market-sizing
by phurynmarket-sizing helps estimate TAM, SAM, and SOM with top-down and bottom-up methods. Use it for Market Research workflows, market entry decisions, investor decks, and launch planning when you need a defensible logic trail, assumptions to validate, and a practical first-pass market estimate.
This skill scores 78/100, which means it is a solid listing candidate for Agent Skills Finder. Directory users can reasonably install it if they want a market-sizing workflow that is more structured than a generic prompt, though they should still expect some judgment calls because the repository has no companion scripts, references, or support files.
- Clear trigger and use cases: the frontmatter explicitly says to use it for TAM/SAM/SOM sizing, investor pitches, and market entry decisions.
- Substantive workflow guidance: the body includes a defined analysis sequence covering market definition, top-down estimation, bottom-up estimation, SAM scoping, and SOM estimation.
- Good operational framing: it tells the agent to read user-provided research directly and to use web search for current market data and growth projections.
- No supporting artifacts: there are no scripts, references, resources, or example files to reduce ambiguity or validate the approach.
- Some execution detail is still implicit: despite a long body, the preview does not show a concrete output template or step-by-step decision rules for resolving conflicting data.
Overview of market-sizing skill
What market-sizing does
The market-sizing skill helps you estimate TAM, SAM, and SOM for a product, company, or category using both top-down and bottom-up methods. It is useful when you need a defensible answer for a market opportunity, investor deck, launch plan, or market entry decision. This market-sizing skill is designed for Market Research workflows where the goal is not just a number, but a clear logic trail behind it.
Who should use it
Use market-sizing if you need a fast but structured first-pass market estimate and want the result to be easier to defend than a generic prompt. It fits founders, analysts, product teams, and researchers who already have a market definition or can provide one. It is less useful if you want a pure citation engine or a fully sourced consultant-style report without supplying constraints.
What makes it different
The market-sizing skill pushes a two-check approach: estimate from the top of the market and then verify from the bottom using customers, price, and usage assumptions. That makes it better for spotting inflated assumptions, unclear segment boundaries, and mismatched geography or channel scope. The main value is decision quality: it helps you separate addressable demand from total industry buzz.
How to Use market-sizing skill
Install and trigger the skill
Use the market-sizing install flow in your skills environment, then invoke it with a specific sizing request rather than a vague topic. A strong request names the market, geography, customer type, and time horizon. Example: Estimate TAM/SAM/SOM for AI note-taking software sold to US SMB healthcare clinics in 2025.
Give the skill the right inputs
The skill works best when you provide any of the following: existing research, competitor pricing, customer counts, adoption assumptions, segment definitions, or constraints like region and industry. If you have raw notes, ask the skill to first normalize the market definition before sizing it. Better input looks like: Use this list of competitors and clinic counts to size the US outpatient scheduling software market for 2026.
Read the repo in the right order
Start with SKILL.md because it contains the sizing workflow and assumptions to validate. Then inspect any supporting files in pm-market-research/skills/market-sizing/ if they are added later, especially anything that defines constraints, templates, or calculation logic. If the repo is still minimal, treat SKILL.md as the primary operating guide and supply your own external data.
Work the estimate in layers
Ask for a sequence: define market boundaries, run a top-down estimate, build a bottom-up cross-check, then reconcile differences into TAM, SAM, and SOM. This matters because many bad market-sizing outputs fail by jumping straight to a headline number. For best results, explicitly request assumptions, formulas, and sensitivity ranges so the answer is auditable.
market-sizing skill FAQ
Is market-sizing good for Market Research?
Yes. It is a good fit for Market Research when you need a structured sizing model and a clear assumption chain. It is not a replacement for primary research, but it can organize secondary research into a usable estimate quickly.
How is this different from a normal prompt?
A normal prompt may produce a number, but the market-sizing skill gives you a repeatable sizing workflow: define scope, estimate from two directions, then validate the serviceable slice. That reduces the chance of mixing up TAM with SAM or overstating SOM. It also makes the output easier to review and update.
Do I need to be an expert to use it?
No, but you do need a clear market question. Beginners can use the skill if they can state the product, customer, and geography in one sentence. If those inputs are fuzzy, the output will be fuzzy too.
When should I not use it?
Do not use market-sizing when you only need a quick opinion, a branding narrative, or a general industry overview. It is also a poor fit if you need highly sourced, jurisdiction-specific financial analysis and do not plan to provide data or accept assumption-based estimates.
How to Improve market-sizing skill
Tighten the market definition
The biggest quality gain comes from narrowing scope before calculating anything. Specify who buys, what is being sold, where it is sold, and what is excluded. For example, B2B payroll software for Canadian companies with 20-200 employees is much better than payroll market.
Provide numbers the model can test
Give customer counts, price bands, conversion assumptions, or comparable revenue figures when you have them. Those inputs let the market-sizing skill compare top-down and bottom-up paths instead of inventing unsupported assumptions. If you do not know a number, ask for a range and the rationale.
Ask for sensitivity, not just a point estimate
The most useful output usually includes a base case plus high and low cases. That helps you see which assumption drives the result and whether the opportunity is still attractive under conservative assumptions. After the first pass, ask the skill to recalculate using your preferred adoption rate, pricing, or geographic filter.