financial-modeling
Builds and stress-tests financial models for forecasting, scenario planning, and decision support — revenue build, cost structure, driver logic, and the sensitivities that show where a plan breaks. Use this to model a decision's financial consequence, build a forecast or long-range plan, evaluate an investment or hire, or pressure-test someone else's model before relying on it.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/financial-modeling && cp -r /tmp/financial-modeling/plugins/finance/skills/financial-modeling ~/.claude/skills/financial-modelingSKILL.md
# Financial modeling A model is an argument about how the business works, expressed in arithmetic. Its value is the argument, not the output precision. ## Structure Three separated layers, always: 1. **Inputs** — every assumption, in one place, each with a source and a date. An assumption buried inside a formula is invisible and therefore never challenged. 2. **Calculations** — no hard-coded numbers. Ever. A constant inside a formula is an untraceable assumption. 3. **Outputs** — the statements and the summary a decision-maker actually reads. One row, one calculation, carried consistently across periods. Models become unauditable through inconsistent rows more than through complexity. ## Build revenue from drivers Never grow a top-line by a percentage. Build it: volume × price, or accounts × retention × expansion. Driver-based models can be argued with, and being argued with is the point — a growth rate cannot be wrong, only optimistic. Cost structure separated into fixed, variable, and step-fixed. The step-fixed items are where plans break, because they move in jumps nobody modeled. ## Sensitivities are the deliverable A single-scenario model tells you nothing about risk. For every model, produce: - **Which two or three assumptions actually move the answer.** Usually far fewer than expected. - **Breakeven on each** — how wrong can this be before the decision reverses? - **Downside case** — not a haircut on the base case, but a coherent story where things go badly. If a plan only works in the base case, that is the finding. ## Reviewing someone else's model The description of a model is not evidence about the model. Check these, in this order, because each one invalidates everything after it. - **Trace one number end to end.** Pick an output that matters and follow it back to inputs. If you cannot, nobody else has either, and the model has never actually been reviewed. - **Find the hard-coded constants.** Search the calculation area for typed numbers. Each one is an assumption that escaped the input sheet, and they are where overrides hide. - **Check the row consistency.** A formula that differs partway across a row is either a deliberate change nobody documented or an error, and the two look identical. - **Test the extremes.** Set a key driver to zero and to double. Models frequently break, go negative in impossible ways, or fail to respond at all — which tells you the driver is decorative. - **Check that the statements tie.** Cash flow reconciles to the balance sheet movement; the balance sheet balances in every period, not just the first. - **Ask what is missing.** Working capital, hiring lag, churn, price changes, tax, and the step costs that come with growth are the omissions that flatter a plan most. **Then find the assumption doing the work.** Most models rest on one or two numbers, and those are usually the least evidenced. Ask where each came from and what it is based on — the answer is frequently that it was chosen to make the case work, which is a fine thing to know before relying on it. ## Presenting Lead with the answer, then the two assumptions it rests on most heavily, then what would change it. Never present a model without stating what it is most sensitive to — the recipient will assume robustness you did not claim. ## Never - Report a number to more precision than the assumptions support. Five significant figures from a guessed growth rate is false confidence. - Build a model whose logic you cannot explain in three sentences. - Change an assumption to reach a desired output without labeling it as a target case.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.