Positioning Icp

Positioning, ICP & Messaging Architecture for AI Products

Overview

This public intake copy packages packages/skills-catalog/skills/(gtm)/positioning-icp from https://github.com/tech-leads-club/agent-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Positioning, ICP & Messaging Architecture for AI Products You are an expert in AI product positioning, ICP definition, messaging architecture, and product-market fit validation. You combine April Dunford's positioning methodology with modern enrichment-signal-driven ICP building, outcome-focused messaging frameworks, and the reality that PMF in AI markets is perishable and must be revalidated quarterly. You understand the 2025-2026 buyer shift where business function leaders (not IT) now drive AI purchasing decisions, and you help founders translate technical capabilities into business outcomes that close deals.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, 1. Positioning Stack for AI Products, 2. Defining ICP with Enrichment Signals, 3. Competitive Positioning in Fast-Moving AI Markets, 4. Messaging Architecture, 5. The Buyer Shift: Business Leaders as AI Buyers.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when the request clearly matches the imported source intent: When the user wants to define their ideal customer profile, position an AI product, build messaging architecture, or validate product-market fit. Also use when the user mentions 'ICP,' 'ideal customer profile,'....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Before Starting

Gather this context before building any positioning, ICP, or messaging deliverable:

• What does the product actually do today? Get a one-paragraph description of the core capability, not the vision.
• Who are the current best customers? Ask for 3-5 accounts that renewed, expanded, or had the shortest sales cycles.
• What alternatives do prospects use before finding this product? Includes manual processes, spreadsheets, competitors, and internal tools.
• What is the current pricing model? Seat-based, usage-based, outcome-based, or hybrid.
• What is the primary sales motion? PLG, sales-led, community-led, or hybrid. Average deal size and sales cycle length.
• Who signs the contract today? Job title and department of the actual economic buyer.
• When was the last time the ICP or positioning was updated? If more than 90 days ago for an AI product, flag it as overdue.
• What is the current Sean Ellis score? If unknown, flag PMF validation as a prerequisite.

Examples

Example 1: Ask for the upstream workflow directly

Use @positioning-icp to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @positioning-icp against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @positioning-icp for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @positioning-icp using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

• User says: "Define our ICP and positioning" → Result: Agent gathers best customers, alternatives, pricing, and sales motion; builds four-layer positioning stack (Category, Wedge, Proof Vector, Alternative Framing); outputs ICP with firmographic + behavioral criteria and suggests 90-day revalidation.
• User says: "Our messaging doesn't convert" → Result: Agent asks who signs the contract and what stalls deals; runs "Would a VP forward this to CFO?" test; suggests messaging tiers (narrative, value props, features) and proof vectors; recommends A/B tests.
• User says: "How do we score and prioritize leads?" → Result: Agent recommends Fit + Intent weights (e.g. Firmographic 40%, Technographic 35%, Behavioral 25%); defines ACTIVATE threshold (high fit + high intent, respond <4 hr); ties to lead-enrichment for data.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in packages/skills-catalog/skills/(gtm)/positioning-icp, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

• Positioning feels stale → Cause: AI market moves fast; 90-day cadence not followed. Fix: Revalidate every 90 days; update category/wedge if competitors or model capabilities changed; refresh proof vectors.
• ICP too broad → Cause: "Everyone" or many segments. Fix: Pick 1–2 segments where you win most; use enrichment signals to narrow; document who is NOT a fit.
• Sean Ellis score unknown → Cause: PMF not measured. Fix: Run 40% "very disappointed" survey; if below threshold, flag PMF as prerequisite before scaling GTM.

For checklists, benchmarks, and discovery questions read references/quick-reference.md when you need detailed reference.

Related Skills

• @accessibility - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-cold-outreach - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-pricing - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-sdr - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• quick-reference.md
• quick-reference.md

Imported Reference Notes

Imported: 1. Positioning Stack for AI Products

AI products face a unique positioning challenge: the technology layer moves faster than the market layer. A positioning statement that worked 90 days ago may already be stale because model capabilities shifted, a competitor launched a similar feature, or buyer expectations evolved.

The Four-Layer Positioning Stack

Build positioning from the bottom up. Each layer must hold before the next one works.

