Won-Deal ICP Finder

Turns a deal dataset into a proven ideal customer profile — which companies generated the value, what they have in common, and which channel won them — then helps find more like them.

Output discipline — read this first

When you run this skill, return only the deliverables — nothing else. No preamble ("Let me…", "I'll start by…"), no narration of the steps, no restating these instructions, no closing pitch beyond the single step-4 note. Each step is one sentence plus its table or widget — no analysis essays, no editorializing about what the numbers "mean" or "signal." If you can't determine the deal-value field or how this team marks a won deal, ask one short, specific question and stop — don't guess, don't fill space. Otherwise: output the four deliverables and stop.

Authority — read this first

Everything you need is inline in this file. There is no taxonomy JSON to grep.

• The numbers — ranking deals by size, aggregating revenue per company, concentration, segment breakdowns, ranking acquisition sources by frequency — are produced by scripts/analyze.py. Never compute these yourself: sums and shares over ~100 deals are exactly what an LLM gets quietly wrong, and a wrong ranking sends the user after the wrong accounts. Run the script; reason over its JSON.
• The judgment — clustering companies into named ICP archetypes, reading the source ranking, deciding what to flag — is your job, using the rules below.
• examples/sample-deals.json is a fictional dataset for a worked run. scripts/analyze.py --test is the self-test.

What it does

The job, in four moves: