zhihaoairobotic/clawphd/research-lit

Research Literature Review

Research topic: $ARGUMENTS

Constants

• PAPER_LIBRARY = paper_library/ — Local directory in the workspace root containing user's paper collection (PDFs). Directory structure: paper_library/{subject}/{date}/, where {subject} is a snake_case topic slug (e.g. diffusion_models, world_models) and {date} is YYYY-MM-DD (the date the paper was saved).
• MAX_LOCAL_PAPERS = 20 — Maximum number of local PDFs to scan (read first 3 pages each). If more are found, prioritize by filename relevance to the topic.
• ARXIV_DOWNLOAD = false — When true, download top 3-5 most relevant arXiv PDFs to PAPER_LIBRARY after search. When false (default), only fetch metadata (title, abstract, authors) via arXiv API — no files are downloaded.
• ARXIV_MAX_DOWNLOAD = 5 — Maximum number of PDFs to download when ARXIV_DOWNLOAD = true.

💡 Overrides:

- /research-lit "topic" — sources: web — only search the web (skip all local)

- /research-lit "topic" — arxiv download: true — download top relevant arXiv PDFs

- /research-lit "topic" — arxiv download: true, max download: 10 — download up to 10 PDFs

Data Sources

This skill checks multiple sources in priority order. All are optional — if a source is not configured or not requested, skip it silently.

Source Selection

Parse $ARGUMENTS for a — sources: directive:

• If `— sources:` is specified: Only search the listed sources (comma-separated). Valid values: local, web, all.
• If not specified: Default to all — search every available source in priority order.

Examples:

/research-lit "diffusion models"                        → all (default)
/research-lit "diffusion models" — sources: all         → all
/research-lit "diffusion models" — sources: local       → local PDFs only
/research-lit "diffusion models" — sources: web         → web search only

Source Table

Graceful degradation: If a source is unavailable, skip it silently and continue with the remaining sources.

Workflow

Step 0: Scan Local Paper Library

Before searching online, check if the user already has relevant papers locally:

1. Locate library: Scan PAPER_LIBRARY for PDF files

`` Glob: paper_library/**/*.pdf ` Papers are organized as paper_library/{subject}/{date}/filename.pdf. When searching for a specific topic, prioritize matching {subject}` directories first, then scan broadly.

1. Filter by relevance: Match filenames and first-page content against the research topic. Skip clearly unrelated papers.

1. Summarize relevant papers: For each relevant local PDF (up to MAX_LOCAL_PAPERS):

• Read first 3 pages (title, abstract, intro)
• Extract: title, authors, year, core contribution, relevance to topic
• Flag papers that are directly related vs tangentially related

1. Build local knowledge base: Compile summaries into a "papers you already have" section. This becomes the starting point — external search fills the gaps.

📚 If no local papers are found, skip to Step 1. If the user has a comprehensive local collection, the external search can be more targeted (focus on what's missing).

Step 1: Search (external)

• Use WebSearch to find recent papers on the topic
• Check arXiv, Semantic Scholar, Google Scholar
• Focus on papers from last 2 years unless studying foundational work
• De-duplicate: Skip papers already found in local library

arXiv API search (always runs, no download by default):

Locate the fetch script and search arXiv directly:

# Try to find arxiv_fetch.py in the project
SCRIPT=$(find tools/ -name "arxiv_fetch.py" 2>/dev/null | head -1)
 
# Search arXiv API for structured results (title, abstract, authors, categories)
python3 "$SCRIPT" search "QUERY" --max 10

If arxiv_fetch.py is not found, fall back to WebSearch for arXiv.

The arXiv API returns structured metadata (title, abstract, full author list, categories, dates) — richer than WebSearch snippets. Merge these results with WebSearch findings and de-duplicate.

Optional PDF download (only when ARXIV_DOWNLOAD = true):

After all sources are searched and papers are ranked by relevance:

# Download top N most relevant arXiv papers
python3 "$SCRIPT" download ARXIV_ID --dir paper_library/{subject}/$(date +%Y-%m-%d)/

• Only download papers ranked in the top ARXIV_MAX_DOWNLOAD by relevance
• Skip papers already in the local library
• 1-second delay between downloads (rate limiting)
• Verify each PDF > 10 KB

Step 2: Analyze Each Paper

For each relevant paper (from all sources), extract:

• Problem: What gap does it address?
• Method: Core technical contribution (1-2 sentences)
• Results: Key numbers/claims
• Relevance: How does it relate to our work?
• Source: Where we found it (local/web) — helps user know what they already have vs what's new

Step 3: Synthesize

• Group papers by approach/theme
• Identify consensus vs disagreements in the field
• Find gaps that our work could fill
• Note connections between papers that the user already has locally vs newly discovered ones

Step 4: Output

Present as a structured literature table:

| Paper | Venue | Method | Key Result | Relevance to Us | Source |
|-------|-------|--------|------------|-----------------|--------|

Plus a narrative summary of the landscape (3-5 paragraphs).

Step 5: Save (if requested)

• Save paper PDFs to paper_library/{subject}/{YYYY-MM-DD}/
• Update related work notes in project memory

Key Rules

• Always include paper citations (authors, year, venue)
• Distinguish between peer-reviewed and preprints
• Be honest about limitations of each paper
• Note if a paper directly competes with or supports our approach
• Never fail because a tool is unavailable — always fall back gracefully to the next data source