit 75865a5

Compare with similar skills

Financial Modeling next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Financial Modeling compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Financial Modeling this skillseb1n/awesome-ai-agent-skills206—~2kAutomated safety check: PassMIT
Creating Financial ModelsChen-zexi/open-ptc-agent7293 repos~1.3kAutomated safety check: PassMIT
Equity ResearchrollingSirius/equity-research-skill453—~1.5kAutomated safety check: PassMIT
SaaS Metrics Coachrongxinzy/RongxinAI1542 repos~1.3kAutomated safety check: PassMIT
Startup Financial Modelingnicepkg/auto-company19411 repos~2.8kAutomated safety check: PassNone
Stock Value AnalyzerFunnyKun/stock-value-analyzer140—~3.3kAutomated safety check: PassNone

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Questions about Financial Modeling

What does Financial Modeling do?

Build financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data. Financial Modeling is an agent skill from seb1n/awesome-ai-agent-skills. Build financial projections, P&L statements, DCF models, and valuation analyses from assumptions and historical data.

When should I use Financial Modeling?

Financial Modeling fits situations like: the user requests financial modeling; provides relevant inputs for this workflow.

How do I install Financial Modeling in Claude Code?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill financial-modeling -a claude-code`. Or copy the skill folder (finance-and-accounting/financial-modeling in seb1n/awesome-ai-agent-skills) into .claude/skills/financial-modeling in your project. Claude Code loads it when a task matches its description.

How do I install Financial Modeling in Codex?

Run `npx skills add seb1n/awesome-ai-agent-skills --skill financial-modeling -a codex`. Or copy the skill folder (finance-and-accounting/financial-modeling in seb1n/awesome-ai-agent-skills) into .agents/skills/financial-modeling in your project. Codex loads it when a task matches its description.

Can I use Financial Modeling in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add seb1n/awesome-ai-agent-skills --skill financial-modeling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/financial-modeling, .gemini/skills/financial-modeling, .github/skills/financial-modeling and .opencode/skills/financial-modeling in your project.

What does Financial Modeling need to run?

SKILL.md names no scripts, command-line tools or credentials: Financial Modeling is instructions for the agent only.

Does Financial Modeling access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Financial Modeling safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Financial Modeling use?

Financial Modeling is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Financial Modeling use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Financial Modeling?

Skills that share tags, products or a category with Financial Modeling: Creating Financial Models (Chen-zexi/open-ptc-agent, 729 stars), Equity Research (rollingSirius/equity-research-skill, 453 stars), SaaS Metrics Coach (rongxinzy/RongxinAI, 154 stars) and Startup Financial Modeling (nicepkg/auto-company, 194 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Financial Modeling?

seb1n (a GitHub user) maintains it in seb1n/awesome-ai-agent-skills, which has 206 GitHub stars. The repository holds 101 skills in this directory. The repository was last updated on August 9, 2026.

Source: seb1n/awesome-ai-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.