Scholar Research

Search, analyze, and summarize peer-reviewed academic papers from open access sources. Provides credibility scoring, visualization, timeline generation, and...

Security Scan

The skill's code, documentation, and runtime instructions match its stated purpose (searching, scoring, summarizing, and extracting figures from open-access papers); it requests no secrets and has no installer, though a few non-security issues are noted (hardcoded test path, reliance on system PDF tools).

Purpose & Capability

Name/description (search, score, summarize, extract figures) align with the included modules: search.py, score.py, summarize.py, figure_extract.py and a PDF downloader. Optional API tokens are present in config.json for services the skill documents (OpenAlex, Semantic Scholar, CrossRef) and are not required by default.

Instruction Scope

SKILL.md instructs the agent to search, fetch metadata/PDFs, score, and extract figures — this is exactly what the code does. The code performs network calls to many external public APIs and downloads PDFs (requests). Figure extraction attempts to call system binaries (pdftotext, pdfimages) via subprocess. A test file (test_runner.py) contains a hardcoded absolute chdir to '/home/bigclaw/.openclaw/…' which is environment-specific and could cause unintended filesystem access if executed; this is a development/test artifact and not necessary for normal skill use.

Install Mechanism

No install spec is provided (instruction-only install), so nothing will be silently downloaded at install time. The package includes Python source and lists Python dependencies (requests, beautifulsoup4, PyPDF2/opencv/transformers mentioned in SKILL.md), and the figure extraction relies on external system utilities (Poppler's pdftotext/pdfimages) if available. That reliance should be documented to avoid surprises but is proportionate to the stated feature set.

Credentials

The skill does not declare required environment variables or a primary credential. config.json includes optional API tokens/email fields for OpenAlex, Semantic Scholar, and CrossRef (reasonable and documented). There are no requests for unrelated credentials or secrets in the files.

Persistence & Privilege

The skill does not request persistent global privileges (always:false). It does not modify other skills or system-wide agent settings. It operates as a normal user-space tool that downloads content into local directories when asked.

Assessment

This repository appears coherent with its description: it searches public academic APIs, downloads PDFs, scores and summarizes papers, and extracts figures using optional system tools. Before installing or running it, consider: 1) Run in a sandbox or VM since it performs network requests and writes downloaded PDFs to disk. 2) Figure extraction uses pdftotext/pdfimages (Poppler) via subprocess; install those if you want full functionality or disable figure extraction. 3) test_runner.py contains a hardcoded chdir to '/home/bigclaw/.openclaw/…' — do not run that file as-is (it's a development/test artifact). 4) Provide API tokens/email only for services you trust and avoid putting sensitive credentials in config files you share. 5) The package has minor packaging/path issues (CLI entry point and imports) that are engineering issues, not security problems. If you want higher assurance, request provenance (homepage/source repo) from the publisher or run the code in an isolated environment and audit network behavior during a sample run.

Scholar Research Skill

Search and analyze academic papers from open access sources with credibility scoring and detailed summaries.

When to Use

• User wants to find papers on a specific topic
• User needs credibility assessment of papers
• User wants summarized research with methodology
• User wants to track field evolution over time
• User needs figures/tables extracted from top papers

Data Sources (Free/Open Access)

The skill searches across these sources:

• arXiv - Pre-prints (Physics, Math, CS, q-bio, q-fin)
• PubMed/PMC - Biomedical & Life sciences
• DOAJ - Peer-reviewed OA journals (all disciplines)
• OpenAlex - 250M+ papers metadata
• CORE - Largest OA full-text aggregator
• Semantic Scholar - Limited free tier
• Unpaywall - Finds free versions of paywalled papers
• CrossRef - All DOI metadata
• bioRxiv - Biology pre-prints
• medRxiv - Medicine pre-prints
• Zenodo - EU research data/papers
• HAL - French OA repository
• J-STAGE - Japanese OA repository
• SSRN - Economics, Law pre-prints

User-Added Sources

Users can add custom sources via config:

{
  "custom_sources": [
    {"name": "My University", "url": "https://repo.my.edu", "api": "..."}
  ]
}

Scoring System

Default Weights (Total: 100 + 40 bonus)

Paper Quality (100 points):

Factor	Weight	Description
citation_count	15%	Times cited by other papers
publication_recency	10%	Newer = more relevant
author_reputation	12%	Combined h-index of authors
journal_impact	12%	Impact factor, CiteScore
peer_review_status	10%	Peer-reviewed vs pre-print
open_access	8%	Free to read/download
retraction_status	10%	Not retracted
author_network	8%	Connected to established network
funder_acknowledgment	5%	Clear funding sources
reproducibility	5%	Code/data available

Bonus Points (up to +40):

• Author Trust: +20 max
• Journal Reputation: +20 max

Customizing Weights

Users can modify weights in config:

{
  "scoring": {
    "citation_count": 25,
    "publication_recency": 5
  }
}

Or use preset profiles: "strict", "recent_only", "balanced"

Output Format

Top Papers (default: 5, user-configurable)

[1] Paper Title (Year)
    Score: 95/100 | Citations: 234
    📄 PDF | 📊 Figures | 🔬 SI
    
    Summary: [One paragraph]
    
    Methodology: [Detailed breakdown]

Field Timeline

📈 FIELD TIMELINE (N papers)

2024: ████████████████████ 15 papers
       → Major: [Breakthrough 1]
       → Trend: [Trend 1]

2023: ████████████████ 12 papers
       → Major: [Breakthrough 2]

Credibility Distribution

📊 Credibility Distribution

Score 90-100: ██ (5) ★ Top
Score 70-89:  ████████ (15)
Score 50-69:  ██████████████████ (25)
Score 30-49:  ██████████ (10)
Score 0-29:   ██ (2)

[████████████░░░░░░░░░] Average: 58/100

Workflow

1. Search: Query across all enabled sources
2. Fetch: Download metadata + PDFs
3. Score: Calculate credibility scores
4. Sort: Rank by score + relevance
5. Present: Top N papers + timeline
6. Extract: Figures from top-scored papers (optional)

Usage Examples

Find papers on: machine learning
Fields: computer science, AI
Top papers: 5
Extract figures: true

Find papers on: quantum computing
Fields: physics
Top papers: 10
Extract figures: false

Dependencies

• Python 3.8+
• requests (API calls)
• beautifulsoup4 (parsing)
• pypdf2 (PDF extraction)
• opencv-python (figure detection)
• transformers (summarization)
• matplotlib (visualization)

Configuration

See config.json for:

• API keys
• Source enable/disable
• Scoring weights
• Display preferences
• Custom sources

Notes

• Always prioritize open access sources
• Cite sources in responses
• Warn about pre-print limitations
• Check retraction status when available
• Respect rate limits