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positioning-icp

Build positioning stacks, ICP scoring models, and messaging architecture for AI products.

Updated Sep 15, 2026
Search Tags:#messaging#positioning#lead scoring#product-market fit#gtm#icp#ai products
One-click install
npx skills add https://github.com/Peterson-Benhame/agent-skills --skill positioning-icp-peterson-benhame
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: positioning-icp
Source: https://github.com/Peterson-Benhame/agent-skills/tree/main/packages/skills-catalog/skills/%28gtm%29/positioning-icp
Command: npx skills add https://github.com/Peterson-Benhame/agent-skills --skill positioning-icp-peterson-benhame

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? AI product teams struggle to define their ideal customer profile, sharpen positioning in fast-moving markets, and translate technical capabilities into business outcomes that close deals. This Skill provides structured frameworks for positioning, ICP scoring, messaging architecture, and quarterly PMF revalidation. ## Core Features & Use Cases - Four-Layer Positioning Stack: Build Category, Wedge, Proof Vector, and Alternative Framing layers using April Dunford's methodology adapted for AI products. - ICP Scoring with Enrichment Signals: Combine firmographic, technographic, and intent signals into Fit and Intent scores with a prioritization matrix and enrichment waterfall. - Three-Tier Messaging Architecture: Translate technical capabilities into business outcomes across strategic narrative, value proposition, and feature messaging tiers. - PMF Revalidation: Run a 90-day cycle with Sean Ellis surveys, cohort retention analysis, and competitive audits to detect PMF decay. - Use Case: A founder whose messaging is not converting asks for help; the Skill runs the "Would a VP forward this to their CFO?" test, rebuilds messaging tiers with proof vectors, and sets a 90-day revalidation cadence. ## Quick Start Ask the agent to define your ICP and positioning by providing your best customers, main alternatives, pricing model, and sales motion.

Frequently Asked Questions about positioning-icp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I define an ICP for an AI product?▼

Build the ICP from three signal layers: firmographic (industry, size, funding stage), technographic (stack fit, API maturity), and intent (content consumption, trigger events). Score Fit and Intent separately, then prioritize accounts in an ACTIVATE/NURTURE/MONITOR/DISQUALIFY matrix.

How do I score and prioritize inbound leads?▼

Use a Fit Score weighted 40% firmographic, 35% technographic, and 25% behavioral, plus an Intent Score weighted 40% first-party, 35% third-party, and 25% trigger events. Route high-fit, high-intent accounts to sales with a response time under 4 hours.

What is a good Sean Ellis score for product-market fit?▼

A Sean Ellis score of 40% or more respondents answering "very disappointed" indicates PMF is achieved. Scores of 20-30% signal weak fit requiring ICP narrowing, while scores below 20% suggest a pivot is needed.

How often should AI product positioning be updated?▼

AI product positioning should be revalidated every 90 days because model capabilities, competitor launches, and buyer expectations shift quarterly. The cycle includes a Sean Ellis survey, cohort retention analysis, competitive audit, and ICP refresh.

Why does AI product messaging fail to convert?▼

Messaging fails when it leads with model names or technical capabilities instead of business outcomes. Apply the test: would a VP forward the message to their CFO to justify the purchase? If not, rewrite it at the correct altitude with quantified proof vectors.

When should I not use this positioning framework?▼

Do not use it for technical implementation, code review, or software architecture decisions. It is designed for GTM strategy work such as ICP definition, messaging, and PMF validation, not engineering tasks.

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