AI Paper Digest

Use this skill to produce a compact, source-aware AI paper briefing first, then continue into deeper paper analysis on demand.

Quick Start

• For a daily digest, run scripts/fetch_ai_digest.py to collect recent papers from arXiv, OpenAlex journals, and selected paper-discovery feeds.
• Use references/source-priority.md to decide which venues and sites to prioritize for the user's request.
• Return a short Chinese introduction for each paper:
  1. what the paper studies
  2. the core idea or method at a high level
  3. why it may matter
• When the user points to one paper for deeper analysis, switch from digest mode to paper-analysis mode and work from the paper abstract, official page, PDF, or local file.

Workflow

1. Decide the task mode

• Use daily-digest when the user wants today's or recent papers across sources.
• Use venue-focused when the user cares about conferences or journals such as ICML, NeurIPS, ICLR, CVPR, COLT, TPAMI, or TMLR.
• Use single-paper-analysis when the user asks about one named paper, one link, one PDF, or a follow-up such as "这篇论文的方法是什么".

2. Build the source set

• Default priority order:
  1. arXiv daily preprints for freshness
  2. Hugging Face Papers for community-visible daily papers
  3. OpenAlex journal results for recent journal articles
  4. Official proceedings or venue pages for venue-specific retrieval
  5. Secondary discovery sites such as Aminer, Paper Copilot, Bohrium for navigation and triangulation
• Treat the user's preferred venues as a ranking hint, not a reason to ignore fresher relevant papers from other sources.
• Prefer official or primary sources when available. Use discovery sites to locate papers faster, then link back to the paper page, proceedings page, arXiv page, DOI page, or PDF.

3. Produce the digest

• Group or tag papers by venue family where possible: core ML, theory, vision, broad AI, data mining, journals, or community picks.
• Keep each paper entry compact unless the user asks for depth.
• Include:
  • title
  • source and venue guess
  • date or recency basis
  • paper link
  • 2-4 sentence Chinese introduction
• If "today" is ambiguous because a source updates weekly or irregularly, say the exact time basis, for example "近 24 小时 arXiv" or "近 7 天期刊文章".

4. Continue into single-paper analysis

• For a follow-up on one paper, gather the best available source in this order:
  1. local PDF or local note from the user
  2. official abstract or proceedings page
  3. arXiv abstract or PDF
  4. publisher abstract page
• If a local PDF is provided, use $grounded-file-qa for evidence-grounded reading and page-aware follow-ups.
• For method analysis, structure the answer with:
  1. problem setting
  2. proposed method
  3. model or algorithm components
  4. training or optimization strategy
  5. experiments and findings
  6. limitations or assumptions
• Separate direct evidence from inference. If the method cannot be confirmed from the abstract alone, say that and request or fetch the PDF before going deeper.

Venue Coverage

Use the venue registry in references/source-priority.md. The main target set includes:

• Core ML: ICML, NeurIPS, ICLR
• Theory: COLT, AISTATS
• Vision: CVPR, ICCV, ECCV
• Broad AI: AAAI, IJCAI
• Data mining: KDD, ICDM
• Journals: IEEE TPAMI, IEEE TKDE, JMLR, Machine Learning, Artificial Intelligence, TMLR
• Additional sources: arXiv, Hugging Face Papers, Aminer, Paper Copilot, Bohrium, Hugging Face Blog

Scripts

scripts/fetch_ai_digest.py

• Use this script for a reusable, dependency-light first pass over recent AI papers.
• It fetches:
  • recent arXiv AI/ML categories
  • recent OpenAlex journal articles filtered for AI-related topics
  • Hugging Face Papers page headlines
• It ranks results with simple heuristics and prints JSON, so the result can be reformatted into markdown or fed into later analysis steps.

Example:

python3 skills/ai-paper-digest/scripts/fetch_ai_digest.py --days 3 --limit 20

When to skip the script

• Skip the script if the user already provided a specific paper or PDF.
• Skip the script if the task is only to explain one known paper.
• Skip the script if the environment blocks network and no cached or local sources are available.

Response Style

• Answer in Chinese unless the user asks otherwise.
• For digest entries, optimize for scanability and avoid long abstracts pasted verbatim.
• For deeper paper analysis, use clear headings and concise technical language.
• Do not overclaim novelty or results if only titles or abstracts are available.

Failure Handling

• If a source is temporarily unavailable, say which source failed and continue with the remaining sources.
• If a venue page is difficult to scrape, fall back to discovery sites or search, then verify the target paper page before summarizing.
• If the user asks for "today's hottest" papers, explain the ranking basis because there is no single authoritative AI-wide hotness metric.

Prompt Patterns

• Use $ai-paper-digest to give me today's AI papers with short Chinese intros.
• Use $ai-paper-digest to focus on ICLR, NeurIPS, ICML, and arXiv papers from the last 3 days.
• Use $ai-paper-digest to find recent CVPR and ICCV related papers and tell me which ones are worth reading first.
• Use $ai-paper-digest to summarize this paper first, then explain its method and experiments.