Build Financial Models

Build financial models — skill template

Turn this template into a working skill by resolving every TODO. The seed prompt below is the published Anthropic example this template was derived from; your skill should generalize it for repeated, hands-off use.

Outcome

Create investment analyses with complete financial models, scenario planning, and risk evaluation.

TODO: Restate the outcome for YOUR context in one sentence: who runs this, on what input, producing what artifact, how often.

Seed prompt (verbatim from source, © Anthropic PBC)

I'm evaluating MediTech Solutions (healthcare SaaS) and need a complete investment analysis.

Deal structure: $75M growth equity stake at 3.6x ARR entry, exit at 7.0x in year 5. Current metrics are $50M ARR growing 35% with 18% EBITDA margin.

Get the company financials from Daloopa - search for MediTech Solutions and pull their historical revenue, EBITDA margins, customer metrics, and growth rates.

Pull healthcare SaaS comparables from S&P Global - find public companies in the sector and get their current trading multiples, growth rates, and margin profiles. I need this to validate our 7.0x exit assumption.

Search the web for healthcare SaaS customer concentration benchmarks - the company mentioned their top 3 customers represent about 40% of revenue and I need to know if that's typical or concerning for this sector. Also look up recent healthcare SaaS growth trends to stress-test the 35% growth assumption.

Retrieve our IC template from Box - search the "IC Templates" folder and use the private equity model format as the structure.

Key questions to address: How do returns look if growth slows to 25% or 20%? What does the customer concentration risk mean for our downside scenario? How does our 7.0x exit assumption compare to where public healthcare SaaS companies are trading today?

Create an Excel model with scenarios (base, upside, downside), sensitivity analysis on growth and exit multiple, risk assessment focusing on customer concentration, and a comps table showing where public companies trade. Use sophisticated private equity formatting with premium visual quality, an intentional color scheme, working formulas, frozen panes, and conditional formatting.

Required context and inputs

• Connectors: Daloopa, S&P Global, Box
• Features: Extended Thinking, Web Search
• Excel for downloading and editing models
• Optional: Claude for Excel (beta)
• TODO: exact file paths / folders / message formats this skill should expect
• TODO: domain context the model cannot infer (naming conventions, thresholds, house style)

Tools, connectors, APIs, and authentication

• Box
• Claude for Excel add-in
• Daloopa
• S&P Global
• TODO: confirm which connectors are enabled in the runtime that will execute this skill
• Authentication: connectors authenticate via their own OAuth flows — this skill must

NEVER ask for, store, or echo credentials, tokens, or API keys. If auth is missing, stop and tell the user which connector to enable.

Permissions and sensitive actions

• Reads: TODO (folders, channels, records this skill may read)
• Writes: TODO (what it may create/modify, and where)
• Held back for the primary agent / human: sending external communications, financial

transactions, deleting or overwriting originals, submitting web forms

Workflow

1. Describe the task: specify investment opportunity details and partner requirements
2. Give Claude context: connect data platforms (Daloopa, S&P Global, Box); enable Extended Thinking and Web Search
3. What Claude creates: multi-sheet model with Executive Summary, Financial Model, Scenario Analysis, Risk Assessment, and Comps & Valuation
4. Follow up prompts: create IC memo, validate growth assumptions, research exit multiples
5. Tricks, tips, and troubleshooting: keep comps fresh, work in Excel with Claude, unlock further capabilities

TODO: adapt the steps above (from the source page) into imperative instructions for the executing agent, including what to do between steps.

Decision points

• TODO: list each point where the skill must choose between paths, with the rule to apply
• Default rule: prefer the reversible option; when two readings of the input are

plausible, surface both rather than picking silently.

Validation criteria

• Output matches the outcome statement above (spot-check against the seed prompt's asks)
• Every factual claim is traceable to a provided input, connector record, or cited source
• All figures reconcile to source statements/workbooks; totals recomputed programmatically, not by eye
• Flag (never silently correct) discrepancies between model and source data
• TODO: add one domain-specific check a reviewer in your org would apply

Failure modes and fallbacks

• Connector unavailable or unauthenticated → pause, tell the user exactly which connector to enable and why; offer a degraded run from uploaded files
• Search returns thin/conflicting results → present both readings with sources instead of picking one silently
• Ambiguous or missing input → ask one targeted question; if unattended, state the assumption inline and proceed
• Any step would take a sensitive/irreversible action → stop and hand back to the user (see Delegation)

Delegation

Apply the repo's delegation policy (docs/delegation-policy.md — bundle or restate it if you install this skill outside the repo). Defaults for this template:

• Run single-agent unless a step fans out over independent items (files, records,

vendors, channels). Only independent work parallelizes.

• Each delegated task must ship with: the minimal context slice it needs, an explicit

output contract, a validation check the primary agent runs on the result, and a fallback if it returns empty or fails.

• Final review, synthesis, and every sensitive action listed above stay with the

primary agent.

• TODO: name the concrete subtasks (if any) that qualify for delegation here.

Attribution

Derived from Build financial models (retrieved 2026-07-26). Seed prompt and workflow content © Anthropic PBC. Scaffold structure original to this repository.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: iankiku
• Source: iankiku/agent-skill-factory
• License: MIT

Install and usage instructions live in the source repository linked above.