hxxra

---

name: hxxra

description: A Research Assistant workflow skill with five core commands: search papers, download PDFs, analyze content, generate reports, and save to Zotero. Entry point is a Python script located at scripts/hxxra.py and invoked via stdin/stdout (OpenClaw integration). The search uses crawlers for Google Scholar and arXiv APIs; download uses Python requests or arXiv API; analyze uses an LLM; report generates Markdown summaries from analysis.json files; save uses Zotero API.

---

# hxxra

This skill is a Research Assistant that helps users search, download, analyze, report, and save research papers.

Recommended Directory Structure

For better organization, it is recommended to create a dedicated workspace for `hxxra` under your OpenClaw working directory:

📁 workspace/                              # OpenClaw current working directory
└── 📁 hxxra/
    ├── 📁 searches/                       # Stores all search result JSON files
        ├── 2025-03-07_neural_radiance_fields_arxiv.json
        ├── 2025-03-07_transformer_architectures_scholar.json
        └── ...
    ├── 📁 papers/                           # Stores downloaded PDF files and per-paper analysis results (each as a subfolder)
        ├── papers_report.md                # Generated Markdown report summarizing all analyzed papers
        ├── 2023_Smith_NeRF_Explained/      # Folder named after the PDF (without extension)
          ├── 2023_Smith_NeRF_Explained.pdf
          ├── analysis.json                 # Structured output from LLM analysis
          └── notes.md                      # (Optional) User-added notes
        ├── 2024_Zhang_Transformer_Survey/
          ├── 2024_Zhang_Transformer_Survey.pdf
          ├── analysis.json
          └── ...
        └── ...
    └── 📁 logs/ # Stores execution logs
        └── hxxra_2025-03-07.log

This structure keeps all related files organized and easily accessible for review and further processing.

Core Commands

1. **hxxra search** - Search for research papers

**Dependencies**: `pip install scholarly`

**Purpose**: Search for papers using Google Scholar and arXiv APIs

**Academic Note**: To account for the distinct characteristics of each data source, the tool adopts a differentiated sorting strategy—**arXiv results are ordered by submission date in descending order**, prioritizing the timeliness of recent research; **Google Scholar results retain the source's default relevance ranking**, ensuring strong alignment with the query keywords while appropriately weighing influential or classical literature.

**Parameters**:

**Input Examples**:

{"command": "search", "query": "neural radiance fields", "source": "arxiv", "limit": 10, "output": "results.json"} | python scripts/hxxra.py
{"command": "search", "query": "transformer architecture", "source": "scholar", "limit": 15} | python scripts/hxxra.py

**Output Structure**:

{
  "ok": true,
  "command": "search",
  "query": "<query>",
  "source": "<source>",
  "results": [
    {
      "id": "1",
      "title": "Paper Title",
      "authors": ["Author1", "Author2"],
      "year": "2023",
      "source": "arxiv",
      "abstract": "Abstract text...",
      "url": "https://arxiv.org/abs/xxxx.xxxxx",
      "pdf_url": "https://arxiv.org/pdf/xxxx.xxxxx.pdf",
      "citations": 123
    }
  ],
  "total": 10,
  "output_file": "/path/to/results.json"
}

------

2. **hxxra download** - Download PDF files

**Purpose**: Download PDFs for specified papers

**Parameters**:

**Input Examples**:

{"command": "download", "from-file": "results.json", "ids": ["1", "3", "5"], "dir": "./downloads"} | python scripts/hxxra.py
{"command": "download", "from-file": "results.json", "dir": "./downloads"} | python scripts/hxxra.py

**Output Structure**:

{
  "ok": true,
  "command": "download",
  "downloaded": [
    {
      "id": "1",
      "title": "Paper Title",
      "status": "success",
      "pdf_path": "{workspace}/hxxra/papers/2023_Smith_NeRF_Explained/2023_Smith_NeRF_Explained.pdf",
      "size_bytes": 1234567,
      "url": "https://arxiv.org/pdf/xxxx.xxxxx.pdf"
    }
  ],
  "failed": [],
  "total": 3,
  "successful": 3,
  "download_dir": "{workspace}/hxxra/papers"
}

------

3. **hxxra analyze** - Analyze PDF content

**Dependencies**: `pip install pymupdf pdfplumber openai`

**Purpose**: Analyze paper content using LLM

**Parameters**:

** Note: Either `--pdf` or `--directory` must be provided, but not both*

**Input Examples**:

{"command": "analyze", "pdf": "paper.pdf", "output": "./analysis/"} | python scripts/hxxra.py
{"command": "analyze", "directory": "hxxra/papers/"} | python scripts/hxxra.py

**Output Structure**:

{
  "ok": true,
  "command": "analyze",
  "analyzed": [
    {
      "id": "paper_1",
      "original_file": "paper.pdf",
      "analysis_file": "{workspace}/hxxra/papers/2023_Smith_NeRF_Explained/analysis.json",
      "metadata": {
        "title": "Paper Title",
        "authors": ["Author1", "Author2"],
        "year": "2023",
        "abstract": "Abstract text..."
      },
      "analysis": {
        "background": "Problem background...",
        "methodology": "Proposed method...",
        "results": "Experimental results...",
        "conclusions": "Conclusions..."
      },
      "status": "success"
    }
  ],
  "summary": {
    "total": 1,
    "successful": 1,
    "failed": 0
  }
}