+--------------------------------------------------+
|  ALTERNATIVE FRAMING                              |
|  "The [Competitor] alternative that [key diff]"   |
+--------------------------------------------------+
|  PROOF VECTOR                                     |
|  Quantified evidence the wedge delivers results   |
+--------------------------------------------------+
|  WEDGE                                            |
|  The specific capability gap you exploit           |
+--------------------------------------------------+
|  CATEGORY                                         |
|  The market context buyers already understand      |
+--------------------------------------------------+

Layer Definitions

Positioning Statement Template

For [target ICP segment] who [situation or trigger], [product name] is the [category] that [wedge/key differentiator], unlike [primary alternative], which [limitation of alternative]. We prove this with [proof vector].

Common Positioning Mistakes in AI

Imported: 2. Defining ICP with Enrichment Signals

Build your ICP from three signal layers, not gut feel. Modern ICP definition combines historical win data with real-time enrichment signals to create a living profile that adapts as the market shifts.

The Three Signal Layers

ICP Scoring Model

Keep firmographic/technographic fit and intent as separate dimensions. Collapsing them into a single score hides whether an account is a good fit but not ready, or a bad fit that is actively searching.

Fit Score (0-100)

Fit Score = (Firmographic Match * 0.4) + (Technographic Match * 0.35) + (Behavioral Fit * 0.25)

Intent Score (0-100)

Intent Score = (Third-Party Intent * 0.35) + (First-Party Signals * 0.40) + (Trigger Events * 0.25)

ICP Prioritization Matrix

                    High Intent
                        |
         NURTURE        |        ACTIVATE
     (Good fit,         |     (Good fit,
      not ready yet)    |      ready now)
                        |
  ----------------------+----------------------
                        |
         DISQUALIFY     |        MONITOR
     (Poor fit,         |     (Poor fit but
      not ready)        |      showing intent)
                        |
                    Low Intent

         Low Fit                    High Fit

• ACTIVATE (High Fit + High Intent): Route to sales immediately. These accounts match your ICP and are actively looking. Target response time: under 4 hours.
• NURTURE (High Fit + Low Intent): Enroll in targeted content sequences. They will convert when a trigger event hits.
• MONITOR (Low Fit + High Intent): Watch for ICP drift. If multiple "low fit" accounts convert, your ICP definition needs updating.
• DISQUALIFY (Low Fit + Low Intent): Do not spend resources. Revisit only during quarterly ICP refresh.

Enrichment Waterfall Architecture

Sequential enrichment checks multiple data providers until verified contact data is found. Stop at the first provider that returns high-confidence results to minimize cost.

Step 1: Clay (primary enrichment)
  |
  +--> Confidence >= 0.85? --> ACCEPT, stop
  |
  +--> Confidence < 0.85? --> Continue
  |
Step 2: Apollo (secondary)
  |
  +--> Confidence >= 0.85? --> ACCEPT, stop
  |
  +--> Confidence < 0.85? --> Continue
  |
Step 3: ZoomInfo (tertiary)
  |
  +--> Confidence >= 0.85? --> ACCEPT, stop
  |
  +--> Confidence < 0.85? --> Continue
  |
Step 4: BetterContact (verification layer)
  |
  +--> SMTP + catch-all validation
  +--> Final confidence score assigned
  +--> Confidence >= 0.50? --> ACCEPT with flag
  +--> Confidence < 0.50? --> REJECT

Confidence Thresholds

ICP Definition Workflow

1. Export your best 20-50 customers by NRR, deal velocity, or LTV
2. Run firmographic enrichment to find common patterns (industry, size, stage)
3. Run technographic enrichment to find stack commonalities
4. Analyze intent signals that preceded closed-won deals
5. Build the scoring model with weights derived from your data, not assumptions
6. Test against your pipeline to see if the model would have predicted your last 10 wins
7. Set a 90-day review cadence because in AI markets, your ICP drifts quarterly

Imported: 3. Competitive Positioning in Fast-Moving AI Markets

The Competitor Alternative SEO Play

"[Competitor] alternative" keywords carry extremely high purchase intent. Prospects searching these terms have already identified their problem and are actively evaluating solutions. These keywords often rank faster than category keywords because competition is lower.

Execution Checklist

Competitor Landing Page Structure

1. Headline: "Looking for a [Competitor] alternative?"
2. Pain acknowledgment: Why buyers leave [Competitor]
3. Comparison table: Feature-by-feature with honest gaps noted
4. Proof vector: Case study or metric from a switcher
5. Migration section: "Switch in under 30 minutes"
6. CTA: Free trial or demo, low commitment

Competitive Intelligence Cadence

Positioning Against Different Competitor Types

Imported: 4. Messaging Architecture

The Capability-to-Outcome Translation Framework

AI products chronically over-index on technical capabilities in their messaging. The fix is systematic translation from what the product does to what the buyer gets.

