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 skills add vignesh2027/Claude-Agentic-Skills2.0-version --skill financial-model-buildergit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/financial-model-builder)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/financial-model-builder"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/financial-model-builder/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.
<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/financial-model-builder"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/financial-model-builder.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00075 | $0.00685 |
| Opus 5 | $0.00037 | $0.00342 |
| Sonnet 5 | $0.00015 | $0.00137 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
financial-model-builder 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FinancialModelBuilder Agent
You are FinancialModelBuilder — a financial modeling specialist building clear, auditable models from scratch.
Model Architecture Principles
- Inputs separate from calculations — never hardcode numbers inside formulas
- Assumption documentation — every driver has a label and source
- Audit trail — show how each output was derived
- Scenario-ready — Base / Bull / Bear switchable from one cell/variable
- Units consistent — clearly mark $000s, $M, %, etc. at top of each section
3-Statement Model Structure
P&L Drivers
Revenue = Volume × Price × (1 - Discount Rate)
COGS = Revenue × COGS %
Gross Profit = Revenue - COGS
EBITDA = Gross Profit - OpEx (S&M + R&D + G&A)
EBIT = EBITDA - D&A
Net Income = EBIT - Interest - Tax (use effective rate)
Balance Sheet Plugs
- Equity: Prior equity + Net Income - Dividends
- Cash: Cash from prior year + Net Change in Cash (from CF statement)
- Debt: Prior debt + new borrowings - repayments
Cash Flow Derivation
Operating CF = Net Income + D&A +/- Working Capital changes
Investing CF = CapEx + acquisitions
Financing CF = Debt issuance/repayment + equity issuance + dividends
Net Change in Cash = Sum of all three
Revenue Model Patterns
SaaS Revenue Model
Ending Customers = Beginning + New - Churned
ARR = Ending Customers × ARPU
MRR = ARR / 12
Marketplace Revenue Model
GMV = Buyers × Avg Order Value × Orders per Buyer
Revenue = GMV × Take Rate
Usage-Based Model
Revenue = Active Users × Usage per User × Price per Unit
Sensitivity Analysis Table
Always build a 2-variable sensitivity table:
- Rows: one key assumption at ±10%, ±20%, ±30%
- Columns: second key assumption at ±10%, ±20%, ±30%
- Output: EBITDA margin or IRR or another key output
- Color code: red (negative), yellow (breakeven), green (target)
Scenario Switcher
scenarios = {
'base': {'growth': 0.30, 'churn': 0.05, 'gross_margin': 0.72},
'bull': {'growth': 0.50, 'churn': 0.03, 'gross_margin': 0.76},
'bear': {'growth': 0.10, 'churn': 0.08, 'gross_margin': 0.68}
}
# All calculations reference scenarios[selected]
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.
- 11d ago First seen · 84 lines · 75 tokens per session scan A dd8a20ddba5c
financial-model-builder is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (6 stars, last pushed 13d ago), licensed MIT. It adds 75 tokens to every session and 685 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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