Market Sizing (TAM / SAM / SOM)

Estimate the opportunity with defensible top-down and bottom-up numbers — and, crucially, ground the wedge (which slice you actually win first) in real customer evidence. Most market-sizing decks are context-only guesses; the Evermuse edge is proving the beachhead is real with customers who already want it.

This is the showcase skill for nature=context: the market numbers come from external/industry signals, and customer evidence is used to validate the wedge, not to size the whole market.

Step 0 — Relevance & availability

Confirm this is a market-opportunity question and Evermuse is connected (see using-evermuse Step 0). If disconnected, produce the sizing framework labeled ⚠ ungrounded (external numbers still possible via web research, but the wedge won't be evidence-validated) and tell the user to authorize the MCP.

Evermuse Grounding (required)

Follow ${CLAUDE_PLUGIN_ROOT}/skills/using-evermuse/SKILL.md.

Required — one parallel batch of searches. Verify the product (Rule 1), then fire in a single parallel batch:

• 3–4 evidence searches, each worded from a different angle (limit up to 50). Evidence comes back rich and varied — expect large, useful result sets.
• one guidance search and one context search (limit up to 50). These are usually sparse or empty; run them anyway and note when they're thin.

Read each response's digest — it reports how many more results exist. Use judgment on whether a query is worth pulling deeper (raise limit toward the 100 max and/or page with next_offset to avoid repeats), weighing payload size, remaining context, task complexity, and the value of the data. For deep pulls, consider spawning sub-agents — instruct them to return every citation with the same metadata the tools return (url, who_said_it, meeting_name, created_at) so you can still cite.

Optional — considered use. Once grounded, reach for the other tools only when they add value: a quote-angled search for verbatim voice, read_source for a single deep dive, inline citations (references/citations.md), and add_source to save the deliverable. Sources, citations, and saving are optional — not required.

Instructions

Size the market for $ARGUMENTS within the stated constraints (geography, vertical, customer type).

1. Market definition — the problem space, segment boundaries, geography, scoping decisions.
2. TAM — top-down (industry total → relevant slice, cite sources) and bottom-up (customers × price × frequency) to cross-validate; reconcile the two.
3. SAM — the portion realistically serviceable given product, channels, language, pricing tier; as a % of TAM with reasoning.
4. SOM — achievable share in 1-3 years given competitive position and GTM capacity; this is where the customer wedge validates the number — if evidence shows a hot beachhead, cite it as the basis for near-term obtainability.
5. Growth projection — 2-3 year evolution and the drivers/trends behind it.
6. Assumptions & risks — numbered, each with a confidence level (high/med/low) and how to validate it.

Summary table

| Metric | Current estimate | 2-3 yr projection | Basis |
|--------|------------------|-------------------|-------|
| TAM | | | [external source] |
| SAM | | | product/channel constraints |
| SOM | | | GTM capacity + evidenced wedge [`1`](URL) |

Wedge validation (the Evermuse differentiator)

A short block: which slice do we win first, and who in the corpus already wants it? 1-2 verbatim quotes proving urgency:

> "[verbatim quote showing pull/urgency]" — [Name], [Meeting], [Date] [`1`](URL)

Close with key assumptions & risks, keeping market sources and customer evidence clearly separated via inline citations. Be explicit about what's data vs. estimate, and where confidence intervals are wide.

Further reading

• TAM/SAM/SOM method adapted from phuryn/pm-skills (MIT); wedge-validation grounding is Evermuse-native. See Market Research: Advanced Techniques.