kwp-data-validate-workflow

This is a workflow skill for the data category.

Sub-Skills

The following skills are available in this workflow:

• rebyteai/kwp-data-data-context-extractor
• rebyteai/kwp-data-data-exploration
• rebyteai/kwp-data-data-validation
• rebyteai/kwp-data-data-visualization
• rebyteai/kwp-data-interactive-dashboard-builder
• rebyteai/kwp-data-sql-queries
• rebyteai/kwp-data-statistical-analysis

Workflow Instructions

/validate - Validate Analysis Before Sharing

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Review an analysis for accuracy, methodology, and potential biases before sharing with stakeholders. Generates a confidence assessment and improvement suggestions.

Usage

/validate <analysis to review>

The analysis can be:

• A document or report in the conversation
• A file (markdown, notebook, spreadsheet)
• SQL queries and their results
• Charts and their underlying data
• A description of methodology and findings

Workflow

1. Review Methodology and Assumptions

Examine:

• Question framing: Is the analysis answering the right question? Could the question be interpreted differently?
• Data selection: Are the right tables/datasets being used? Is the time range appropriate?
• Population definition: Is the analysis population correctly defined? Are there unintended exclusions?
• Metric definitions: Are metrics defined clearly and consistently? Do they match how stakeholders understand them?
• Baseline and comparison: Is the comparison fair? Are time periods, cohort sizes, and contexts comparable?

2. Check for Common Analytical Errors

Systematically review for:

Data completeness:

• Missing data that could skew results (e.g., nulls in key fields, missing time periods)
• Data freshness issues (is the most recent data actually complete or still loading?)
• Survivorship bias (are you only looking at entities that "survived" to the analysis date?)

Statistical issues:

• Simpson's paradox (trend reverses when data is aggregated vs. segmented)
• Correlation presented as causation without supporting evidence
• Small sample sizes leading to unreliable conclusions
• Outliers disproportionately affecting averages (should medians be used instead?)
• Multiple testing / cherry-picking significant results

Aggregation errors:

• Double-counting from improper joins (many-to-many explosions)
• Incorrect denominators in rate calculations
• Mixing granularity levels (e.g., user-level metrics averaged with account-level)
• Revenue recognized vs. billed vs. collected confusion

Time-related issues:

• Seasonality not accounted for in comparisons
• Incomplete periods included in averages (e.g., partial month compared to full months)
• Timezone inconsistencies between data sources
• Look-ahead bias (using future information to explain past events)

Selection and scope:

• Cherry-picked time ranges that favor a particular narrative
• Excluded segments without justification
• Changing definitions mid-analysis

3. Verify Calculations and Aggregations

Where possible, spot-check:

• Recalculate a few key numbers independently
• Verify that subtotals sum to totals
• Check that percentages sum to 100% (or close to it) where expected
• Confirm that YoY/MoM comparisons use the correct base periods
• Validate that filters are applied consistently across all metrics

4. Assess Visualizations

If the analysis includes charts:

• Do axes start at appropriate values (zero for bar charts)?
• Are scales consistent across comparison charts?
• Do chart titles accurately describe what's shown?
• Could the visualization mislead a quick reader?
• Are there truncated axes, inconsistent intervals, or 3D effects that distort perception?

5. Evaluate Narrative and Conclusions

Review whether:

• Conclusions are supported by the data shown
• Alternative explanations are acknowledged
• Uncertainty is communicated appropriately
• Recommendations follow logically from findings
• The level of confidence matches the strength of evidence

6. Suggest Improvements

Provide specific, actionable suggestions:

• Additional analyses that would strengthen the conclusions
• Caveats or limitations that should be noted
• Better visualizations or framings for key points
• Missing context that stakeholders would want

7. Generate Confidence Assessment

Rate the analysis on a 3-level scale:

Ready to share -- Analysis is methodologically sound, calculations verified, caveats noted. Minor suggestions for improvement but nothing blocking.

Share with noted caveats -- Analysis is largely correct but has specific limitations or assumptions that must be communicated to stakeholders. List the required caveats.

Needs revision -- Found specific errors, methodological issues, or missing analyses that should be addressed before sharing. List the required changes with priority order.

Output Format

## Validation Report

### Overall Assessment: [Ready to share | Share with caveats | Needs revision]

### Methodology Review
[Findings about approach, data selection, definitions]

### Issues Found
1. [Severity: High/Medium/Low] [Issue description and impact]
2. ...

### Calculation Spot-Checks
- [Metric]: [Verified / Discrepancy found]
- ...

### Visualization Review
[Any issues with charts or visual presentation]

### Suggested Improvements
1. [Improvement and why it matters]
2. ...

### Required Caveats for Stakeholders
- [Caveat that must be communicated]
- ...

Examples

/validate Review this quarterly revenue analysis before I send it to the exec team: [analysis]

/validate Check my churn analysis -- I'm comparing Q4 churn rates to Q3 but Q4 has a shorter measurement window

/validate Here's a SQL query and its results for our conversion funnel. Does the logic look right? [query + results]

Tips

• Run /validate before any high-stakes presentation or decision
• Even quick analyses benefit from a sanity check -- it takes a minute and can save your credibility
• If the validation finds issues, fix them and re-validate
• Share the validation output alongside your analysis to build stakeholder confidence