The Translation Test

If your messaging includes a model name, you are selling to engineers. If your messaging includes a business outcome, you are selling to buyers.

Three-Tier Messaging Architecture

Build messaging at three altitudes. Each tier serves a different audience and context.

+----------------------------------------------------------+
|  TIER 1: Strategic Narrative (CEO, Board, Press)          |
|  "Why this category matters now"                          |
|  One paragraph. No product features.                      |
+----------------------------------------------------------+
|  TIER 2: Value Proposition (VP/Director Buyer)            |
|  "What changes for your team when you adopt this"         |
|  3-5 bullet points. Business outcomes with proof.         |
+----------------------------------------------------------+
|  TIER 3: Feature Messaging (Evaluator/Champion)           |
|  "How it works and why the approach is better"            |
|  Detailed. Technical where appropriate. Comparison-ready. |
+----------------------------------------------------------+

Messaging Validation Checklist

Run every piece of messaging through these five checks:

Imported: 5. The Buyer Shift: Business Leaders as AI Buyers

Who Buys AI in 2025-2026

AI purchasing has shifted decisively from IT departments to business function leaders. Organizations that align leadership around AI priorities are nearly twice as likely to report above-average growth. This means your ICP, messaging, and sales motion must target the business buyer, not the CTO.

Implications for GTM

Mapping Your Messaging to the New Buyer

For every message, ask: "Would a VP of [department] forward this to their CFO to justify the purchase?" If the answer is no, the message is at the wrong altitude.

Imported: 6. Perishable PMF: Quarterly Revalidation

Why AI PMF Expires

In AI markets, PMF is not a milestone you reach and keep. Model capabilities evolve monthly, buyer expectations shift as they interact with better AI systems elsewhere, and new competitors launch weekly. Companies that validated PMF six months ago may already be losing it.

The data confirms this: only 5% of generative AI projects deliver real business value, often because teams validate once and assume the signal holds. Continuous revalidation is the fix.

The 90-Day PMF Revalidation Cadence

Run this cycle every quarter. Each component takes 1-2 weeks. Total cycle: 4-6 weeks, leaving buffer before the next one starts.

Sean Ellis Score Benchmarks for AI Products

PMF Decay Warning Signs

AI Pricing Model Landscape (Context for PMF)

Pricing directly affects PMF signals. The wrong model creates churn even when the product delivers value.

Cross-reference: See ai-pricing skill for detailed pricing strategy frameworks, willingness-to-pay research methods, and pricing page optimization.

Imported: 7. April Dunford's Positioning Framework Applied to AI

The "Obviously Awesome" methodology provides the most battle-tested positioning process. Here it is adapted for AI product realities.

The 10-Step Process (AI-Adapted)

Imported: 8. Implementation Playbook

Week 1-2: Discovery and Data Pull

• Export top 20-50 customers by NRR, deal velocity, or LTV
• Run firmographic + technographic enrichment via Clay or Apollo
• Analyze intent signals that preceded last 10 closed-won deals
• Interview 5 best customers: "Why did you buy? What alternatives did you consider?"
• Pull competitor positioning from their homepage, G2, and recent funding announcements

Week 3: Build ICP and Scoring Model

• Define firmographic, technographic, and behavioral fit criteria with weights
• Build intent scoring model with third-party, first-party, and trigger components
• Back-test model against last quarter's wins and losses
• Set up enrichment waterfall in Clay with confidence thresholds
• Document ICP in a single-page reference sheet the sales team can use

Week 4: Positioning and Messaging

• Complete the four-layer positioning stack
• Write Tier 1 narrative (one paragraph, no features)
• Write Tier 2 value propositions (3-5 bullets with proof)
• Write Tier 3 feature messaging (detailed, comparison-ready)
• Run the five-check validation on every message
• Build competitor comparison pages for top 3 alternatives

Week 5-6: Validate and Ship

• Test messaging with 5 prospects in discovery calls
• Run Sean Ellis survey if PMF score is unknown
• Update website, sales deck, and outreach sequences
• Brief sales team on new positioning and ICP criteria
• Set 90-day calendar reminder for revalidation cycle