today-paper-summary
Retrieves papers the user browsed today, downloads PDFs, generates summaries, and returns an enriched list. Use when the user asks what papers they read today, wants a summary of today's papers, or asks about their recent reading activity.
Best use case
today-paper-summary is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Retrieves papers the user browsed today, downloads PDFs, generates summaries, and returns an enriched list. Use when the user asks what papers they read today, wants a summary of today's papers, or asks about their recent reading activity.
Teams using today-paper-summary should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/today-paper-summary/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How today-paper-summary Compares
| Feature / Agent | today-paper-summary | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Retrieves papers the user browsed today, downloads PDFs, generates summaries, and returns an enriched list. Use when the user asks what papers they read today, wants a summary of today's papers, or asks about their recent reading activity.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# today-paper-summary
Collect academic papers visited in the browser today, download their PDFs, summarize each one, and return structured results.
## Workflow
### Step 1: Fetch today's paper URLs
```python
from browser_history import collect_today_paper_urls
records = collect_today_paper_urls()
```
`records` is a `List[Dict]` with fields: `browser`, `db_path`, `url`, `title`, `visit_count`, `last_visit_time`.
### Step 2: Download PDFs
- Save directory: `<working_dir>/<today's date>/`, e.g. `2026-03-11/`
- Filename: use the paper ID or a slug of the title, e.g. `2603.09973.pdf`
- If `url` already points to a PDF, use it directly; if it's an abstract page, construct the PDF URL (for arXiv, replace `/abs/` with `/pdf/`)
- Use `requests` with a 30s timeout; on failure, record `download_error` and continue
```python
import requests
from pathlib import Path
from datetime import date
save_dir = Path(date.today().isoformat())
save_dir.mkdir(exist_ok=True)
def get_pdf_url(url: str) -> str:
"""Convert an abstract URL to a PDF URL (arXiv-specific)."""
return url.replace("/abs/", "/pdf/")
def download_pdf(pdf_url: str, dest: Path) -> bool:
try:
r = requests.get(pdf_url, timeout=30, headers={"User-Agent": "Mozilla/5.0"})
if r.status_code == 200:
dest.write_bytes(r.content)
return True
except Exception:
pass
return False
```
### Step 3: Extract text and summarize
- Use `pypdf` to read the PDF — extract the first 5 pages (sufficient to cover the abstract and introduction)
- Use LLM with the prompt below, asking for a structured summary
```python
import pypdf
def extract_text(pdf_path: Path, max_pages: int = 5) -> str:
reader = pypdf.PdfReader(str(pdf_path))
return "\n".join(p.extract_text() or "" for p in reader.pages[:max_pages])
```
Summary prompt template:
```
Summarize the following paper in concise English using this format:
[Core contribution] One sentence (≤ 50 words)
[Method highlights]
- ...
[Key findings]
- ...
Paper content:
{text}
```
### Step 4: Build the enriched list
```python
results = []
for r in records:
results.append({
"browser": r["browser"],
"last_visit_time": r["last_visit_time"],
"title": r["title"],
"url": r["url"],
"summary": "<summary from Step 3, or 'unavailable' on failure>",
})
```
Return `results`.
## Notes
- Install dependencies if needed: `pip install pypdf requests`
- Some publisher PDFs require institutional access; the download returns a login page instead of the actual PDF — set `summary` to `"PDF requires authorized access"` in that case
- arXiv PDFs are openly accessible and require no login
- Keep downloaded PDFs in the date folder for the user to reviewRelated Skills
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