arc-skill-sandbox
Test untrusted skills in an isolated environment before installing.
Setup & Installation
Install command
clawhub install trypto1019/arc-skill-sandboxIf the CLI is not installed:
Install command
npx clawhub@latest install trypto1019/arc-skill-sandboxOr install with OpenClaw CLI:
Install command
openclaw skills install trypto1019/arc-skill-sandboxor paste the repo link into your assistant's chat
Install command
https://github.com/openclaw/skills/tree/main/skills/trypto1019/arc-skill-sandboxWhat This Skill Does
Runs untrusted skills in a monitored environment before installation. Tracks filesystem access, environment variable reads, network connections, and subprocess calls during execution. Produces a JSON report with a safety verdict (SAFE / SUSPICIOUS / DANGEROUS).
Static analysis misses runtime behavior, so executing the skill in a monitored environment reveals what it actually does with your data and credentials.
When to Use It
- Testing a ClawHub skill before installing it on your real agent
- Checking if a downloaded script reads API keys or tokens
- Catching outbound network calls made by an unknown skill
- Running a skill with fake credentials to detect exfiltration attempts
- Generating a safety report before a team-wide skill rollout
View original SKILL.md file
# Skill Sandbox
Run untrusted skills in a monitored environment. See exactly what they do before giving them access to your real system.
## Why This Exists
ClawHub has hundreds of skills. Some are malicious. Even after scanning with arc-skill-scanner, you can't catch everything with static analysis. The sandbox lets you run a skill's scripts and observe their behavior at runtime — what network calls they make, what files they access, what environment variables they read.
## Commands
### Sandbox a skill directory
```bash
python3 {baseDir}/scripts/sandbox.py run --path ~/.openclaw/skills/some-skill/
```
### Run a specific script in sandbox
```bash
python3 {baseDir}/scripts/sandbox.py run --script ~/.openclaw/skills/some-skill/scripts/main.py
```
### Run with network monitoring
```bash
python3 {baseDir}/scripts/sandbox.py run --path ~/.openclaw/skills/some-skill/ --monitor-network
```
### Run with fake environment variables
```bash
python3 {baseDir}/scripts/sandbox.py run --path ~/.openclaw/skills/some-skill/ --fake-env
```
### Run with a time limit
```bash
python3 {baseDir}/scripts/sandbox.py run --path ~/.openclaw/skills/some-skill/ --timeout 30
```
### Generate a safety report
```bash
python3 {baseDir}/scripts/sandbox.py report --path ~/.openclaw/skills/some-skill/
```
## What It Monitors
### Filesystem Access
- Files opened (read/write)
- Directories created
- File deletions
- Permission changes
### Environment Variables
- Which env vars are read
- Whether sensitive keys are accessed (API keys, tokens, passwords)
- Option to inject fake values to see what the skill does with them
### Network Activity
- Outbound HTTP/HTTPS requests (URLs, methods, payloads)
- DNS lookups
- Socket connections
- FTP, SMTP, and other protocols
### Process Execution
- Subprocess calls
- Shell commands
- Dynamic imports
## Safety Modes
- **observe** (default) — Run the skill and log everything it does. No restrictions.
- **restricted** — Block network access and filesystem writes outside a temp directory.
- **honeypot** — Provide fake credentials and endpoints to see if the skill tries to exfiltrate.
## Output
The sandbox produces a JSON report with:
- All filesystem operations (reads, writes, deletes)
- All environment variable accesses
- All network connections attempted
- All subprocess calls
- Warnings for suspicious patterns
- A safety verdict (SAFE / SUSPICIOUS / DANGEROUS)
## Integration
Combine with the workflow orchestrator for automated pre-install checks:
```
scan skill → sandbox run → review report → install if safe → audit log
```
## Limitations
- Python skills only (JavaScript/shell support planned)
- Cannot catch all evasion techniques (obfuscated or delayed execution)
- Network monitoring requires the skill to use standard Python libraries
- Not a true OS-level sandbox (use Docker for that level of isolation)
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Testing a ClawHub skill before installing it on your real agent
- 1Testing a ClawHub skill before installing it on your real agent
- 2Checking if a downloaded script reads API keys or tokens
- 3Catching outbound network calls made by an unknown skill
- 4Running a skill with fake credentials to detect exfiltration attempts
- 5Generating a safety report before a team-wide skill rollout
Test untrusted skills in an isolated environment before installing.
Security Audits
These signals reflect official OpenClaw status values. A Suspicious status means the skill should be used with extra caution.
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