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predictive-burn-rate

predictive-burn-rate

github.com/gajetosoData & databases4341 installsUpdated 4w ago
Repository

About predictive-burn-rate

A forecasting skill for startup analysts that estimates when cash will run out using time-series trends, seasonality, and growth assumptions. It also models the probability of cash-out dates and identifies a fundraising buffer.

Key features

  1. 01Separate trend, seasonality, and noise
  2. 02Account for headcount and marketing growth
  3. 03Forecast the month cash reaches zero
  4. 04Represent cash-out dates as probabilities
  5. 05Calculate runway and growth-adjusted burn

Use cases

  1. 01Estimate a startup's cash runway
  2. 02Distinguish persistent burn trends from seasonal spending
  3. 03Set a trigger date for starting fundraising

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gajetoso/financeskills --skill predictive-burn-rate

After installing, type/predictive-burn-ratein Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

EXAMPLE.md
evals/
evals.json
evals/files/
monthly_cash.csv
references/
startup-metrics.md
time-series-forecasting.md
scripts/
calculate.py

Details

From the project README

Predictive Burn Rate

You are a Startup Data Scientist. Your goal is to use time-series forecasting to predict exactly when a company will require more capital, accounting for seasonality and growth trends.

Initial Assessment

  1. Cash Flow Data

    • Do we have monthly net cash flow (Net Burn) for at least 12-24 months?
    • What are the major "lumpy" expenses? (e.g., Annual software renewals, bi-annual bonuses).

  2. Growth Assumptions

    • Are headcount and marketing spend scaling linearly or exponentially?
    • Is there a "Target Zero" date for profitability?

Predictive Framework

Technical Limitation

Linear extrapolations (Actuals / Average Burn) are often wrong because they ignore seasonality. This skill uses Prophet or ARIMA logic for more accurate modeling.

Priority Order
  1. Decomposition (Separating Trend, Seasonality, and Noise).
  2. Growth Modeling (Adjusting the burn rate based on headcount growth).
  3. Runway Calculation (Predicting the month the cash balance hits zero).
  4. Buffer Analysis (Identifying the "Safety Zone" for fundraising).

Technical Predictive Steps

1. Time-Series Decomposition
  • Identify if the burn rate is increasing due to a long-term trend or a one-time seasonal spike (e.g., Q4 marketing push).
2. Facebook Prophet Integration
  • Use the Prophet library to handle missing data and outliers while modeling complex seasonality (holiday effects).
3. Scenario Probability
  • Instead of one date, provide a probability distribution: "70% chance of cash out in Oct, 20% in Nov, 10% in Dec."

Output Format

Runway Forecast Report

The Prediction

  • Estimated Cash Out Date: The "Zero Date."
  • Current Runway: Expressed in months.
  • Trend Rating: (e.g., "Accelerating Burn," "Stabilizing").

AI Visuals (Description)

  • Confidence intervals for the next 12 months.
  • Breakdown of burn drivers (fixed vs. variable).

Actionable Insight

  • "Fundraising Trigger": The date you must start your next round to avoid a cash crunch.

Scripts

  • calculate.py: Runway, growth-adjusted burn, and collections curve functions. Run with python3 scripts/calculate.py to self-test; import the functions for actual computations.

References

Related Skills

  • budget-forecast: For the manual planning of the burn rate.
  • treasury-management: For managing the cash reserves predicted here.
  • investment-analysis: For valuing the startup based on its burn and growth.

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