medgeclaw

🧬 MedgeClaw

An AI-Powered Biomedical Research Assistant

English | 中文

---

An open-source biomedical AI research assistant built on OpenClaw and Claude Code, integrating 140 K-Dense Scientific Skills for bioinformatics, drug discovery, clinical research, and more.

Talk to your research assistant via WhatsApp, Slack, or Discord → it runs the analysis → you view results in RStudio or JupyterLab.

---

Architecture

User (voice / text via WhatsApp · Slack · Feishu · Discord)
        ↓
OpenClaw Gateway  (conversation layer)
        ↓  biomed-dispatch skill
Claude Code  (execution layer)
        ↓  K-Dense Scientific Skills (140 skills)
R + Python Analysis Environment (Docker)
        ↓                    ↓                    ↓
Research Dashboard :77xx    RStudio :8787       Feishu Rich Cards
  (real-time progress,      / JupyterLab :8888    (SVG panels → PNG
   code & output preview)   (interactive)          → interactive cards)

---

What's Included

Component	Description
OpenClaw	Conversational AI gateway — connects to your messaging apps
Claude Code	Executes complex analysis workflows autonomously
K-Dense Scientific Skills	140 ready-to-use skills: genomics, drug discovery, clinical research, ML, and more
Research Dashboard	Real-time web dashboard showing progress, code, outputs, and file previews
R Environment	DESeq2, Seurat, edgeR, clusterProfiler, survival, ggplot2, and more
Python Environment	Scanpy, BioPython, PyDESeq2, lifelines, scikit-learn, and more
RStudio Server	Browser-based R IDE at localhost:8787
JupyterLab	Browser-based Python/R notebooks at localhost:8888
biomed-dispatch	The bridge skill that routes your requests to Claude Code
CJK Visualization	Auto-detects CJK fonts for matplotlib, no more tofu blocks in Chinese plots
SVG UI Templates	Professional SVG panels for lists, checklists, pipeline status, and rich reports
Feishu Rich Card	Send image-rich interactive cards in Feishu group chats for progress reports

---

Prerequisites

• Node.js 22+ — nodejs.org
• Docker + docker-compose — docs.docker.com
• Git
• An API key from one of the supported model providers (see below)

---

Quick Start

# 1. Clone with submodules (includes K-Dense Scientific Skills)
git clone --recurse-submodules https://github.com/xjtulyc/MedgeClaw
cd MedgeClaw

# 2. Configure environment
cp .env.example .env
nano .env  # Fill in your API key and provider settings

# 3. Run setup
bash setup.sh

# 4. Sync MedgeClaw configuration to OpenClaw
python3 sync.py
openclaw gateway restart

# 5. Start the analysis environment
docker compose up -d

# 6. Start OpenClaw
openclaw onboard

Then open your messaging app and start talking to your assistant.

Environment Configuration

The .env file controls:

• API settings: Provider, base URL, model, API key
• Third-party proxy fix: ANTHROPIC_SMALL_FAST_MODEL (required for non-Anthropic endpoints)
• Path overrides (optional): MEDGECLAW_ROOT, OPENCLAW_DIR
• Docker settings: Container name, RStudio/Jupyter passwords

See .env.example for all available options.

---

Model Providers

Edit .env to choose your provider. All providers are drop-in replacements — no other changes needed.

Provider	Base URL	Notes
Anthropic Claude (default)	https://api.anthropic.com	Best quality
MiniMax 2.1	https://api.minimax.chat/anthropic	Available in CN
GLM-4.7 (Z.ai)	https://api.z.ai/api/anthropic	Available in CN
DeepSeek	https://api.deepseek.com/anthropic	Low cost
Ollama (local)	http://localhost:11434/v1	Fully offline

---

⚠️ Using Third-Party API Proxies

If you use a third-party API proxy (MiniMax, GLM, DeepSeek, or any non-Anthropic endpoint), you must configure ANTHROPIC_SMALL_FAST_MODEL in your .env file. Without this, Claude Code will fail silently.

Why

Claude Code runs a pre-flight safety check before every bash command, using a lightweight "small fast model" (defaults to claude-3-5-haiku). Most third-party proxies don't support Haiku, causing the pre-flight to return 503 errors and hang indefinitely with:

⚠️ [BashTool] Pre-flight check is taking longer than expected.

Fix

Add this line to your .env:

# Required for third-party API proxies:
ANTHROPIC_SMALL_FAST_MODEL=claude-sonnet-4-20250514  # or any model your proxy supports

Then re-run bash setup.sh to apply.

How to verify

# Should complete in < 30 seconds. If it hangs, your SMALL_FAST_MODEL is wrong.
claude --dangerously-skip-permissions -p 'run: echo hello'

---

Usage Examples

Once OpenClaw is running, send messages like:

Analyze RNA-seq data at data/counts.csv vs data/meta.csv, treatment vs control

Search PubMed for recent papers on CRISPR base editing, summarize top 10

Run survival analysis on data/clinical.csv, time=OS_months, event=OS_status

Perform single-cell RNA-seq analysis on the 10X data in data/10x/

Virtual screen EGFR inhibitors from ChEMBL (IC50 < 50nM), generate SAR report

Results are saved to ./outputs/ and viewable in RStudio (localhost:8787) or JupyterLab (localhost:8888).

