Paper Claw Skill

Intelligent multi-source paper digest generator. Automatically fetch, classify, and summarize papers with AI-powered translations in 7 languages.

Features

• 🌐 Multi-Source Support — arXiv (170+ categories), extensible for CNKI, Web of Science
• 🗣️ Multi-Language — Chinese, English, Japanese, Korean, German, French, Spanish
• 🤖 Multi-Provider LLM — Kimi, OpenAI, Claude, Gemini, DeepSeek with auto-fallback
• 📧 Email Delivery — HTML digests with full Markdown attachment
• 👥 Recipient Management — JSON-based configuration
• ⚙️ Config-Driven — Zero-code customization
• 🔄 State Persistence — Auto-deduplication

Setup

1. Environment Variables

Required for email delivery:

export SMTP_HOST="smtp.qq.com"
export SMTP_PORT="465"
export SMTP_USER="your-email@qq.com"
export SMTP_PASS="your-auth-code"

Optional for AI summaries (multiple providers supported):

# Primary: Kimi AI (recommended for Chinese)
export MOONSHOT_API_KEY="sk-your-kimi-key"

# Alternatives (auto-fallback)
export OPENAI_API_KEY="sk-your-openai-key"
export ANTHROPIC_API_KEY="sk-your-claude-key"
export GOOGLE_API_KEY="your-gemini-key"
export DEEPSEEK_API_KEY="sk-your-deepseek-key"

2. Recipient Configuration

Create config/recipients.json:

{
  "recipients": [
    {"email": "prof@university.edu.cn", "name": "Professor", "enabled": true},
    {"email": "student@university.edu.cn", "name": "Student", "enabled": true}
  ]
}

3. Source & Category Configuration

Edit config/default.json to customize sources:

{
  "sources": {
    "arxiv": {
      "enabled": true,
      "categories": [
        {"id": "cs.CL", "name": "NLP", "url": "https://arxiv.org/list/cs.CL/recent"},
        {"id": "cs.CV", "name": "Computer Vision", "url": "https://arxiv.org/list/cs.CV/recent"}
      ]
    }
  }
}

See config/arxiv_categories.json for all 170+ available categories.

4. Language Configuration

{
  "language": {
    "default": "zh",
    "supported": ["zh", "en", "ja", "ko", "de", "fr", "es"]
  }
}

Quick Start for Agents

The fastest way to configure Paper Claw is using Presets:

from skill.example import list_presets, preview_preset, apply_preset

# Step 1: See available presets
presets = list_presets()
# Returns: [
#   {"id": "speech_audio", "name": "Speech & Audio", ...},
#   {"id": "nlp", "name": "NLP & LLM", ...},
#   {"id": "computer_vision", "name": "Computer Vision", ...},
#   {"id": "general_ai", "name": "General AI/ML", ...}
# ]

# Step 2: Preview what will be configured
preview = preview_preset("nlp")
# Shows: arXiv categories (cs.CL, cs.LG) and classification categories (LLM, RAG, etc.)

# Step 3: Apply the preset
apply_preset("nlp")  # Updates config/default.json automatically

Available Presets

| Preset ID | Research Field | ArXiv Categories | Classification |

|-----------|---------------|------------------|----------------|

| speech_audio | Speech & Audio | cs.SD, eess.AS | Speech LLM, ASR, TTS, Enhancement, SLU, Paralinguistics, Audio |

| nlp | NLP & LLM | cs.CL, cs.LG, cs.AI | LLM, RAG, Agents, NLP Tasks, Evaluation |

| computer_vision | Computer Vision | cs.CV, cs.MM, cs.LG | Image Generation, Object Detection, Segmentation, Video Understanding, Multimodal, 3D Vision |

| general_ai | General AI/ML | cs.AI, cs.LG, cs.CL, cs.CV, stat.ML | Deep Learning, RL, Generative Models, Optimization, Theory, Applications |

Detailed Usage

List Presets

from skill.example import list_presets

presets = list_presets()
for p in presets:
    print(f"{p['id']}: {p['name']}")
    print(f"  {p['description']}")

