Guardrails Safety Filter Builder
This skill builds custom AI safety filters to proactively manage responses and mitigate potential risks, ensuring more reliable outputs.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add guardrails-safety-filter-builder npx -- -y @trustedskills/guardrails-safety-filter-builder
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"guardrails-safety-filter-builder": {
"command": "npx",
"args": [
"-y",
"@trustedskills/guardrails-safety-filter-builder"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
guardrails-safety-filter-builder
What it does
This skill allows you to programmatically construct and deploy safety filters for AI agents. It enables the definition of specific constraints to prevent harmful outputs or unauthorized actions within your agent's workflow.
When to use it
- You need to enforce strict content policies on an LLM before it generates a response.
- Your application requires dynamic filtering rules that change based on user context or session data.
- You are building an enterprise agent where regulatory compliance and safety are mandatory requirements.
- You want to customize standard safety protocols to fit niche use cases without modifying core model weights.
Key capabilities
- Define custom safety constraints programmatically.
- Deploy filters directly into the agent's inference pipeline.
- Create modular rule sets for different operational environments.
Example prompts
- "Create a safety filter that blocks any response containing personal identifiable information (PII) and suggests a redaction template instead."
- "Build a guardrail to prevent the agent from executing code unless it has been verified by a human reviewer first."
- "Construct a filter that detects toxic language patterns and automatically triggers a moderation workflow before replying."
Tips & gotchas
Ensure you test your custom filters against edge cases to avoid false positives that might block legitimate user queries. Remember that these filters operate as pre-processing or post-processing layers; they cannot alter the underlying model's reasoning capabilities, only its output visibility and execution permissions.
Tags
TrustedSkills Verification
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Security Audits
| Gen Agent Trust Hub | Pass |
| Socket | Pass |
| Snyk | Pass |
🌐 Community
Passed automated security scans.