agentsclimarketplace

Forecasting

Skill m-binimran/finance-pack/skills/forecasting

Claude Code pack for financial analysts: data-integrity (citation), SEC/FINRA disclaimer, projection-labeling & MNPI/PII guardrails + skills for statement analysis, ratios, DCF/comps valuation, modeling, earnings, forecasting & FP&A, loops & review agents. Not investment advice.

Install
npx -y skills add m-binimran/finance-pack --skill forecasting

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Build a financial forecast (revenue, P&L, cash) from drivers and assumptions, with a clear method and ranges. Use when projecting performance for planning, valuation, or fundraising.

SKILL.md

1.4 KB, as published. Nobody here has run it

forecasting

A forecast is a driver tree plus assumptions. Make the drivers and the method explicit.

Process

  1. Pick the method: driver-based (units x price, funnel, cohorts) > naive growth %. Bottom-up where possible.
  2. Anchor to sourced historicals and a base rate; don't extrapolate a recent spike blindly.
  3. Build the drivers: revenue drivers, margin assumptions, cost structure (fixed vs. variable), working capital, capex. State each assumption and where it came from.
  4. Project the P&L (and cash if needed) over the horizon; keep it consistent with the drivers.
  5. Ranges + scenarios: base/bull/bear, and the 2-3 assumptions that matter most (scenario-analysis).
  6. Reality-check against capacity, market size, and history - flag anything that implies the impossible.

Output

  • The forecast (driver assumptions + projected P&L), a base/bull/bear range, and the key swing assumptions - all labeled as estimates.

Guardrails

  • Projections labeled with assumptions (projection-guard); historicals sourced (data-integrity).
  • Give ranges, not false precision; sanity-check against base rates (methodology).

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.