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v1.0.0
GitHub

About this Skill

Perfect for Next.js and Supabase Developers needing advanced competitive landscape analysis and moat definition capabilities. prd-v03-moat-definition is a systematic approach to building AI-powered products using progressive documentation and context-aware development workflows, positioned in the HORIZON workflow between Competitive Landscape and Pricing Model Selection.

Features

Consumes landscape map artifacts from Competitive Landscape Mapping
Utilizes CFD-* entries for competitive intelligence and documented competitors
Integrates with BR-* product type entries for informed product development
Supports workflow positioning between v0.2 Competitive Landscape and v0.3 Pricing Model Selection
Leverages progressive documentation for efficient product development
Enables context-aware development workflows for AI-powered products

# Core Topics

mattgierhart mattgierhart
[24]
[4]
Updated: 2/26/2026

Quality Score

Top 5%
59
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add mattgierhart/PRD-driven-context-engineering/prd-v03-moat-definition

Agent Capability Analysis

The prd-v03-moat-definition MCP Server by mattgierhart is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion. Optimized for how to use prd-v03-moat-definition, what is prd-v03-moat-definition, prd-v03-moat-definition alternative.

Ideal Agent Persona

Perfect for Next.js and Supabase Developers needing advanced competitive landscape analysis and moat definition capabilities.

Core Value

Empowers agents to define moats using landscape maps, competitive intelligence, and feature matrices, leveraging context-aware development workflows and progressive documentation, particularly with Claude integration.

Capabilities Granted for prd-v03-moat-definition MCP Server

Analyzing competitive landscapes for Next.js applications
Defining moats for Supabase-powered products
Generating feature matrices for competitive intelligence

! Prerequisites & Limits

  • Requires prior work from v0.2 Competitive Landscape Mapping
  • Needs landscape map artifact and CFD-* entries
  • Specific to Next.js, Supabase, and Claude ecosystems
SKILL.md
Readonly

Moat Definition

Position in HORIZON workflow: v0.2 Competitive Landscape → v0.3 Moat Definition → v0.3 Pricing Model Selection

Consumes

This skill requires prior work from v0.2:

  • Landscape map artifact (from Competitive Landscape Mapping) — Current behavior documentation, feature matrix, competitor analysis
  • CFD-* entries (competitive intelligence, from Competitive Landscape Mapping) — Documented competitors with pricing, features, user feedback
  • BR-* product type entry (from Product Type Classification) — Classification constrains which competitors are relevant to analyze

This skill assumes v0.2 analysis is complete with documented competitors.

Produces

This skill creates/updates:

  • CFD-* entries (competitor moat analysis) — Assessment of each competitor's defensibility by moat type
  • BR-* entries (targeting rules) — Constraints derived from moat analysis, defining where to compete vs. avoid
  • Moat strength inventory artifact — Summary of competitor moats with vulnerability signals

All CFD moat analysis entries should include:

  • confidence: 2-3/5 (based on public evidence + user interviews about switching friction)
  • Evidence source (pricing pages, reviews, customer interviews)
  • Forward target: "Would move to 4/5 if we interview 5+ current/former customers about switching costs"

Example moat analysis entry:

markdown
1CFD-055: Competitor Moat Analysis — Notion 2 3Competitor: Notion 4Primary Moat Type: Switching Costs (data lock-in) 5Moat Strength Tier: Strong 6Confidence: 3/5 (source: public-research + 2-user-interviews) 7Date: 2026-02-01 8 9Switching Cost Quantification: 10 - Financial: Multi-year contract, no early termination ($0 direct cost) 11 - Time/Effort: 20+ hours migration, team retraining 12 - Data Migration: Proprietary database format (complex export) 13 - Workflow Retraining: Unique templates, team habits 14 - Integration Rework: Deep Slack/GitHub dependencies 15 16Total Switching Cost: $3K in labor + 20 hours = Material friction 17Moat Verdict: Strong — switching costs >$3K + meaningful time investment 18 19Vulnerability Signal: SMB segment with small teams; they use <20% of feature set (opportunity for simpler tool) 20Targeting Decision: Avoid direct competition. Wedge in SMB with simplified, cheaper offering. 21 22Evidence: 23 - CFD-042 (landscape): Reviews show enterprise love; SMB complaints focus on cost + complexity 24 - CFD-015 (value hypothesis): SMB would save $12,500/year with simpler tool 25Next Target: "Would move to 4/5 if we interview 5+ SMB teams about exact switching cost dollars"

Moat Type Taxonomy

Every moat falls into one of six types. Identify primary + secondary moats per competitor:

Moat TypeDefinitionStrong WhenWeak When
Switching CostsFriction to leave (data, workflow, contracts)Multi-year data, deep integrationsEasy export, monthly contracts
Network EffectsValue increases with usersTwo-sided marketplace, content platformSingle-player tool, linear value
Data/IPProprietary data or algorithmsUnique training data, patentsCommodity ML, public datasets
Brand/TrustRecognition, credibilityRegulated industry, high-risk decisionsLow-stakes, undifferentiated
Scale/CostVolume economicsInfrastructure-heavy, marginal cost near zeroLabor-intensive, linear cost
RegulatoryCompliance barriersCertifications required, government contractsNo compliance requirements

For micro-SaaS: Switching costs and brand/trust matter most. Network effects and scale rarely apply.

