arbitrage-audit-data
Skill5 starsChanged 2 months agoMITView on GitHub
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?"
In this repository24 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- analystSkill
- dashboards-to-decisionsSkill
- databricks-data-product-reviewSkill
- data-consumer-discoverySkill
- data-model-designSkill
- data-pipeline-qualitySkill
- data-product-operatorSkill
- data-product-thinkingSkill
- data-product-validationSkill
- data-quality-assessmentSkill
- data-storytellingSkill
- data-team-operating-modelSkill
- data-team-positioningSkill
- duckdb-data-profilingSkill
- ethical-risk-assessmentSkill
- grill-me-dataSkill
- healthcare-cohort-designSkill
- healthcare-data-domainSkill
- healthcare-data-readiness-debriefSkill
- healthcare-data-reconciliationSkill
- metrics-definitionSkill
- research-synthesis-dataSkill
- stakeholder-alignmentSkill
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 16
Every data product sits on top of a gap.
- Distinctive: line 27
Ask: "If a competitor had the same data and unlimited AI, what would still be hard for them to replicate?"
- Distinctive: line 46
When AI closes the "generate the report" gap, the new gap is: "who decides if this report is answering the right question?"
- Distinctive: line 48
When AI closes the "connect the data sources" gap, the new gap is: "who decides what the connected data means for this specific business …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.
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