------

4. **hxxra report** - Generate Markdown report

**Purpose**: Generate a comprehensive Markdown report from all `analysis.json` files in a directory

**Parameters**:

**Input Examples**:

{"command": "report", "directory": "hxxra/papers/", "output": "hxxra/papers/report.md", "title": "My Research Papers", "sort": "year"} | python scripts/hxxra.py
{"command": "report", "directory": "hxxra/papers/"} | python scripts/hxxra.py

**Output Structure**:

{
  "ok": true,
  "command": "report",
  "total_papers": 10,
  "output_file": "/path/to/hxxra/papers/report.md"
}

**Generated Markdown Format**:

The generated report includes:

- Title, authors, year, keywords, code link (if available)

- Abstract

- Research background

- Methodology

- Main results

- Conclusions

- Limitations

- Impact

- Source folder path

**Note**: The report command recursively scans all subdirectories for `analysis.json` files and only includes papers with `status: "success"`.

------

5. **hxxra save** - Save to Zotero

**Purpose**: Save papers to Zotero collection

**Parameters**:

**Input Examples**:

{"command": "save", "from-file": "hxxra/searches/search_results.json", "ids": ["1", "2", "3"], "collection": "AI Research"} | python scripts/hxxra.py
{"command": "save", "from-file": "hxxra/searches/search_results.json", "collection": "My Collection"} | python scripts/hxxra.py

**Output Structure**:

{
  "ok": true,
  "command": "save",
  "collection": "AI Research",
  "saved_items": [
    {
      "id": "1",
      "title": "Paper Title",
      "zotero_key": "ABCD1234",
      "url": "https://www.zotero.org/items/ABCD1234",
      "status": "success"
    }
  ],
  "failed_items": [],
  "total": 3,
  "successful": 3,
  "zotero_collection": "ABCD5678"
}

------

Workflow Examples

Complete Workflow

# 1. Search for papers
{"command": "search", "query": "graph neural networks", "source": "arxiv", "limit": 10, "output": "hxxra/searches/gnn_arxiv.json"} | python scripts/hxxra.py

# 2. Download papers
{"command": "download", "from-file": "hxxra/searches/gnn_arxiv.json", "dir": "hxxra/papers"} | python scripts/hxxra.py

# 3. Analyze downloaded papers
{"command": "analyze", "directory": "hxxra/papers/"} | python scripts/hxxra.py

# 4. Generate comprehensive report
{"command": "report", "directory": "hxxra/papers/", "output": "hxxra/papers/report.md", "sort": "year"} | python scripts/hxxra.py

# 5. Save to Zotero
{"command": "save", "from-file": "hxxra/searches/gnn_arxiv.json", "collection": "GNN Papers"} | python scripts/hxxra.py

Single Command Examples

# Search with scholar
{"command": "search", "query": "reinforcement learning", "source": "scholar", "limit": 15} | python scripts/hxxra.py

# Download specific papers
{"command": "download", "from-file": "hxxra/searches/search_results.json", "ids": ["2", "4", "6"], "dir": "hxxra/papers"} | python scripts/hxxra.py

# Analyze single PDF in detail
{"command": "analyze", "pdf": "hxxra/papers/2024_Zhang_Transformer_Survey/2024_Zhang_Transformer_Survey.pdf"} | python scripts/hxxra.py

# Generate report sorted by title
{"command": "report", "directory": "hxxra/papers/", "sort": "title", "output": "hxxra/papers/report_by_title.md"} | python scripts/hxxra.py

# Save with custom notes
{"command": "save", "from-file": "hxxra/searches/search_results.json", "ids": ["1"], "collection": "To Read"} | python scripts/hxxra.py

Configuration Requirements

API Credentials(config.json)

1. **arXiv API**: No key required for basic access

2. **Google Scholar**: May require authentication for large queries

3. **Zotero API**: Required credentials:

```json

{

"api_key": "YOUR_ZOTERO_API_KEY", # Create at https://www.zotero.org/settings/keys/new

"user_id": "YOUR_ZOTERO_USER_ID", # Found on the same page (numeric, not username)

"library_type": "user" # or "group"

}

```

4. **LLM API**: OpenAI or compatible API key for analysis

Notes

Error Handling

Each command returns standard error format:

{
  "ok": false,
  "command": "<command>",
  "error": "Error description",
  "error_code": "ERROR_TYPE",
  "suggestion": "How to fix it"
}

Development Status

Current Version: v1.2.0 (2026/3/8)

Version History

**v1.2.0 · 2026/3/8**

**v1.1.1 · 2026/3/7**

**v1.1.0 · 2026/3/7**

**v1.0.2 · 2026/3/6**

**v1.0.1 · 2026/3/6**

**v1.0.0 · 2026/2/9**

Initial release of hxxra – a research assistant tool for searching, downloading, analyzing, and saving research papers.