lamm-mit/scientific-report-pdf
skills/scientific-report-pdf/SKILL.md
Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
$ install --global
skillsauthnpx skillsauth add lamm-mit/scienceclaw scientific-report-pdfInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- scientific-report-pdf
- description:
- Generate a structured scientific PDF report from a JSON description. Accepts a JSON file specifying title, authors, abstract, sections (headings, text, tables, figures), and inline data panels (heatmap, bar, scatter, line). Produces a publication-style A4 PDF using reportlab with no LaTeX dependency. All figures are either loaded from PNG paths or generated on-the-fly from inline data.
- domain:
- visualization
- dependencies:
- reportlab, matplotlib, pillow
scientific-report-pdf
Generates a structured scientific PDF report from a JSON input file. No LaTeX or pandoc required — uses reportlab for pure-Python PDF rendering.
Usage
python3 skills/scientific-report-pdf/scripts/scientific_report_pdf.py --input-json report.json
python3 skills/scientific-report-pdf/scripts/scientific_report_pdf.py --input-json report.json --output-dir /tmp/
python3 skills/scientific-report-pdf/scripts/scientific_report_pdf.py --describe-schema
Input JSON Structure
{
"title": "The Sound of Molecules",
"authors": ["ReportAgent", "MusicAnalyst"],
"subtitle": "CS1 Investigation | LAMM Research Platform",
"abstract": "We present ...",
"sections": [
{"type": "heading", "level": 1, "text": "1. Introduction"},
{"type": "text", "text": "Sonification has been applied to ..."},
{
"type": "table",
"label": "Table 1",
"caption": "RDKit descriptors for 16 compounds.",
"headers": ["Compound", "MW", "LogP"],
"rows": [["aspirin", "180.2", "1.19"], ["ibuprofen", "206.3", "3.72"]]
},
{
"type": "figure",
"label": "Figure 1",
"caption": "Era-match heatmap.",
"path": "/path/to/era_match.png"
},
{
"type": "panel",
"label": "Figure 2",
"caption": "Mean similarity by drug class.",
"panel_type": "bar",
"figsize": [10, 5],
"data": {
"categories": ["NSAID", "Opioid", "Stimulant"],
"series": [{"name": "Bach", "values": [0.4, 0.7, 0.3], "color": "#c0392b"}],
"xlabel": "Drug class",
"ylabel": "Mean cosine similarity",
"title": "Harmonic Affinity by Drug Class"
}
},
{"type": "pagebreak"},
{
"type": "panel",
"label": "Figure 3",
"caption": "Cosine similarity heatmap.",
"panel_type": "heatmap",
"data": {
"values": [[0.8, 0.3], [0.2, 0.9]],
"row_labels": ["aspirin", "fentanyl"],
"col_labels": ["Bach", "Beethoven"],
"cmap": "YlOrRd",
"annotate": true
}
}
],
"metadata": {
"investigation_id": "cs1_sound_of_molecules",
"platform": "LAMM Infinite",
"agents": ["SoundAgent1", "MusicAnalyst", "ReportAgent"]
}
}
Section Types
| type | Required fields | Description |
|------|----------------|-------------|
| heading | level (1-3), text | Section heading |
| text | text | Paragraph body |
| table | headers, rows | Data table with optional label, caption, highlight_col |
| figure | path | Embed existing PNG/JPG |
| panel | panel_type, data | Auto-generate matplotlib figure |
| pagebreak | — | Force page break |
| hr | — | Horizontal rule |
Panel Types
| panel_type | Required data fields |
|-----------|---------------------|
| heatmap | values (2D array), row_labels, col_labels |
| matrix | same as heatmap |
| bar | categories, series (list of {name, values, color}) |
| grouped_bar | same as bar |
| scatter | x, y |
| line | x, y |
Output
{
"pdf_path": "/tmp/The_Sound_of_Molecules_20260403_001234.pdf",
"n_pages": 8,
"n_figures": 4,
"size_kb": 512
}
Dependencies
reportlab— PDF generationmatplotlib— auto-generated panel figurespillow— RGBA→RGB image conversionpypdf(optional) — page count in output
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