Paper

/paper — Academic Paper Workflow

Default Config

Edit this section to customize for your project.

output_dir: "./papers"
# Obsidian vault users: set paper_output_dir in your project's CLAUDE.md
# Drive/OneDrive: use the appropriate MCP, then set path (e.g. "gdrive://My Drive/Papers")

venues: [CHI, ACL, EMNLP, NAACL, NeurIPS, ICML, ICLR, EDM, LAK, AIED, ITS, CSCW, SIGIR, CIKM]

keywords:
  - cognitive load
  - adaptive learning
  - intelligent tutoring
  - conversational learning
  - LLM tutoring
  - learning analytics
  - behavioral signals
  - educational dialogue

min_citations: 5      # /paper find: filter out papers with fewer citations
daily_max: 10         # /paper digest: max papers per run

annotation_lang: zh   # zh = Chinese annotations | en = English; override with --lang

openalex_email: ""    # Optional. Add email to join OpenAlex Polite Pool (higher rate limits).
semantic_scholar_api_key: ""  # Optional. Free key: semanticscholar.org/product/api

Override any config value in your project's CLAUDE.md using keys: paper_output_dir, paper_venues, paper_keywords, paper_annotation_lang.

Subcommands

Flags:

• --context "ML课第3周" — override project context
• --questions "Q1:... Q2:..." — switch to Question mode (Mode A)
• --questions — interactive question mode (Claude asks you)
• --output /path/ — override output directory for this run
• --top N — return N results (default: 5 for find, daily_max for digest); also accepts bare number after query
• --lang en — override annotation language for this run
• --source arxiv — search only arxiv (latest preprints, no citation filter)
• --source venues — search only papers from config venues list (citation-weighted ranking)
• --local /path/ — read local PDFs from folder (no API calls); or --local a.pdf b.pdf for specific files

Flags can be written with or without --. Claude accepts natural language equivalents (e.g. top 5, source venues, local /path/, questions "...").

Context Resolution (in priority order)

1. --context flag in command
2. Current directory's CLAUDE.md (auto-loaded by Claude Code)
3. No context → use Mode B directly (zero-config, logic analysis mode)

Data Sources

Primary: OpenAlex

Free, no API key required, 10 req/s rate limit.

https://api.openalex.org/works?search={query}
  &select=title,authorships,publication_year,cited_by_count,primary_location,doi
  &per-page=20
  [&mailto={openalex_email}  ← add if configured]

After fetching: immediately extract and keep only — title, first author, year, cited_by_count, venue name (primary_location.source.display_name), DOI. Discard all other fields from the response before further processing.

Secondary: Semantic Scholar (only if semantic_scholar_api_key is set)

https://api.semanticscholar.org/graph/v1/paper/search?query={topic}
  &fields=title,authors,year,citationCount,venue,externalIds&limit=20

Header: x-api-key: {semantic_scholar_api_key}

Tertiary: arxiv (fallback + digest source)

https://export.arxiv.org/api/query?search_query=(cat:cs.CL+OR+cat:cs.HC+OR+cat:cs.AI+OR+cat:cs.LG)
  +AND+({keywords})&sortBy=submittedDate&sortOrder=descending&max_results=30

Note: arxiv papers have no citation count. Label as preprint.

Rate Limit Handling

If a source returns 429: check Retry-After header → wait that many seconds. If no header: wait 2s → 5s → 10s (3 retries). After 3 failures → skip source, move to next in priority. Note which source was skipped in output.

Subcommand: find

1. Parse flags: topic query, --top N (default: 5), --lang, --source (default: mixed), --local (path or file list)

   • --top N also accepts a bare number immediately after the query string (e.g. /paper find "query" 10 → top 10)

2. Resolve source strategy:

   --source / flag	API calls	Ranking formula
   (default, mixed)	Single OpenAlex call sort=cited_by_count:desc; arxiv fallback if <3 results	0.5 × relevance_rank + 0.3 × log(cited_by_count+1) + 0.2 × recency_score
   arxiv	arxiv API only (sortBy=submittedDate); no min_citations filter	0.5 × relevance_rank + 0.5 × recency_score
   venues	Single OpenAlex call; keep only papers where venue name matches any entry in config venues list (case-insensitive partial match on primary_location.source.display_name)	0.5 × relevance_rank + 0.5 × log(cited_by_count+1)
   --local /path/ or --local a.pdf b.pdf	No API calls — read local PDFs only	Take first --top N files (alphabetical order)

   --local and --source are mutually exclusive. If both are given, return an error.

