Gws Modelarmor Sanitize Prompt
VerifiedGoogle Model Armor: Sanitize a user prompt through a Model Armor template.
$ Add to .claude/skills/ About This Skill
# modelarmor +sanitize-prompt
> PREREQUISITE: Read `../gws-shared/SKILL.md` for auth, global flags, and security rules. If missing, run `gws generate-skills` to create it.
Sanitize a user prompt through a Model Armor template
Usage
```bash gws modelarmor +sanitize-prompt --template <NAME> ```
Flags
| Flag | Required | Default | Description | |------|----------|---------|-------------| | `--template` | ✓ | — | Full template resource name (projects/PROJECT/locations/LOCATION/templates/TEMPLATE) | | `--text` | — | — | Text content to sanitize | | `--json` | — | — | Full JSON request body (overrides --text) |
Examples
```bash gws modelarmor +sanitize-prompt --template projects/P/locations/L/templates/T --text 'user input' echo 'prompt' | gws modelarmor +sanitize-prompt --template ... ```
Tips
- If neither --text nor --json is given, reads from stdin.
- For outbound safety, use +sanitize-response instead.
See Also
- gws-shared — Global flags and auth
- gws-modelarmor — All filter user-generated content for safety commands
Use Cases
- Sanitize user prompts through Google Model Armor templates before sending to LLMs
- Filter harmful or adversarial content from AI agent inputs
- Apply enterprise-grade prompt safety policies to AI applications
- Protect LLM-powered services from prompt injection attacks
- Enforce content safety standards across AI agent interactions
Pros & Cons
Pros
- +Compatible with multiple platforms including claude-code, openclaw
- +Well-documented with detailed usage instructions and examples
- +Purpose-built for ai & machine learning tasks with focused functionality
Cons
- -No built-in analytics or usage metrics dashboard
- -Configuration may require familiarity with ai & machine learning concepts
FAQ
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