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name: analysis-report description: Write data analysis reports where all quantitative information appears in programmatically-generated plots, never in hand-written text tables. Prevents AI from fabricating numbers by ensuring all values come from computed data rendered visually. Use when creating analysis reports, generating summary statistics, or presenting correlation/comparison results.

Analysis Report Writing

Rules for AI-generated analysis reports that prevent number fabrication.

The Problem

When generating analysis reports, AI can:

1. Make up numbers instead of computing them from data
2. Write tables with fabricated values that don't match actual analysis
3. Make interpretive claims not grounded in computed results

The Solution: Plots Only, No Text Tables

Core Principle: If it's quantitative, it must be in a plot.

All quantitative information must be rendered as plots generated directly from data.

Why This Works

1. Plots are generated programmatically - plotting code reads from data files
2. No hand-written numbers - eliminates fabrication
3. Source of truth is the data - not AI "memory" or guesses

Report Structure Rules

DO:

• Use headings and prose to explain what a plot shows
• Reference plots with ![Caption](plots/filename.png)
• Keep interpretive text minimal and qualitative
• Let the plots speak with their embedded values

DON'T:

• Write tables with numbers (use bar charts instead)
• Quote specific correlation values in prose
• Make quantitative claims not visible in a plot
• Summarize plot data in text form

Example - BAD:

The correlation matrix shows:
| A-B | 0.93 |
| A-C | 0.55 |

Example - GOOD:

The correlation matrix:
![Pairwise Correlations](plots/biomarker_pairwise.png)

Plot Design Guidelines

Each plot should be self-documenting:

1. Include values on the plot - bar labels, coefficients in titles
2. Add context lines - reference lines (y=0, y=1.0, thresholds)
3. Use color coding - positive/negative, above/below threshold
4. Show sample size - n= in titles or labels
5. Add stats boxes - mean, std, n for distributions

Implementation Pattern

analysis.py
  → reads from data source (cached)
  → computes statistics
  → saves to results/*.csv
  → generates plots/*.png from the data
  → report.md references only the plots

File Structure

analysis/{name}/
├── analysis.py          # Main script - generates everything
├── scripts/             # Modular functions
│   ├── __init__.py
│   ├── data.py          # Data fetching
│   ├── compute.py       # Calculations
│   └── plotting.py      # All plot functions
├── results/             # CSV outputs
├── plots/               # PNG outputs
└── report.md            # References plots only

Verification Checklist

Before finalizing a report:

• Run the analysis script to regenerate all plots
• Verify report contains ONLY plot references, no text tables
• Check that all claims in prose are visible in referenced plots
• Confirm no specific numbers are written in prose

When to Use This Skill

Use when:

• Creating data analysis reports
• Generating summary statistics
• Presenting correlation or comparison results
• Building dashboards or visualizations from data

Key trigger phrases:

• "write an analysis report"
• "summarize these results"
• "create a report from this data"
• "show the correlations between..."