3. If --local flag present — execute local PDF steps instead of steps 3–6:

   1. Scan the given folder for all .pdf files, or use the explicitly listed files directly
   2. If file count > --top N, take first N (alphabetical order)
   3. For each PDF, extract: title, authors, year, abstract (first 300 words), conclusion paragraph (last 300 words)
   4. Generate a Digest Card per PDF (same 4-sentence format: 研究问题 / 核心方法 / 关键发现 / 相关性)
   5. Citation badge: fixed as 📄 Local PDF (no OpenAlex lookup)
   6. Card bottom link: /paper read /absolute/path/to/file.pdf 精读 (use absolute path)
   7. Save to: {output_dir}/FindResults/find-local-YYYY-MM-DD-HHmm-{folder-slug}.html
   8. Print summary and exit — skip steps 4–8 below

3b. Fetch papers (online mode):

• Add &filter=cited_by_count:>{min_citations} for default/venues mode (skip for arxiv)
• After fetch: immediately discard all fields except title, first author, year, cited_by_count, venue name, DOI
• On any source failure → follow Rate Limit Handling

4. Deduplicate against existing files:

   • Check {output_dir}/FindResults/ and {output_dir}/ root for [FirstAuthor-YYYY-slug].html
   • Mark existing papers as [已有], include in summary with their path, skip card generation

5. Rank top N using the formula for the active source strategy

6. Generate Digest HTML — a single self-contained HTML file containing N paper cards.

   Digest Card format (one card per paper):

   [Title] — [First Author] et al., [Year] — [Venue or arXiv]
   [Citation badge]  [Source tag]
   研究问题: [1 sentence — what problem does this paper solve?]
   核心方法: [1 sentence — what approach do they use?]
   关键发现: [1 sentence — what is the main result?]
   相关性:   [1 sentence — why this matters for your research, based on CLAUDE.md context]
   [DOI link if available] · → /paper read <doi_or_path> 精读
   

   • Citation badge uses tier logic from Paper Quality Bar section
   • Annotation language for cards follows annotation_lang config (or --lang flag)
   • HTML structure: simple cards layout, embed references/template.css in <style>

7. Save Digest HTML to: {output_dir}/FindResults/find-YYYY-MM-DD-HHmm-{query-slug}.html

   • Create subdirectory automatically if it doesn't exist

8. Print summary:

   Found N papers via {source}. Digest saved:
   → 30_Research/FindResults/find-2026-03-14-1430-cognitive-load-llm.html
   
   Papers included:
   - Jin-2025-llm-teachable-agent        [⭐ 47 citations · CHI]
   - Cai-2025-intrinsic-load             [📄 preprint · arXiv]
   - Klepsch-2017-two-types-icl          [已有 → 30_Research/FindResults/...]
   

Subcommand: read

1. Read the PDF at provided path
2. Extract: title, authors, year, venue/journal, abstract, full text
3. Resolve mode:
   • No --questions → Mode B (logic analysis)
   • --questions "..." → Mode A (inline). Parse questions using these rules:
     • Strip optional Q1: / Q2: etc. prefixes (they are decorative, not required)
     • Split on: comma , / semicolon ; / newline / Q\d+ pattern boundaries
     • Extract up to 6 questions; if more are given, take first 6 and warn the user
     • Accept any of these formats:

       "Q1: 方法? Q2: 与 baseline 比较?"   ← Q-label + colon
       "方法?, 与 baseline 比较?"            ← comma-separated
       "方法?; 与 baseline 比较?"            ← semicolon-separated
       

   • --questions (no argument) → Mode A (interactive): Output exactly: 请输入你的阅读问题（每行一个，或逗号分隔，最多6个）： Wait for user input, then parse using the same rules above, then proceed with Mode A
4. Resolve annotation language: --lang flag > annotation_lang config
5. Generate full dual-column HTML annotation (see HTML Template section)
   • Paper Quality Bar: omit (no citation data from local PDF unless found in text)
6. Save to {output_dir}/[FirstAuthorLastName-YYYY-keyword].html
   • Write directly to path; do not ls the directory beforehand
7. Output: file path + brief summary of key arguments found

Subcommand: digest

1. Check cache: if {output_dir}/PaperDigests/YYYY-MM-DD-digest.html exists today → skip fetch, output cached path
2. Fetch arxiv: same URL as Data Sources section above
3. Filter: keep top daily_max most relevant to config keywords (semantic match on title+abstract)
4. Deduplicate: skip arxiv IDs seen in previous 7 days' digests
5. Annotate: for each paper, run Mode B annotation (brief version); annotation language follows config
6. Combine: all annotations → single HTML digest file; each paper has a compact Paper Quality Bar
7. Save: {output_dir}/PaperDigests/YYYY-MM-DD-digest.html
8. Update daily note: append to 10_Daily/YYYY-MM-DD.md:

   ## Paper Digest
   → [[30_Research/PaperDigests/YYYY-MM-DD-digest|Today's Paper Digest]] (N papers)
   

Subcommand: cite

1. Parse note name from user input (e.g., [[Klepsch-2017-annotation]] or file path)
2. Find HTML or MD file in {output_dir}/ or vault root
3. Extract metadata: title, authors, year, venue, DOI/URL
4. Output directly (no file saved):
   • APA 7th: Author, A., & Author, B. (Year). Title. *Venue*, *vol*(issue), pages. https://doi.org/...
   • BibTeX:

     @article{key,
       author = {...}, title = {...}, journal = {...}, year = {...}, doi = {...}
     }
     

HTML Annotation Template

Paper Quality Bar (find/digest only — between navbar and section-nav)

Shows: {source_icon} {source} | {venue} | {year} | {citation_badge} | {venue_type_tag}

Citation tiers:

Venue type tag: A* 会议 (top venues: CHI/NeurIPS/ACL/EMNLP/ICML/ICLR/SIGIR/CSCW) | 会议/期刊 (other config venues) | Workshop | Preprint

Source icon: 🔍 OpenAlex | 📚 Semantic Scholar | 📄 arXiv

CSS: see references/template.css (.quality-bar, .badge-*, .qb-* classes).

