Scientify

AI-powered research workflow automation for OpenClaw.

中文文档

Features

Skills (LLM-powered)

Commands (Direct, no LLM)

Tools

Quick Start

# Install the plugin
openclaw plugins install scientify

# Start using
openclaw "Research transformer efficiency and generate ideas"

Installation

openclaw plugins install scientify

Note: Do NOT use npm install scientify. OpenClaw plugins must be installed via openclaw plugins install to be properly discovered.

The plugin will be installed to ~/.openclaw/extensions/scientify/ and automatically enabled.

Usage Scenarios

1. One-shot Idea Generation

You: Research "long document summarization" and generate some innovative ideas

Agent: [Auto-executes]
  1. Search arXiv papers
  2. Search GitHub repositories
  3. Download and analyze .tex sources
  4. Generate 5 innovative ideas
  5. Select and enhance the best idea
  6. Map to code implementations

2. Daily Literature Tracking

You: Set up a daily task at 9am to check arXiv for new papers on
    "transformer efficiency", summarize relevant ones and send to Feishu

Agent: Setting up:
  1. Create scheduled Hook (cron: "0 9 * * *")
  2. Daily arxiv search for papers from last 24h
  3. Compare against your idea (selected_idea.md)
  4. Filter relevant papers, generate summary
  5. Push via Feishu webhook

[Example push]
📚 Today's Relevant Papers (3)
• "FlashAttention-3: Fast Attention with ..." - Highly relevant to your idea
• "Efficient Long-Context Transformers" - Medium relevance
• "..."

3. Research Progress Reporter

You: Generate weekly research progress report every Friday and send to Slack

Agent: [Auto-executes every Friday]
  1. Read $WORKSPACE changes
  2. Stats: X new papers, Y new ideas, Z code commits
  3. Generate weekly report markdown
  4. Push to Slack #research channel

4. Competitive Paper Monitor

You: Monitor new papers from "Yann LeCun" and "Meta AI"

Agent: Setting up monitoring:
  - Daily check arxiv author "Yann LeCun"
  - Check arxiv affiliation "Meta AI"
  - Push notification when new papers appear

5. Paper Reading Assistant

You: Read papers/2401.12345/ and compare its method with my idea

Agent: [Reading paper .tex files]

  📄 Paper: "Efficient Attention for Long Documents"

  ## Comparison with Your Idea

  | Aspect | Paper Method | Your Method |
  |--------|-------------|-------------|
  | Attention | Sparse Attention | Hierarchical Attention |
  | Complexity | O(n√n) | O(n log n) |
  | Advantage | Simple implementation | Preserves more info |

  ## Suggestions
  Consider combining both approaches...

6. Related Work Writer

You: Write Related Work section based on papers in my project

Agent: [Reading papers/ and literature/notes/]

  Generating related_work.tex:

  \section{Related Work}

  \subsection{Long Document Summarization}
  Recent works have explored various approaches...
  \cite{paper1} proposed...
  \cite{paper2} extended this by...

  \subsection{Efficient Transformers}
  ...

7. Idea Mashup

You: Combine "reinforcement learning" and "text summarization" to spark new ideas

Agent: [Searching papers from both fields]

  🔥 Mashup Results:

  1. **RL-guided Summarization**
     Use RL to optimize readability and information coverage
     References: [2301.xxx], [2302.xxx]

  2. **Interactive Summarization Agent**
     User feedback as reward signal for iterative optimization

  3. ...

8. Experiment Design Helper

You: My idea is xxx, help me design experiments

Agent: [Reading selected_idea.md and related papers]

  ## Experiment Design

  ### Datasets
  1. CNN/DailyMail - Standard news summarization (287k samples)
  2. arXiv - Long scientific papers (215k samples)
  3. ...

  ### Baselines
  1. BART-large (ref: paper_001.md)
  2. LED (ref: paper_003.md)

  ### Metrics
  - ROUGE-1/2/L
  - BERTScore
  - Human evaluation: fluency, information coverage

  ### Ablation Studies
  1. Remove xxx module
  2. ...

Workspace Structure

~/.openclaw/workspace/projects/
├── .active                      # Current project ID
├── nlp-summarization/           # Project A
│   ├── project.json             # Metadata
│   ├── task.json                # Task definition
│   ├── survey/
│   │   ├── search_terms.json    # Search terms used
│   │   └── report.md            # Final survey report
│   ├── papers/
│   │   ├── _downloads/          # Raw downloaded files
│   │   ├── _meta/               # Paper metadata JSON files
│   │   │   └── {arxiv_id}.json
│   │   └── {direction}/         # Clustered papers by research direction
│   ├── repos/                   # Cloned repos
│   ├── notes/                   # /research-survey: per-paper analysis
│   │   └── paper_{arxiv_id}.md
│   ├── survey_res.md            # /research-survey: method comparison
│   ├── plan_res.md              # /research-plan: implementation plan
│   ├── project/                 # /research-implement: ML code
│   │   ├── model/
│   │   ├── data/
│   │   ├── run.py
│   │   └── requirements.txt
│   ├── ml_res.md                # /research-implement: execution report
│   ├── iterations/              # /research-review: judge reports
│   │   └── judge_v*.md
│   ├── experiment_res.md        # /research-experiment: final results
│   └── ideas/                   # Generated ideas
│       ├── idea_1.md
│       ├── idea_2.md
│       └── selected_idea.md     # Best idea
└── another-project/

Configuration

After installation, the plugin is automatically enabled. You can customize settings in ~/.openclaw/openclaw.json:

{
  "plugins": {
    "entries": {
      "scientify": {
        "enabled": true,
        "workspaceRoot": "~/my-research",
        "defaultMaxPapers": 15
      }
    }
  }
}

Plugin Management

# List installed plugins
openclaw plugins list

# Disable plugin
openclaw plugins disable scientify

# Enable plugin
openclaw plugins enable scientify

# Update to latest version
openclaw plugins update scientify

Known Limitations

Sandbox & GPU

The research-pipeline skill's code execution step depends on your OpenClaw agent configuration:

• If sandbox.mode: "off" (default for CLI), commands run directly on host
• Current sandbox does NOT support GPU (--gpus) or custom shared memory (--shm-size)

For GPU-accelerated ML training, consider:

1. Running outside sandbox (configure agent with sandbox.mode: "off")
2. Using a dedicated cloud GPU instance
3. Waiting for OpenClaw GPU support

Development

See CLAUDE.md for version update SOP and contribution guide.

License

MIT

Author

tsingyuai