
Icp Builder
This walks you through building customer profiles and buyer personas with actual structure instead of guessing. It separates B2B (firmographics, tech stack, buying triggers) from B2C (demographics, psychographics, behavior), then maps pain points to channels and objections. The interview and survey templates are solid if you're doing real customer research. It forces you to label assumptions versus validated insights, which is the part most teams skip. Best for early stage products or when your targeting feels fuzzy. Outputs persona cards, ICP docs, and a validation checklist. It's thorough but you'll want to skip sections if you already have customer data.
- skills.sh installs
- 549
- GitHub stars
- 736
- last updated
- Aug 21, 2026
Install
$ npx -y skills add openclaudia/openclaudia-skills --skill icp-builder --agent claude-codeInstalls into .claude/skills of the current project.
Files
Ideal Customer Profile (ICP) Builder
You are an expert in customer research and persona development. When the user asks you to define their ideal customer, build personas, or segment their audience, follow this framework.
Step 1: Gather Context
Establish: product/service, problem solved, current customers (if any), market type (B2B/B2C), price point, sales motion (self-serve/sales-assisted/enterprise), stage (pre-launch/early/growth), existing data (analytics, CRM, surveys), geography.
Step 2: ICP Framework
B2B ICP (Company-Level)
FIRMOGRAPHICS
Industry: [specific verticals]
Size (employees): [range] | Size (revenue): [range]
Growth Stage: [startup/scaleup/enterprise/public]
Geography: [regions] | Business Model: [SaaS/e-comm/agency/etc.]
TECHNOGRAPHICS
Tech Stack: [tools they use] | Current Solution: [for this problem]
Technical Maturity: [early adopter/mainstream/laggard]
SITUATIONAL TRIGGERS (events creating buy urgency)
- [e.g., "Just raised funding", "Grew past 50 employees"]
- [e.g., "Current contract ending", "New VP hired"]
Sponsors
Details
- Repository
- github.com/openclaudia/openclaudia-skills
- Category
- Data Science & ML
- First seen
- Jun 3, 2026
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