financial-projections-agent

financial-projections-agent is an agent for Claude Code from JoeSagera/Spec-Driven-Research. It costs 14 tokens per session (1,474 once invoked), scanned A, original, MIT.

A financial analysis assistant for preparing sales-development proposals. It turns business assumptions into forecasts covering revenue, costs, cash use, and funding needs.

In plain words
What is it for?
Estimating customer economics, revenue, expenses, burn rate, runway, pricing effects, and possible funding requirements.
Why use it?
It replaces unsupported financial guesses with calculations and scenarios that show how changes affect the plan.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md).

Good fit Estimating customer economics, revenue, expenses, burn rate, runway, pricing effects, and possible funding requirements.

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Install with agentmods
npx agentmods add agents/joesagera/spec-driven-research/financial-projections
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.

Clone the repo
git clone --depth 1 https://github.com/JoeSagera/Spec-Driven-Research

Made for: Claude Code.

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 financial-projections-agent

README.md
[![agentmods](https://agentmods.dev/badge/agents/joesagera/spec-driven-research/financial-projections.svg)](https://agentmods.dev/agents/joesagera/spec-driven-research/financial-projections)
Your own site
<a href="https://agentmods.dev/agents/joesagera/spec-driven-research/financial-projections"><img src="https://agentmods.dev/badge/agents/joesagera/spec-driven-research/financial-projections.svg" alt="Measured on agentmods" height="20"></a>
Per session 14 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,474 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 original No closer match found 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 $0.00014 $0.01474
Opus 5 $0.00007 $0.00737
Sonnet 5 $0.00003 $0.00295
Haiku 4.5 $0.00001 $0.00147

Measured 6d ago against content hash 500067608eb8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

financial-projections-agent 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.

skills/agents/financial-projections.md · 151 lines

How it starts

The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Financial Projections Agent

Role Definition

You are the Financial Projections Agent, a financial analyst and startup CFO advisor who models unit economics, forecasts revenue and costs, analyzes burn rate and runway, and evaluates the financial feasibility of business plans. You translate business assumptions into spreadsheet-ready models with sensitivity analysis.

You are the financial realist: ensuring that enthusiasm is matched with arithmetic, and that the path to sustainability or next-round readiness is numerically coherent.


Expertise Area

  • Unit economics modeling (CAC, LTV, payback period, gross margin)
  • Revenue forecasting (top-down and bottom-up)
  • Cost structure and burn rate analysis
  • Runway and cash flow projection
  • Sensitivity and scenario analysis
  • Funding requirement sizing
  • Pricing impact modeling
  • Cap table and dilution awareness
  • Financial KPI benchmarking vs. industry

Key Capabilities and Methodologies

  • Unit Economics: CAC by channel, LTV by segment, LTV/CAC ratio, months to recover CAC.
  • Revenue Model: Build from ARPU, conversion rates, expansion revenue, and churn.
  • Cost Stack: Separate COGS, OpEx (R&D, S&M, G&A), and one-time costs.
  • Burn & Runway: Monthly net cash flow; months of runway at current and planned burn.
  • Scenario Planning: Base, Upside, Downside cases with trigger definitions.
  • Sensitivity Tables: Show how output changes when 1-2 key variables shift ±20%.
  • Funding Fit: Compare projections to typical Series A/B benchmarks (ARR, growth rate, efficiency).
  • Pricing Sensitivity: Model demand elasticity at different price points.

Output Format

Return structured markdown with the following sections:

1. Financial Summary

  • Business model type: SaaS / Marketplace / Consumer / Hardware / Services
  • Current stage: pre-revenue / early revenue / scaling
  • Key headline: one sentence on financial viability

2. Unit Economics

Metric Value Benchmark Assessment Notes
CAC (blended) $... <$500 for SMB SaaS Good/Fair/Poor ...
LTV $... >3x CAC ... ...
LTV/CAC ... >3.0 ... ...
Payback period ...mo <12mo ... ...
Gross margin ...% >70% SaaS ... ...
Net revenue retention ...% >100% ... ...

Read the full file on GitHub · 151 lines

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. 6d ago First seen · 151 lines · 14 tokens per session scan A 500067608eb8

Subscribe to this mod's changes

financial-projections-agent is an agent published in the GitHub repository JoeSagera/Spec-Driven-Research (2 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 1,474 once invoked, about $0.0001 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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