moatmri

name	moatmri
description	Analyze AI disruption pressure across a business, map competitive exposure, and produce a 90-day defensive action plan.
category	AI & Agents
source	antigravity
tags	["ai","automation","cro"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/moatmri

MoatMRI — AI Disruption Pressure Analysis

Where does intelligence pressure break this system first?

When to Use This Skill

• "Is my business at risk from AI? Where am I most exposed?"
• "How would an AI-native startup take over my market?"
• "What should I do in the next 90 days to defend against AI disruption?"
• "I'm doing due diligence on [company] — what's their AI displacement risk?"
• "Where does my competitive moat actually hold against AI pressure?"

How It Works

Step 1 — Gather Inputs

Ask if not provided:

• Industry (e.g., "real estate", "community banking", "retail pharmacy", "law firm")
• Entity type (e.g., "independent broker", "solo practitioner", "regional franchise")
• Target name (optional — specific organization for named analysis)

Limitations

• Produces strategic risk analysis, not audited market research or investment advice.
• Depends on current company, market, regulatory, and competitive context supplied by the user or gathered from reliable sources.
• Treats disruption scenarios as planning tools; scores should be revisited as new evidence appears.

Step 2 — 10-Vector Pressure Map

Score AI disruption pressure across exactly these 10 vectors (0–10):

#	Vector	What to Measure
1	labor_substitution	Which roles/functions are directly automatable
2	customer_interface	How AI changes how customers reach this entity
3	knowledge_commoditization	Does AI commoditize the expertise this entity sells
4	pricing_pressure	Does AI enable lower-cost competitors to undercut
5	supply_chain_automation	Does AI change input costs or supplier relationships
6	data_moat	Does this entity have proprietary data AI can't replicate
7	trust_relationship_moat	How much does customer loyalty protect against displacement
8	distribution_channel_disruption	Does AI create new channels that bypass this entity
9	regulatory_compliance_exposure	Does AI alter the regulatory or liability landscape
10	decision_speed_gap	Does AI accelerate decisions in ways that disadvantage this entity

For each vector produce: score, headline, near_term (12 months), far_term (3 years).

Aggregate risk score: mean of all 10 vectors. Flag any vector ≥ 7 as critical.

Step 3 — AI Front-Door Takeover Storyboard

6-step narrative of how an AI-native competitor displaces this entity:

1. The entry point
2. The wedge (first 10% of market)
3. The acceleration (what makes it compound)
4. The tipping point (when incumbent can't recover)
5. The aftermath
6. The survivor profile

Step 4 — 90-Day Counterstrike Plan

• Track A (Days 0–30): Immediate defense — what to stop, what to protect
• Track B (Days 31–60): Intelligence-layer build — data/relationships to fortify
• Track C (Days 61–90): Offensive positioning — use AI pressure as competitive weapon

Best Practices

• ✅ Score all 10 vectors before calculating aggregate — resist stopping at obvious ones
• ✅ Keep the storyboard specific to industry/entity, not generic disruption narrative
• ✅ Track C should be actionable within 90 days, not aspirational 3-year strategy
• ❌ Don't conflate data_moat with trust_relationship_moat — they protect differently

Additional Resources

• Repository: thebrierfox/moatmri-skill
• Full BYOK tool: ace-license-server-production.up.railway.app/byok/moatmri
• Built by IntuiTek¹ (~K¹) — MIT License