Reporting And Figure Export

Reporting And Figure Export

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially matplotlib and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for packaging analysis outputs into reproducible reports, clean tables, and publication-ready figure exports.

When To Use This Skill

• use when the task is to turn analysis outputs into a clear report or deliverable
• use when plots and tables must be exported in publication- or presentation-ready form
• use when a notebook, Quarto report, or figure bundle is needed

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• analysis tables
• plots
• metadata
• interpretation notes

Expected Outputs

• QC or analysis reports
• exported figures
• deliverable-ready tables

Preferred Tools

• matplotlib
• seaborn
• Quarto-style or notebook reporting
• pandas

Starter Pattern

Preferred starting point: matplotlib
Inputs: analysis tables, plots, metadata, interpretation notes
Outputs: QC or analysis reports, exported figures, deliverable-ready tables

Workflow

1. Separate raw outputs from presentation outputs

Keep the analysis artifacts intact and build curated exports on top.

2. Standardize figure formatting

Use consistent fonts, labels, color legends, and vector output when possible.

3. Assemble compact reports

Summarize key methods, QC, main findings, and file provenance.

4. Export tables cleanly

Write machine-readable and human-readable result tables with stable column names.

5. Preserve reproducibility

Record how the report and figures were generated.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• QC or analysis reports
• exported figures
• deliverable-ready tables

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Validate file structure and metadata before handing outputs to downstream tools.
• Retain provenance for every conversion, query, or pipeline execution step.

Anti-Patterns

• copy-pasting plots without recording how they were made
• using unreadable legends, tiny fonts, or raster-only outputs when vector is possible
• mixing exploratory and final figures without labeling them

Related Skills

• Sequence And Format IO
• Alignment And Mapping
• Read QC
• Database Access

Optional Supplements

• seaborn
• plotly