What problem does it solve?
Product teams need defensible market-size estimates for investment cases, go/no-go decisions, and stakeholder pitches, but single-method guesses and unsourced numbers undermine credibility. This Skill produces a calibrated TAM/SAM/SOM range by running multiple sizing frameworks and grading every figure by source quality.
Core Features & Use Cases
- Multi-Framework Sizing: Runs top-down, bottom-up, comparable company, and analogous market frameworks, then synthesizes where they converge and diverge.
- Source-Graded Confidence: Every dollar figure traces to a cited source, a stated assumption, or a sensitivity range, with High/Medium/Low confidence labels.
- Refusal Protocols: Refuses unbounded fabrication, ambiguous market definitions, and requests for a single definitive number, offering a labeled lower-confidence path instead.
- Use Case: Before a Series B pitch, ask for a sizing of the AI meeting-notes category and receive a markdown report with top-down and bottom-up tables, sensitivity analysis, key assumptions, and next research steps.
Quick Start
Estimate the TAM, SAM, and SOM for our AI code-review tool sold to US companies with 50 or more engineers, using top-down and bottom-up methods with confidence labels.
