paper claw

Fetch, classify, and summarize papers from multiple sources (arXiv, etc.) with AI-powered multi-language summaries and email delivery.

PigeonDan1@pigeondan1

Install

openclaw skills install @pigeondan1/paperclaw

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:

bash

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):

bash

# 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:

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:

json

{
  "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

json

{
  "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:

python

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

python

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

python

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

python

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

bash

# 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:

bash

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

Windows Task Scheduler:

powershell

$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:

text

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

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

bash

# 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

paper claw

Install

Paper Claw Skill

Features

Setup

1. Environment Variables

2. Recipient Configuration

3. Source & Category Configuration

4. Language Configuration

Quick Start for Agents

Available Presets

Detailed Usage

List Presets

Preview Before Apply

Apply Preset

Fetch Papers

Generated Outputs

Email Delivery

Schedule Daily Runs

AI Summary Chain

Agent Tools

fetch_papers

configure_sources

configure_language

get_digest_content

configure_recipients

Preset Details

Speech & Audio (Default)

NLP & LLM

Computer Vision

General AI/ML

Customizing After Preset

SMTP Providers

Notes

Examples

Files

Related skills