SpatialClaw

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# SpatialClaw

Memory-enabled spatial omics analysis platform.

Local-first skills, canonical CLI, and persistent research context.

SpatialClaw is a memory-augmented autonomous ecosystem for spatial omics
analysis, with a canonical CLI, LLM-assisted routing, graph memory, and
messaging/terminal interfaces. The current release is centered on spatial
transcriptomics workflows, with support for selected paired spatial modality
and image-omics tasks.

[![Demo](https://raw.githubusercontent.com/ShangBioLab/SpatialClaw/main/docs/demo_cover.png)](https://github.com/user-attachments/assets/edd456cf-e0ff-402e-9701-a0564e2e56bc)

## Features

- Persistent graph memory for datasets, preferences, analyses, and sessions.
- Spatial skill registry with strict canonical skill names and whitelisted flags.
- Local-first execution through `spatialclaw.py run <skill>`.
- Interactive CLI/TUI and Telegram/Feishu bot surfaces.
- Spatial preprocessing, domain identification, annotation, deconvolution,
  communication, statistics, enrichment, trajectory, velocity, CNV,
  registration, visualization, and paired spatial modality integration.

## Quick Start

```bash
git clone <your-repo-url>
cd SpatialClaw
pip install -e .

python spatialclaw.py list
python spatialclaw.py run spatial-preprocessing --demo --output /tmp/spatialclaw_demo
```

After installation, the console command is also available:

```bash
spatialclaw list
sc list
spatialclaw run spatial-preprocessing --demo
sc run spatial-preprocessing --demo
```

## Canonical Skill Names

Short legacy skill names are intentionally not supported. Use canonical names
from the registry:

```bash
python spatialclaw.py run spatial-preprocessing --demo
python spatialclaw.py run spatial-domain-identification --input sample.h5ad --output results/domains
python spatialclaw.py run spatial-svg-detection --input processed.h5ad --output results/svg
python spatialclaw.py run spatial-cell-annotation --input processed.h5ad --output results/annotate
python spatialclaw.py run spatial-deconvolution --input processed.h5ad --output results/deconvolution
python spatialclaw.py run spatial-cell-communication --input processed.h5ad --output results/communication
python spatialclaw.py run spatial-condition-comparison --input processed.h5ad --output results/condition
python spatialclaw.py run spatial-integration --input sample.h5ad --output results/integration
python spatialclaw.py run spatial-registration --input sample.h5ad --output results/registration
python spatialclaw.py run spatial-orchestrator --demo --output results/orchestrator
```

## Interactive Use

```bash
python spatialclaw.py interactive
python spatialclaw.py interactive --ui tui
python spatialclaw.py interactive -p "run spatial-preprocessing demo"
spatialclaw-chat -p "run spatial-preprocessing demo"
sc-chat -p "run spatial-preprocessing demo"
python spatialclaw.py tui
python spatialclaw.py onboard
```

Common slash commands inside the interactive interface:

| Command                                  | Description                |
| ---------------------------------------- | -------------------------- |
| `/skills [domain]`                       | List spatial skills        |
| `/run <skill> [--demo] [--input <path>]` | Run a skill directly       |
| `/sessions`                              | List recent sessions       |
| `/resume [id]`                           | Resume a session           |
| `/new`                                   | Start a new session        |
| `/clear`                                 | Clear conversation history |
| `/mcp list`                              | List MCP servers           |
| `/config list`                           | View configuration         |
| `/help`                                  | Show commands              |
| `/exit`                                  | Quit                       |

## Configuration

Create `.env` at the project root or run:

```bash
python spatialclaw.py onboard
```

Examples:

```env
LLM_PROVIDER=deepseek
DEEPSEEK_API_KEY=sk-...
SPATIALCLAW_MODEL=deepseek-chat
```

For local LLMs:

```env
LLM_PROVIDER=ollama
SPATIALCLAW_MODEL=qwen2.5:7b
LLM_BASE_URL=http://localhost:11434/v1
```

## Project Layout

```text
SpatialClaw/
├── spatialclaw.py
├── spatialclaw/
│   ├── common/
│   ├── core/
│   ├── loaders/
│   ├── memory/
│   ├── routing/
│   ├── agents/
│   └── interactive/
├── skills/
│   └── spatial/
│       ├── _lib/
│       ├── spatial-preprocessing/
│       ├── spatial-domain-identification/
│       ├── spatial-cell-annotation/
│       ├── spatial-deconvolution/
│       ├── spatial-modality-integrate/
│       └── spatial-orchestrator/
│   └── orchestrator/              # Top-level spatial orchestration wrapper
├── bot/
├── docs/
├── examples/
├── sessions/
└── results/
```

## Bot Channels

```bash
pip install -r bot/requirements.txt
python bot/telegram_bot.py
python bot/feishu_bot.py
python -m bot.run --channels telegram,feishu
```

## Development

```bash
python -m pytest -v
make demo
make demo-orchestrator
```

To add a spatial skill:

1. Create `skills/spatial/<skill-directory>/`.
2. Add `SKILL.md`.
3. Add a Python script with `--input`, `--output`, and optional `--demo`.
4. Register canonical metadata and allowed flags in `spatialclaw/core/registry.py`.
5. Add focused tests.
6. Run `python scripts/generate_catalog.py` if the catalog is needed.

## Safety

- All processing is local-first.
- Reports include the SpatialClaw disclaimer.
- CLI passthrough arguments are validated against registry metadata.
- This is a research tool, not a medical device.

## Documentation

- [AGENTS.md](AGENTS.md) — coding-agent guide
- [docs/INSTALLATION.md](docs/INSTALLATION.md) — installation details
- [docs/METHODS.md](docs/METHODS.md) — spatial method reference
- [bot/README.md](bot/README.md) — bot setup

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