| name | auto-paper-collecter |
| description | Personal research-paper radar. Fetches the latest papers and code repos for the user's keyword subscriptions across arXiv, Crossref (IEEE/ACM), Semantic Scholar, GitHub and RSS; deduplicates against history; then YOU (the assistant) expand the queries, filter for computer-science relevance, write Chinese summaries, detect hot sub-fields, and emit a Markdown + HTML digest (optionally emailed). Use when the user asks to "run my paper radar / 文献雷达", "check for new papers", "what's new in my topics", "今天有什么新论文 / 最新文献", "today's research digest", or to configure their keyword subscriptions / sources / schedule. |
auto-paper-collecter (skill)
A self-hosted research-literature radar that runs inside a coding agent.
The Python scripts do only the deterministic work (API fetch, dedup, render,
email). YOU — the assistant running this skill — do all the judgement work:
query expansion, computer-science relevance filtering, Chinese summaries, and
hot-topic synthesis. That means no AI API key is needed — whichever model is
running this skill (Claude in Claude Code, GPT in Codex, …) is the LLM.
Layout
skill/
├── SKILL.md
├── scripts/ common.py · fetch.py · render.py · notify.py (stdlib only)
├── state/ config.json · (queries/candidates/curated/trends/seen .json)
└── digests/ YYYY-MM-DD.md + .html
Run scripts from scripts/: cd skill/scripts && python3 <script>.py
Config — state/config.json
keywords: up to ~3 topic strings to track.domain: the field to constrain relevance to (default computer science).sources: toggle arXiv / Crossref / Semantic Scholar / GitHub / RSS.lookback_days: how far back to fetch (dedup stops repeats anyway).max_per_source, rss_feeds.
When the user asks to change keywords / sources / field, edit this file and
confirm the change back to them.
Optional env vars (never stored in the repo): SEMANTIC_SCHOLAR_KEY (lifts S2
rate limits), GITHUB_TOKEN (lifts GitHub limits), and SMTP_* / EMAIL_TO
for emailing the digest.
The run pipeline — follow IN ORDER
1 · Read config & expand queries (you)
Read state/config.json. For each keyword, think of 2–3 associative
English search queries — synonyms, full forms, adjacent sub-topics — so recall
isn't limited to the literal term (e.g. C2Rust → ["C2Rust", "C-to-Rust translation", "migrating legacy C code to Rust"]). Write them to
state/queries.json as {"<keyword>": ["q1", "q2", ...], ...}.
2 · Fetch candidates (script)
cd skill/scripts && python3 fetch.py
Fetches every enabled source for those queries, drops anything already in
state/seen.json or older than lookback_days, and writes
state/candidates.json. If it reports 0 candidates, tell the user "暂无新文献"
and stop (nothing else to do).
3 · Filter relevance & summarize (you)
Read state/candidates.json. For each item decide: is it (a) computer-science
and (b) genuinely on-topic for its topic keyword? Drop the rest (medical
"translation", finance "AI", random GitHub star-lists, etc.). For every kept
item write a concise Chinese summary and assemble state/curated.json — a
list of objects:
{"source","topic","title","url","venue","authors","published",
"tldr":"一句话核心 (<=60字)","method":"方法简述 (<=80字)",
"contributions":["核心贡献1","核心贡献2"]}
Keep papers first, GitHub repos last (they are a supplementary signal). If a
source gave a tldr already, you may build on it.
4 · Hot-topic synthesis (you, optional but recommended)
Cluster the kept items into a handful of coarse CS sub-fields (自然语言处理 /
计算机视觉 / 系统与编译 …; merge aggressively). Write state/trends.json:
{"top": [{"name","delta": <count>, "summary": "<=80字方向总结", "papers": ["title", ...]}, ... up to 3]}.
5 · Render the digest (script)
cd skill/scripts && python3 render.py
Writes digests/YYYY-MM-DD.md + .html from curated.json (+ trends.json)
and records everything shown into seen.json so it won't repeat.
6 · Notify (script, optional)
cd skill/scripts && python3 notify.py
7 · Report back (you)
Tell the user how many papers were kept, the top hot directions, and the digest
path. Offer to open the HTML or adjust keywords.
Notes
- Scripts are pure Python stdlib — no
pip install required.
fetch.py already filters garbage future dates and de-duplicates across runs.- This skill is the agent-driven counterpart of the project's FastAPI web
dashboard; both share the same sources and pipeline philosophy.
