data-validation-question
Sub-skill of data-validation: Question (+9).
Best use case
data-validation-question is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of data-validation: Question (+9).
Teams using data-validation-question should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/question/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How data-validation-question Compares
| Feature / Agent | data-validation-question | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of data-validation: Question (+9).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Question (+9)
## Question
[The specific question being answered]
## Data Sources
- Table: [schema.table_name] (as of [date])
- Table: [schema.other_table] (as of [date])
- File: [filename] (source: [where it came from])
## Definitions
- [Metric A]: [Exactly how it's calculated]
- [Segment X]: [Exactly how membership is determined]
- [Time period]: [Start date] to [end date], [timezone]
## Methodology
1. [Step 1 of the analysis approach]
2. [Step 2]
3. [Step 3]
## Assumptions and Limitations
- [Assumption 1 and why it's reasonable]
- [Limitation 1 and its potential impact on conclusions]
## Key Findings
1. [Finding 1 with supporting evidence]
2. [Finding 2 with supporting evidence]
## SQL Queries
[All queries used, with comments]
## Caveats
- [Things the reader should know before acting on this]
```
## Code Documentation
For any code (SQL, Python) that may be reused:
```python
"""
Analysis: Monthly Cohort Retention
Author: [Name]
Date: [Date]
Data Source: events table, users table
Last Validated: [Date] -- results matched dashboard within 2%
Purpose:
Calculate monthly user retention cohorts based on first activity date.
Assumptions:
- "Active" means at least one event in the month
- Excludes test/internal accounts (user_type != 'internal')
- Uses UTC dates throughout
Output:
Cohort retention matrix with cohort_month rows and months_since_signup columns.
Values are retention rates (0-100%).
"""
```
## Version Control for Analyses
- Save queries and code in version control (git) or a shared docs system
- Note the date of the data snapshot used
- If an analysis is re-run with updated data, document what changed and why
- Link to prior versions of recurring analyses for trend comparisonRelated Skills
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