Moat Strength Tiers

Rate each competitor's defensibility:

TierCriteriaEvidence SignalsTargeting Implication
ImpenetrableMulti-layered moat, 10+ years data lock-in"Would take years to switch"Avoid direct competition
StrongSignificant switching friction, 1-2 year contractsHigh NPS + low churn despite complaintsTarget underserved segments only
ModerateSome friction, workarounds existChurn 5-10%, export optionsWedge opportunity exists
WeakEasy to replace, commodity offeringMonthly plans, high churn, price shoppingDirect competition viable
ErodingFormer strength decliningNew alternatives gaining shareAggressive targeting

Gate rule: Don't compete where incumbent has Impenetrable or Strong moat unless targeting segment they explicitly ignore.

Switching Cost Inventory

Quantify ALL switching costs — the sum determines moat strength:

Cost TypeHigh ImpactLow ImpactHow to Assess
Financial>6mo contract, early termination feesMonthly billing, no penaltyCheck pricing page terms
Time/Effort40+ hr migration, retraining<4 hr setup, familiar UXTrial the competitor
Data MigrationProprietary format, no exportStandard export (CSV, API)Test export function
Workflow RetrainingUnique methodology, team habitsStandard patternsRead onboarding docs
Integration ReworkDeep API dependenciesStandalone toolMap their integrations

Calculation: Sum hours + dollars. >$5K or >40hr = material switching cost.

Targeting Decision Framework

Use moat analysis to determine where to compete:

Moat Impenetrable/Strong → DON'T COMPETE HERE
                          ↓ unless
                          Target ignored segment (SMB, specific vertical)
                          
Moat Moderate → WEDGE STRATEGY
                ↓ identify
                Entry point that bypasses switching friction
                
Moat Weak/Eroding → DIRECT COMPETITION
                    ↓ execute
                    Feature + price attack on their core

Wedge Opportunity Signals

A wedge exists when:

  • Competitor moat doesn't apply to specific segment
  • One feature has LOW switching cost (can start there)
  • Integration allows coexistence (not replacement)
  • Price sensitivity > switching friction

Analysis Workflow

Step 1: Pull Competitor Data

Retrieve CFD- entries from v0.2 Competitive Landscape. For each competitor, you need: pricing, complaints, feature set.

Step 2: Identify Moat Type

For each competitor, determine primary moat type. Use evidence from reviews, pricing structure, integration depth.

Step 3: Rate Moat Strength

Apply tier criteria. Flag if insufficient evidence (Tier 4-5 confidence).

Step 4: Inventory Switching Costs

Complete the 5-category switching cost assessment. Quantify hours + dollars.

Step 5: Identify Vulnerabilities

Where is their moat weakest? Which segments do they ignore? What's eroding?

Step 6: Generate IDs

CFD entries (customer_feedback.md): Template: assets/cfd-moat-analysis.md

CFD-MOT-###: [Competitor] Moat Analysis — [Moat Type], [Strength Tier]

BR entries (BUSINESS_RULES.md): Template: assets/br-targeting.md

BR-TGT-###: [Targeting Rule] — based on [Competitor] moat weakness

Anti-Patterns to Avoid

Don'tDo Instead
"They're big"Specify which moat type + evidence
Assume low switching costQuantify: hours + dollars
Only analyze direct competitorsInclude Type 4-5 (workarounds, inertia)
Underestimate integration moatMap actual dependency depth
Ignore eroding moatsTrack signals: new entrants, complaints
Target where moat is strongFind the segment where moat doesn't apply

Output Requirements

Before advancing to Our Moat Articulation:

  • ≥3 competitors with moat type identified
  • ≥2 competitors with switching costs quantified
  • Moat strength tier assigned (with evidence)
  • Targeting decision per competitor (compete/avoid/wedge)
  • CFD-MOT entries created (≥3)
  • BR-TGT entries created (≥2)

Downstream Connections

ConsumerWhat It NeedsFormat
v0.3 Our Moat ArticulationWhere competitors are weak, what moats workCFD-MOT entries
v0.3 Pricing ModelWhat price points bypass switching frictionBR-TGT entries
v0.5 Red TeamRisks of competitor responseMoat strength tiers
v0.9 GTMPositioning against competitor moatsTargeting rules

Detailed References

  • Good/bad examples: See references/examples.md
  • CFD-MOT template: See assets/cfd-moat-analysis.md
  • BR-TGT template: See assets/br-targeting.md

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