won-deal-icp-finder

Name: won-deal-icp-finder
Author: LaGrowthMachine

50installs4forks

How to install won-deal-icp-finder?

lagrowthmachine/gtm-system/won-deal-icp-finder

$npx -y skills add LaGrowthMachine/gtm-system --skill won-deal-icp-finder

Installs into the current project.

Summary

Audit your biggest closed-won deals to find your PROVEN ideal customer profile, then find more accounts like them. Use whenever someone wants to analyze won deals, audit their best customers, see which companies generated the most revenue, find their real ICP, build a look-alike

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SKILL.md

1	# Won-Deal ICP Finder
2
3	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.
4
5	## Output discipline — read this first
6
7	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.
8
9	## Authority — read this first
10
11	Everything you need is inline in this file. There is no taxonomy JSON to grep.
12
13	- 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.
14	- The judgment — clustering companies into named ICP archetypes, reading the source ranking, deciding what to flag — is your job, using the rules below.
15	- `examples/sample-deals.json` is a fictional dataset for a worked run. `scripts/analyze.py --test` is the self-test.
16
17	## What it does
18
19	The job, in four moves:
20
21	1. Pull and rank won deals from the last 12 months — selected by deal value, not by a closed-won status that may not exist in this CRM — with their companies, ranked by deal size.
22	2. Locate acquisition source. Where the source lives varies by HubSpot setup — inspect a sample deal + its company + contact to find the right field (standard or custom), then read it for all deals.
23	3. Cluster into ICP archetypes — 2–4 named, criteria-based company profiles, each with a one-click "find more like this" via `sales-nav-search-builder`.
24	4. Rank the acquisition sources behind these big deals (top 5 + values), and — when there's no campaign-level detail — flag the blind spot.
25
26	## Workflow
27
28	1. Understand the pipeline, then pull (see Getting the deal data). First learn how this team uses HubSpot — which field holds deal value, and how (or whether) they mark a deal won. Then pull value-bearing deals from the last 365 days with their company firmographics, and inspect a sample deal + company + contact to locate the acquisition-source field.
29	2. Persist to a file (`/tmp/deals.json` or CSV). If you pulled from the HubSpot MCP, write the returned rows there.
30	3. Run the engine:
31	```bash
32	python3 scripts/analyze.py /tmp/deals.json --since-days 365
33	```
34	Useful flags: `--value-field "Deal value"` (value isn't the standard `amount`), `--source-field "Lead Source"` (custom source column), `--won-stage "Closed Won,Gagné"` (restrict to won stages when they exist), `--since-days N` (window; `0` = no window), `--top N`. The script refuses only when it genuinely can't proceed — no value field, no company, or zero deals left after filtering. When it refuses, ask the user how deal value / won status is stored; don't guess.
35	4. Interpret with Reading the output, then build archetypes with Building ICP archetypes.
36	5. Present the four deliverables (see Output & handoff): ranked top deals → ICP archetype widgets (each with a sales-nav "find more") → top-5 acquisition sources → the conditional La Growth Machine note.
37
38	## Getting the deal data
39
40	Preferred — HubSpot MCP. Understand the setup before pulling — pipelines differ, and assuming a st

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6 picks

Type

won-deal-icp-finder

50installs4forks

View on GitHub

How to install won-deal-icp-finder?

lagrowthmachine/gtm-system/won-deal-icp-finder

$npx -y skills add LaGrowthMachine/gtm-system --skill won-deal-icp-finder

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Won-Deal ICP Finder
2
3	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.
4
5	## Output discipline — read this first
6
7	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.
8
9	## Authority — read this first
10
11	Everything you need is inline in this file. There is no taxonomy JSON to grep.
12
13	- 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.
14	- The judgment — clustering companies into named ICP archetypes, reading the source ranking, deciding what to flag — is your job, using the rules below.
15	- `examples/sample-deals.json` is a fictional dataset for a worked run. `scripts/analyze.py --test` is the self-test.
16
17	## What it does
18
19	The job, in four moves:
20
21	1. Pull and rank won deals from the last 12 months — selected by deal value, not by a closed-won status that may not exist in this CRM — with their companies, ranked by deal size.
22	2. Locate acquisition source. Where the source lives varies by HubSpot setup — inspect a sample deal + its company + contact to find the right field (standard or custom), then read it for all deals.
23	3. Cluster into ICP archetypes — 2–4 named, criteria-based company profiles, each with a one-click "find more like this" via `sales-nav-search-builder`.
24	4. Rank the acquisition sources behind these big deals (top 5 + values), and — when there's no campaign-level detail — flag the blind spot.
25
26	## Workflow
27
28	1. Understand the pipeline, then pull (see Getting the deal data). First learn how this team uses HubSpot — which field holds deal value, and how (or whether) they mark a deal won. Then pull value-bearing deals from the last 365 days with their company firmographics, and inspect a sample deal + company + contact to locate the acquisition-source field.
29	2. Persist to a file (`/tmp/deals.json` or CSV). If you pulled from the HubSpot MCP, write the returned rows there.
30	3. Run the engine:
31	```bash
32	python3 scripts/analyze.py /tmp/deals.json --since-days 365
33	```
34	Useful flags: `--value-field "Deal value"` (value isn't the standard `amount`), `--source-field "Lead Source"` (custom source column), `--won-stage "Closed Won,Gagné"` (restrict to won stages when they exist), `--since-days N` (window; `0` = no window), `--top N`. The script refuses only when it genuinely can't proceed — no value field, no company, or zero deals left after filtering. When it refuses, ask the user how deal value / won status is stored; don't guess.
35	4. Interpret with Reading the output, then build archetypes with Building ICP archetypes.
36	5. Present the four deliverables (see Output & handoff): ranked top deals → ICP archetype widgets (each with a sales-nav "find more") → top-5 acquisition sources → the conditional La Growth Machine note.
37
38	## Getting the deal data
39
40	Preferred — HubSpot MCP. Understand the setup before pulling — pipelines differ, and assuming a st

People who liked this also viewed

6 picks

Type