Mode B: Logic Analysis (Default)

Generate a complete, self-contained HTML file. CSS: embed references/template.css as <style> in <head>. Google Fonts: import Lora + IBM Plex Sans.

5-Color Highlight System:

Document structure:

1. TOP NAVBAR
   - Paper title
   - Context label (or "General Reading" if none)
   - Color legend: 5 colored chips with dimension labels

2. PAPER QUALITY BAR (find/digest only — omit for /paper read)

3. STICKY SECTION NAV
   - Links: Abstract | Introduction | Related Work | Methods | Results | Discussion | Conclusion
   - Highlight active section on scroll

4. DUAL-COLUMN BODY — one <div class="paragraph-group"> per paragraph
   Left column — original text (ALWAYS IN ORIGINAL LANGUAGE — never translate):
     - Copy verbatim from PDF; annotation_lang ONLY controls the right column
     - Highlight key phrases: <mark class="thesis">...</mark> etc.
   Right column — annotation cards (language follows annotation_lang):
     [Colored left border matching paragraph's dominant highlight]
     ① 段落功能 / Paragraph Function：...
     ② 逻辑角色 / Logical Role：...
     ③ 论证技巧或潜在漏洞 / Rhetorical Technique or Logical Gap：...

5. BACK-TO-TOP BUTTON
   <button id="back-to-top" title="返回顶部">↑</button>
   <script>
     const btn = document.getElementById('back-to-top');
     window.addEventListener('scroll', () => { btn.style.display = window.scrollY > 300 ? 'flex' : 'none'; });
     btn.addEventListener('click', () => window.scrollTo({ top: 0, behavior: 'smooth' }));
   </script>

6. BOTTOM — Argument Structure Overview (language follows annotation_lang)
   zh labels: 问题 / 论点 / 证据 / 反驳处理 / 结论
   en labels: Problem / Argument / Evidence / Concession / Conclusion
   - Author's core claim (1 sentence)
   - 最强论证 / Strongest argument
   - 最弱论证 / Weakest argument
   - APA Citation (auto-formatted) + copy button:
     <button class="copy-btn" data-label="复制 APA" '{apa_string}')">复制 APA</button>
   - BibTeX block + copy button:
     <button class="copy-btn" data-label="复制 BibTeX" '{bibtex_string}')">复制 BibTeX</button>

   Add this script alongside the back-to-top script:
   <script>
   function copyText(btn, text) {
     navigator.clipboard.writeText(text);
     btn.textContent = '✓ 已复制'; btn.classList.add('copied');
     setTimeout(() => { btn.textContent = btn.dataset.label; btn.classList.remove('copied'); }, 1500);
   }
   </script>

Mode A: Question-Driven (triggered by --questions)

Same structure as Mode B, with these differences:

• Color legend: per-question colors Q1–Q6 (cycling through a distinct palette)
• Left highlights: <mark class="q1">, <mark class="q2">, etc.
• Right annotation cards: labeled 【Q2 核心论点】 / [Q2 Core Argument]: ① Paragraph Function ② Argument Logic ③ Which question/layer this paragraph answers
• Bottom: Q&A Worksheet (replaces Argument Structure Overview): One card per question — Core argument + Key evidence + Potential counter/limitation

CSS

All CSS is in references/template.css (adjacent to this SKILL.md). When generating any HTML output, embed the file contents as a <style> block in <head>. Only load when generating HTML — /paper cite does not need it.

Error Handling

• PDF unreadable: output error with path, suggest checking permissions
• No results from any source: output "No papers found — try broader keywords"
• OpenAlex 429: retry with backoff (2s→5s→10s); after 3 failures fall back to arxiv
• Semantic Scholar 429: retry with backoff; fall back to OpenAlex/arxiv
• Missing output_dir: create directory automatically before saving
• Malformed --questions: re-prompt user for correct format
• Source fallback notice: always state which source(s) were used, e.g. "⚠️ OpenAlex unavailable — results from arXiv only (no citation counts)"

Notes for Open-Source Use

1. Set output_dir in your project's CLAUDE.md:

   paper_output_dir: 30_Research
   

2. Override keywords/venues/language:

   paper_venues: [CHI, AIED, EDM, LAK]
   paper_keywords: [your, topic, keywords]
   paper_annotation_lang: en
   

3. Optional — OpenAlex Polite Pool (higher rate limits, no account needed):

   openalex_email: yourname@example.com
   

4. Optional — Semantic Scholar secondary source (free key):

   semantic_scholar_api_key: your_key_here
   

Source: sjqsgg/Paperwise — distributed by TomeVault.