scientific-report-pdf
Generate a structured scientific PDF report from a JSON description.
Skill by lamm-mit
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
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About
Use cases: documents, data, research
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/scientific-report-pdf
What's inside
- Skill 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.
- License
- Apache-2.0
- Metadata
- domain: visualization · dependencies: reportlab, matplotlib, pillow
- Instructions
- 115 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (2)
- SKILL.md
- scientific_report_pdf.py · scripts/
SKILL.md (first lines)
# 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
```bash
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
```json
{
"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
```json
{
"pdf_path": "/tmp/The_Sound_of_Molecules_20260403_001234.pdf",
"n_pages": 8,
"n_figures": 4,
"size_kb": 512
}
```
## Dependencies
- `reportlab` — PDF generation
- `matplotlib` — auto-generated panel figures
- `pillow` — RGBA→RGB image conversion
- `pypdf` (optional) — page count in outputFrom lamm-mit/scienceclaw, Apache-2.0.
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Signals
- Stars
- 250
- Forks
- 47
- Open issues
- 1
- Last commit (this folder)
- Apr 7, 2026 (6 months ago)
- Last release
- Jun 23, 2026 (4 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/lamm-mit/scienceclaw
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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scientific-report-pdf skill. Agent Mart. https://agentmart.dev/skills/lamm-mit-scientific-report-pdf (last updated 2026-10-24)