Preview Before Apply

from skill.example import preview_preset

# See what will be configured
preview = preview_preset("computer_vision")
print(f"ArXiv categories: {[c['id'] for c in preview['arxiv_categories']]}")
print(f"Classifications: {[c['name'] for c in preview['classification_categories']]}")

Apply Preset

from skill.example import apply_preset

# Apply NLP configuration
result = apply_preset("nlp")
if result["success"]:
    print(f"Applied: {result['preset_name']}")
    print(f"ArXiv: {result['arxiv_categories']}")
    print(f"Categories: {result['classification_categories']}")

Fetch Papers

# Fetch today's papers (default language from config)
python scripts/main.py

# Fetch with specific language
python scripts/main.py --day 2026-03-10 --language en
python scripts/main.py --day 2026-03-10 --language ja  # Japanese

# Fetch date range
python scripts/main.py --start-date 2026-03-01 --end-date 2026-03-10

Generated Outputs

• Markdown digest: content/posts/YYYY-MM-DD-arxiv-audio-digest.md
• JSON data: data/processed/YYYY-MM-DD.json
• Raw data: data/raw/YYYY-MM-DD.json

Email Delivery

Email is automatically sent with:

• HTML preview — Shows first 3 papers with logo and GitHub link
• Full Markdown attachment — Complete digest with all papers

Schedule Daily Runs

GitHub Actions:

Already configured in .github/workflows/daily_digest.yml

Linux/Mac Cron:

0 1 * * * cd /path/to/paper_claw && python scripts/main.py

Windows Task Scheduler:

$Action = New-ScheduledTaskAction -Execute "python.exe" -Argument "scripts/main.py"
$Trigger = New-ScheduledTaskTrigger -Daily -At "09:00"
Register-ScheduledTask -TaskName "PaperClaw" -Action $Action -Trigger $Trigger

AI Summary Chain

The system uses intelligent fallback across providers:

Kimi → OpenAI → Claude → DeepSeek → Gemini → Rule-based

Even without API keys, summaries are generated using rule-based methods.

Agent Tools

fetch_papers

Fetch papers from configured sources.

Parameters:

• day (string, optional): Date in YYYY-MM-DD format
• start_date + end_date (string, optional): Date range
• language (string, optional): Output language (zh/en/ja/ko/de/fr/es)

Example:

from skill.example import fetch_papers
result = fetch_papers(day="2026-03-10", language="en")

configure_sources

Update data sources and categories.

Parameters:

• sources (object): Source configuration with categories

Example:

from skill.example import configure_sources
configure_sources({
    "arxiv": {
        "enabled": True,
        "categories": [
            {"id": "cs.AI", "name": "AI"},
            {"id": "cs.LG", "name": "ML"}
        ]
    }
})

configure_language

Set output language for summaries.

Parameters:

• language (string): One of zh/en/ja/ko/de/fr/es

Example:

from skill.example import configure_language
configure_language("ja")  # Japanese output

get_digest_content

Retrieve generated digest.

Parameters:

• date (string): Date in YYYY-MM-DD format
• format (string): "markdown", "json", or "summary"

Example:

from skill.example import get_digest_content
content = get_digest_content("2026-03-10", format="summary")

configure_recipients

Update email recipients.

Parameters:

• recipients (array): List of {email, name, enabled}

Example:

from skill.example import configure_recipients
configure_recipients([
    {"email": "user@example.com", "name": "User", "enabled": True}
])

Preset Details

Speech & Audio (Default)

Best for: Speech recognition, synthesis, audio processing researchers

ArXiv Categories:

• cs.SD - Sound (Audio processing, music computing)
• eess.AS - Audio and Speech Processing

Classification:

| Category | Keywords |

|----------|----------|

| Speech LLM | speech llm, audio llm, spoken language model |

| ASR | asr, speech recognition, speech-to-text, whisper |

| TTS | tts, text-to-speech, speech synthesis, tacotron |

| Enhancement | speech enhancement, noise reduction, beamforming |

| SLU | spoken language understanding, intent recognition |

| Paralinguistics | emotion recognition, speaker verification |

| Audio | audio classification, sound event detection |

NLP & LLM

Best for: Natural language processing, large language model researchers

ArXiv Categories:

• cs.CL - Computation and Language
• cs.LG - Machine Learning
• cs.AI - Artificial Intelligence

Classification:

| Category | Keywords |

|----------|----------|

| LLM | llm, gpt, transformer, prompt engineering, llama, bert |

| RAG | rag, retrieval-augmented, knowledge base, embedding |

| Agents | agent, multi-agent, tool use, function calling |

| NLP Tasks | ner, sentiment analysis, translation, summarization |

| Evaluation | benchmark, evaluation metrics, human evaluation |

Computer Vision

Best for: Computer vision, image processing, multimodal researchers

ArXiv Categories:

• cs.CV - Computer Vision
• cs.MM - Multimedia
• cs.LG - Machine Learning

Classification:

| Category | Keywords |

|----------|----------|

| Image Generation | diffusion model, gan, stable diffusion, text-to-image |

| Object Detection | yolo, rcnn, ssd, bounding box |

| Segmentation | semantic segmentation, mask, sam, u-net |

| Video Understanding | action recognition, temporal, tracking |

| Multimodal | vision-language, clip, image-text, vqa |

| 3D Vision | point cloud, depth estimation, nerf |

General AI/ML

Best for: Broad AI/ML research covering multiple domains

ArXiv Categories:

• cs.AI, cs.LG, cs.CL, cs.CV, stat.ML

Classification:

| Category | Keywords |

|----------|----------|

| Deep Learning | neural network, optimization, gradient descent |

| Reinforcement Learning | rl, q-learning, policy gradient, actor-critic |

| Generative Models | gan, vae, diffusion, flow-based |

| Optimization | convex optimization, learning rate, adam |

| Theory | generalization, convergence, bounds, complexity |

| Applications | healthcare, finance, robotics, real-world |

Customizing After Preset

After applying a preset, you can further customize:

from skill.example import configure_sources, configure_categories

# Add more arXiv categories
configure_sources({
    "arxiv": {
        "enabled": True,
        "categories": [
            {"id": "cs.IR", "name": "Information Retrieval", 
             "url": "https://arxiv.org/list/cs.IR/recent"}
        ]
    }
})

# Add custom classification category
configure_categories([
    {
        "name": "Your Custom Category",
        "labels": {"zh": "自定义分类", "en": "Custom"},
        "keywords": ["keyword1", "keyword2"]
    }
])

SMTP Providers

| Service | Host | Port | Note |

|---------|------|------|------|

| QQ Mail | smtp.qq.com | 465 | Use authorization code |

| 163 Mail | smtp.163.com | 465 | Use authorization code |

| Gmail | smtp.gmail.com | 465 | Use app password |

Notes

• All configurations are in config/ directory
• .env and config/recipients.json are git-ignored for security
• API rate limits: System auto-retries with fallback providers
• State is tracked in data/state.json to avoid duplicate processing
• Email includes both HTML preview and full Markdown attachment
• Logo displayed in emails from GitHub raw URL

Examples

# Quick start - fetch and send email
python scripts/main.py --day 2026-03-10

# Multi-language examples
python scripts/main.py --day 2026-03-10 --language zh  # Chinese
python scripts/main.py --day 2026-03-10 --language en  # English
python scripts/main.py --day 2026-03-10 --language ja  # Japanese

# View paper count
cat data/processed/2026-03-10.json | jq '.summary.total'

# View papers by category
cat data/processed/2026-03-10.json | jq '.grouped.ASR'

# Reset state and re-fetch
python scripts/reset_state.py
python scripts/main.py --day 2026-03-10

Files

• skill/tools.json — Tool definitions for agent frameworks
• skill/example.py — Python usage examples
• config/default.json — Source and language configuration
• config/arxiv_categories.json — Complete arXiv category list
• config/recipients.example.json — Recipient template