astronomy-paper-summarize turns astronomy and astrophysics papers into structured Markdown and PDF reports. It uses eight role-specific agents for intake, rapid triage, technical reading, critical evaluation, research connection, and report assembly.
The plugin works with GitHub Copilot CLI, Codex, Claude Code, and other agent platforms that can load SKILL.md skills.
Choose one of three output modes:
| Mode | Best for | Report contents |
|---|---|---|
| Rough | Screening papers quickly | Research question, methods, 1 to 3 findings, relevance score, project implications, and a short takeaway |
| Deep | Studying a paper closely | Structured summary, methodology, critical evaluation, research connections, glossary, useful references, and possible next steps |
| Both | Keeping a quick reference and a full analysis | Produces both reports in the same run |
The analysis can use your research topic, goal, and planned methodology as a lens. This makes relevance scores and research connections specific to your project instead of generic to the paper's field.
You need a supported agent platform and a local copy of the paper as PDF, plain text, Markdown, or pasted text.
For PDF input and output, install:
pdftotextfrom Poppler to extract text from PDFs.pandocto create PDF reports.- A Pandoc-compatible PDF engine. The default command uses XeLaTeX with the FreeSerif font. The plugin can also try WeasyPrint or wkhtmltopdf.
If PDF conversion is unavailable, the plugin still keeps the Markdown report.
A NASA ADS API token is optional. It provides richer publication metadata and citation counts. Set it before starting your agent platform:
export ADS_API_TOKEN="your-token-here"Without a token, the plugin falls back to the arXiv API and then to metadata extracted from the paper.
Register this repository as a marketplace, then install the plugin:
copilot plugin marketplace add zzyu17/astronomy-paper-summarize
copilot plugin install astronomy-paper-summarize@astronomy-paper-summarizeInside an interactive GitHub Copilot CLI session, you can use:
/plugin marketplace add zzyu17/astronomy-paper-summarize
/plugin install astronomy-paper-summarize@astronomy-paper-summarize
Then run /restart or start a new session with /clear.
Register the marketplace and install the plugin:
codex plugin marketplace add zzyu17/astronomy-paper-summarize
codex plugin add astronomy-paper-summarize@astronomy-paper-summarizeStart a new Codex session after installation so the plugin is discovered.
Register the marketplace and install the plugin:
claude plugin marketplace add zzyu17/astronomy-paper-summarize
claude plugin install astronomy-paper-summarize@astronomy-paper-summarizeInside an interactive Claude Code session, you can use:
/plugin marketplace add zzyu17/astronomy-paper-summarize
/plugin install astronomy-paper-summarize@astronomy-paper-summarize
/reload-plugins
The canonical skill is in skills/astronomy-paper-summarize. To use it with another platform:
-
Clone this repository:
git clone https://github.com/zzyu17/astronomy-paper-summarize.git
-
Add the repository's
skillsdirectory to the platform's skill search path, or copy the entireskills/astronomy-paper-summarizedirectory into the platform's user or project skill directory. Keep itsagentsandreferencessubdirectories withSKILL.md. -
Start a new session and ask the platform to summarize an astronomy paper. If the platform has no subagent support, select inline execution when prompted.
Exact skill directories and reload behavior vary by platform.
Give the agent a local paper path and say what kind of summary you want:
Summarize /home/me/papers/example-paper.pdf.
Give me a rough overview of C:\Users\me\papers\example-paper.pdf.
Create both a rough overview and a deep summary for this astronomy paper.
On the first run for a research project, the intake step asks for:
- Your core research topic.
- Your primary research goal.
- Your proposed methodology.
- Rough, deep, or both output.
- Subagent-driven or inline execution.
Subagent-driven execution is the default and is better suited to long papers. Inline execution runs the same workflow sequentially in the current session and works on platforms without subagent support.
Markdown reports go into paper-summaries/ inside the paper's directory. PDFs go beside the original paper:
paper-directory/
├── original-paper.pdf
├── Paper Title-rough-overview.pdf
├── Paper Title-deep-summary.pdf
└── paper-summaries/
├── Paper Title-rough-overview.md
└── Paper Title-deep-summary.md
Only files for the selected mode are created. The plugin removes its temporary staging directory after it assembles the reports.
The plugin stores shared project settings in:
<parent-of-paper-directory>/.astro-paper/config.yaml
Paper directories under the same parent share this configuration. A typical file looks like:
research_background:
core_topic: "Planets in the Neptunian desert"
primary_goal: "Understand their formation and evolution"
proposed_methodology: "Statistical analysis of TESS planet populations"
output:
pdf_converter_rough: ""
pdf_converter_deep: ""After a successful PDF conversion, the plugin saves the latest working conversion command in the corresponding pdf_converter field. You can edit the research background or converter settings between runs.
Check that the marketplace was registered before the plugin was installed. Then restart the platform or reload its plugins. For a generic installation, confirm that the platform can see the complete skills/astronomy-paper-summarize directory.
Confirm that pdftotext is installed and available on PATH. Scanned PDFs usually need OCR first; alternatively, provide a text or Markdown copy of the paper.
The Markdown report should still be available in paper-summaries/. Check that pandoc and the configured PDF engine are installed. You can change the converter command in .astro-paper/config.yaml and run it manually from the paper directory.
Check that ADS_API_TOKEN is visible in the environment where the agent platform was started. The workflow can continue without ADS by using arXiv or metadata from the paper itself.
Choose inline execution. It uses the same agent instructions in sequence within the current session.
This project is licensed under CC BY-NC 4.0.