Beachhead Segment

Pick the smallest winnable, referenceable first market — the wedge that gets you to PMF fastest and opens adjacent expansion. The Evermuse twist: instead of brainstorming segments in the abstract, you measure demand density in the actual corpus — which segment's people show up most often and most painfully in real conversations — then pressure-test the front-runner against four criteria, each backed by evidence.

Step 0 — Relevance & availability

Confirm this is a first-market / segmentation task and Evermuse is connected. If the tools aren't present, produce a framework-only analysis labeled ⚠ ungrounded — Evermuse not connected and tell the user to authorize the MCP. See ${CLAUDE_PLUGIN_ROOT}/skills/using-evermuse/SKILL.md Step 0.

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

You are choosing the beachhead for $ARGUMENTS. Where the source method reaches for "customer interviews / market research," use the Evermuse corpus as the primary source: demand density from find_sources, pain from evidence searches and quotes.

Step 1 — Read demand density from the corpus

List the candidate segments (verticals, company sizes, roles, use cases). For each, gauge presence in the evidence:

• Frequency — how many distinct accounts/meetings feature this segment (find_sources by attendee_domain, which matches transcript participants as well as calendar invitees).
• Intensity — how burning the pain reads in their quotes (quote-angled search, filters-only search). The segment with the most people showing up, most strongly, is your demand-density front-runner — it earns the burden of proof, not an automatic win.

Step 2 — Score the front-runners on four criteria

For the top 2–3 candidates, evaluate each criterion and back it with evidence:

1. Burning pain — acute, daily, unmet? Cite the sharpest pain quote and count how many accounts echo it. Thin evidence is a finding, not a gap to paper over.
2. Willingness to pay — is there budget, clear ROI, and no free workaround that fully satisfies? Cite any quotes about cost of the problem or current spend.
3. Winnable share — can you realistically take 60–70% in 3–18 months? Assess competitive saturation (context) and your differentiation; large enough but not owned.
4. Reachability — can you actually reach and refer within this segment? Look for professional communities, network effects, and adjacency to segments already in the corpus (a solved beachhead should reference into the next market).

Step 3 — Select and plan

Choose the segment with the best combined picture: strongest cited pain, clear willingness to pay, winnable, reachable, and a natural bridge to adjacent segments. Then outline a 90-day acquisition plan for the beachhead and a post-beachhead expansion path (which adjacent segment the references unlock next).

Deliverable format

# Beachhead — [product]

**Demand density ranking**
| Segment | Accounts in corpus | Pain intensity | Note |
|---|---|---|---|
| [seg] | [N meetings/domains] | [high/med/low] | [one line] |

**Recommended beachhead: [segment]**

| Criterion | Verdict | Evidence |
|---|---|---|
| Burning pain | [strong/weak] | > "[quote]" — [attribution] [`1`](URL) ([M accounts]) |
| Willingness to pay | … | [cited [`2`](URL)] |
| Winnable share | … | [competitive context, cited [`3`](URL)] |
| Reachability | … | [cited [`4`](URL)] |

**Why first:** [1–2 sentences]. **90-day acquisition plan:** […]. **Expansion path:** [next adjacent segment, why].

Honesty when evidence is thin

If a candidate segment barely appears in the corpus, say so plainly — "only 2 meetings, one account" — rather than inflating it into a market. A segment with no evidence isn't a beachhead; it's a hypothesis worth an interview (/evermuse:interview if available).

Save & hand off

After saving, offer: "Build the full ICP for this segment?" (/evermuse:ideal-customer-profile), "Plan the launch into it?" (/evermuse:gtm-strategy), or "Position for this segment?" (/evermuse:positioning-and-messaging).

Further reading

• Adapted from phuryn/pm-skills beachhead-segment (MIT); grounding is Evermuse-native.
• Based on Geoffrey Moore's beachhead strategy in Crossing the Chasm.
• 5 GTM Principles You Should Know as a PM
• How to Achieve Product-Market Fit? Part I