---

📊 Research Dashboard

Every analysis task automatically spawns a live web dashboard — no need to wait for completion or check logs.

Features:

• Real-time progress bar — sticky header, always visible
• Analysis plan overview — all steps listed with completion status (✅/⏳)
• Step-by-step breakdown — each step shows: description → code (collapsible) → outputs
• Inline previews — images render directly, tables load from CSV files, text results highlighted
• Full script access — click to load the complete .py file, not just snippets
• Copy & download everywhere — 📋 copy code/tables/text, ⬇ download images/CSVs
• Color-blind friendly — IBM accessible palette + GitHub Dark theme
• File browser — browse all output artifacts with one-click preview

How it works:

AI completes a step → updates state.json → dashboard auto-refreshes (2s polling)

Three files, zero dependencies: dashboard.html + state.json + dashboard_serve.py.

See docs/dashboard.md for the full specification.

---

Directory Structure

MedgeClaw/
├── docker/
│   ├── Dockerfile          # R + Python + RStudio + Jupyter
│   └── entrypoint.sh
├── skills/
│   ├── biomed-dispatch/    # Core bridge skill: routes tasks to Claude Code
│   │   └── SKILL.md
│   ├── dashboard/          # Research Dashboard: real-time task visualization
│   │   ├── SKILL.md        # Dashboard specification & state.json schema
│   │   ├── dashboard.html  # Single-file frontend (dark theme, IBM palette)
│   │   └── dashboard_serve.py  # Threaded HTTP server
│   ├── cjk-viz/            # CJK font detection for matplotlib
│   │   └── SKILL.md
│   ├── svg-ui-templates/   # Professional SVG panels (list, checklist, pipeline, report)
│   │   ├── SKILL.md
│   │   ├── assets/         # 4 template SVGs
│   │   └── references/     # Template guide
│   └── feishu-rich-card/   # Send image-rich interactive cards in Feishu
│       ├── SKILL.md
│       └── references/     # send_card.py helper
├── scientific-skills/      # git submodule → K-Dense (140 skills)
├── data/                   # Per-task data & analysis directories
│   └── <task_name>/
│       ├── dashboard/      # state.json + dashboard.html (auto-created)
│       └── output/         # Analysis outputs (CSV, PNG, etc.)
├── docs/                   # Project documentation
├── docker-compose.yml
├── setup.sh
├── CLAUDE.md               # Project instructions for Claude Code
├── .env.example
└── .gitmodules

---

🔄 Project Synchronization

MedgeClaw integrates with OpenClaw via a configuration-driven sync mechanism.

How it works

• Configuration: .medgeclaw-sync.yml defines what to sync
• Sync script: sync.py reads the config and performs the sync
• Environment: .env defines paths (optional, auto-detected by default)

When to sync

Run python3 sync.py after:

• Initial setup
• Modifying project documentation (MEDGECLAW.md, IDENTITY.md)
• Adding/updating custom skills in skills/
• Changing sync configuration in .medgeclaw-sync.yml

What gets synced

• Project docs → OpenClaw workspace
• Custom skills → OpenClaw workspace/skills/
• Identity/context → SOUL.md, AGENTS.md (appended)
• Skill paths → openclaw.json

See SYNC_README.md for details.

---

Updating K-Dense Scientific Skills

git submodule update --remote scientific-skills

---

Contributing

Contributions welcome. The most valuable contributions are:

• Improvements to skills/biomed-dispatch/SKILL.md for better task routing
• New domain-specific skills in skills/ (e.g., for specific clinical or lab workflows)
• Improvements to the Dockerfile (lighter image, newer package versions)

Please follow the AgentSkills specification for any new skills.

---

Roadmap

• Core architecture: OpenClaw + Claude Code + K-Dense Scientific Skills integration
• Docker analysis environment with RStudio Server and JupyterLab
• Third-party API proxy support (ANTHROPIC_SMALL_FAST_MODEL fix for BashTool pre-flight)
• CLAUDE.md project instructions for Claude Code (docker exec execution model)
• Multi-language analysis validation (Python + R, direct + Claude Code + K-Dense skills)
• Research Dashboard: real-time web dashboard with progress tracking, step-by-step code & output preview, copy/download, color-blind friendly design
• CJK visualization skill: auto-detect CJK fonts in Docker, solve .ttc font rendering issues
• Feishu integration: connect to Feishu group chat for team collaboration
• SVG UI Templates: professional-grade SVG panels (list, checklist, pipeline status, rich-text layout) with Material Design palette
• Feishu Rich Cards: send image-rich interactive cards in Feishu — SVG/matplotlib → PNG → Card Kit API, with mixed text+image layout
• Multi-agent workflow: Parallel dispatch of sub-analyses (e.g., Python + R simultaneously) with result aggregation and cross-validation
• Automated literature integration: Connect PubMed/bioRxiv search → auto-generate introduction and discussion sections referencing analysis results
• Interactive report builder: Auto-generate publication-ready HTML/PDF reports with figures, tables, and statistical narratives from analysis outputs