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hollandkevint (@hollandkevint) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Arbitrage Audit Data by hollandkevint

Name: Arbitrage Audit Data
Author: hollandkevint

Arbitrage Audit Data

3 diagnostic questions for evaluating data product markets through the arbitrage gap lens. Identifies whether your data product sits on a durable or closing advantage. Use when assessing data product positioning, evaluating market risk, or when someone asks "is AI going to replace this?" or "what's our moat?"

File contents

The 3 Questions (Data Product Context)

Question 1: What inefficiency is this data product built on?

Every data product sits on top of a gap. Name it:

Gap Type	Data Product Example	Closing Speed
Knowledge asymmetry	"Only our analysts know how to calculate this metric"	Fast — AI can learn metric definitions
Fragmentation	"Data lives in 5 systems nobody has connected"	Medium — integration tools accelerating
Speed	"This report takes 3 days of manual SQL"	Fast — automation and agents
Discipline	"People skip the quality checks"	Medium — AI can enforce process
Judgment	"Someone needs to decide which cohort definition is clinically valid"	Slow — requires domain expertise
Relationship	"The client trusts our interpretation, not just the numbers"	Slow — fundamentally human

Ask: "If a competitor had the same data and unlimited AI, what would still be hard for them to replicate?"

Question 2: How fast can AI close this gap?

Informational data products (reports, dashboards, automated queries) face fast closure. If your data product's value is "we run the SQL so you don't have to," the clock is ticking.

Judgment data products (cohort validation, clinical interpretation, business context) face slow closure. If your data product's value is "we know what this number means for YOUR situation," that's durable.

Specific data product signals:

Signal	Gap Closing	Action
Consumer could get the same answer from ChatGPT + raw data	Fast	Migrate to judgment layer
Consumer needs your domain expertise to interpret results	Slow	Encode and protect that expertise
Consumer uses your output as input to another automated system	Fast	The consuming system will eventually skip you
Consumer uses your output to make human decisions	Slow	Double down on decision context

Question 3: What new gap opens when this one closes?

When AI closes the "generate the report" gap, the new gap is: "who decides if this report is answering the right question?"

When AI closes the "connect the data sources" gap, the new gap is: "who decides what the connected data means for this specific business context?"

For data teams: The upstream gap is always decision architecture — knowing what to measure, why, and what to do with the answer. That's where data-team-positioning (demand-shaper stance) lives.

Output

# Data Product Arbitrage Audit: [Product Name]
Date: [date]

## The Gap
[What inefficiency this product is built on]

## Gap Type & Closing Speed
[Type] — [Fast/Medium/Slow] — [rationale]

## Upstream Gap
[What becomes the new bottleneck]

## Position Assessment
[Closing gap → migrate. Durable gap → encode and protect.]

## Recommended Action
[One specific thing]

Based on Nate Jones' Arbitrage Gap Framework. Generic version: decision-architecture.

Part of the Data Product Operator plugin.

---name: arbitrage-audit-datadescription: 3 diagnostic questions for evaluating data product markets through the arbitrage gap lens. Identifies whether your data product sits on a durable or closing advantage. Use when assessing data product positioning, evaluating market risk, or when someone asks "is AI going to replace this?" or "what's our moat?"---## The 3 Questions (Data Product Context)### Question 1: What inefficiency is this data product built on?Every data product sits on top of a gap. Name it:| Gap Type | Data Product Example | Closing Speed ||----------|---------------------|---------------|| **Knowledge asymmetry** | "Only our analysts know how to calculate this metric" | Fast — AI can learn metric definitions || **Fragmentation** | "Data lives in 5 systems nobody has connected" | Medium — integration tools accelerating || **Speed** | "This report takes 3 days of manual SQL" | Fast — automation and agents || **Discipline** | "People skip the quality checks" | Medium — AI can enforce process || **Judgment** | "Someone needs to decide which cohort definition is clinically valid" | Slow — requires domain expertise || **Relationship** | "The client trusts our interpretation, not just the numbers" | Slow — fundamentally human |Ask: "If a competitor had the same data and unlimited AI, what would still be hard for them to replicate?"### Question 2: How fast can AI close this gap?**Informational data products** (reports, dashboards, automated queries) face fast closure. If your data product's value is "we run the SQL so you don't have to," the clock is ticking.**Judgment data products** (cohort validation, clinical interpretation, business context) face slow closure. If your data product's value is "we know what this number means for YOUR situation," that's durable.Specific data product signals:| Signal | Gap Closing | Action ||--------|-------------|--------|| Consumer could get the same answer from ChatGPT + raw data | Fast | Migrate to judgment layer || Consumer needs your domain expertise to interpret results | Slow | Encode and protect that expertise || Consumer uses your output as input to another automated system | Fast | The consuming system will eventually skip you || Consumer uses your output to make human decisions | Slow | Double down on decision context |### Question 3: What new gap opens when this one closes?When AI closes the "generate the report" gap, the new gap is: "who decides if this report is answering the right question?"When AI closes the "connect the data sources" gap, the new gap is: "who decides what the connected data means for this specific business context?"**For data teams:** The upstream gap is always decision architecture — knowing what to measure, why, and what to do with the answer. That's where `data-team-positioning` (demand-shaper stance) lives.## Output```markdown# Data Product Arbitrage Audit: [Product Name]Date: [date]## The Gap[What inefficiency this product is built on]## Gap Type & Closing Speed[Type] — [Fast/Medium/Slow] — [rationale]## Upstream Gap[What becomes the new bottleneck]## Position Assessment[Closing gap → migrate. Durable gap → encode and protect.]## Recommended Action[One specific thing]```Based on Nate Jones' Arbitrage Gap Framework. Generic version: [decision-architecture](https://github.com/hollandkevint/decision-architecture).---Part of the [Data Product Operator](https://github.com/hollandkevint/data-product-operator) plugin.

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