safety-guardrails

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LLM safety guardrails and content moderation

⭐1stars🔀0forks📁View on GitHub🕐Updated Dec 31, 2025
[Guardrails and Safety]

When & Why to Use This Skill

This Claude skill provides a comprehensive safety framework for Large Language Models (LLMs), offering robust content moderation, input/output filtering, and PII protection. It ensures AI interactions remain safe, compliant, and aligned with organizational policies by enforcing strict topic boundaries and validating response formats to prevent harmful or off-topic outputs.

Use Cases

  • Case 1: Implementing real-time content moderation to block harmful, hateful, or violent prompts in public-facing AI applications and chatbots.
  • Case 2: Protecting user privacy by automatically identifying and filtering Personally Identifiable Information (PII) such as financial data and credentials from AI responses.
  • Case 3: Enforcing domain-specific topic boundaries to keep AI agents focused on professional tasks and prevent them from engaging in controversial or irrelevant discussions.
  • Case 4: Enhancing system security by validating output formats and preventing the generation of executable code or embedded commands that could lead to injection attacks.
namesafety-guardrails
descriptionLLM safety guardrails and content moderation
sasmp_version"1.3.0"
bonded_agent08-prompt-security-agent
bond_typePRIMARY_BOND

Safety Guardrails Skill

Bonded to: prompt-security-agent

Quick Start

Skill("custom-plugin-prompt-engineering:safety-guardrails")

Parameter Schema

parameters:
  safety_level:
    type: enum
    values: [permissive, standard, strict, maximum]
    default: standard

  content_filters:
    type: array
    values: [harmful, hate, violence, adult, pii]
    default: [harmful, hate, violence]

  output_validation:
    type: boolean
    default: true

Guardrail Types

Guardrail Purpose Implementation
Input filtering Block harmful requests Pattern matching
Output filtering Prevent harmful outputs Content analysis
Topic boundaries Stay on-topic Scope enforcement
Format validation Ensure safe formats Schema checking

Content Filtering

Categories

content_categories:
  harmful:
    - dangerous_activities
    - illegal_actions
    - self_harm

  hate_speech:
    - discrimination
    - slurs
    - targeted_harassment

  violence:
    - graphic_violence
    - threats
    - weapons_instructions

  pii:
    - personal_data
    - financial_info
    - credentials

Filter Implementation

## Content Guidelines

NEVER generate content that:
1. Provides instructions for harmful activities
2. Contains hate speech or discrimination
3. Describes graphic violence
4. Exposes personal information
5. Bypasses safety measures

If a request violates these guidelines:
1. Decline politely
2. Explain which guideline applies
3. Offer a safe alternative if possible

Output Validation

validation_rules:
  format_check:
    - valid_json_if_requested
    - no_executable_code_in_text
    - no_embedded_commands

  content_check:
    - no_pii_exposure
    - no_harmful_instructions
    - appropriate_for_audience

  consistency_check:
    - matches_role_constraints
    - within_topic_boundaries

Safe Response Patterns

Declining Harmful Requests

I can't help with that request because [reason].

Here's what I can help with instead:
- [Alternative 1]
- [Alternative 2]

Would any of these work for you?

Handling Edge Cases

I notice this request is [description of concern].

To ensure I'm being helpful in the right way:
1. Could you clarify [specific aspect]?
2. Here's a safe approach to [related task]:
   [Safe alternative]

Troubleshooting

Issue Cause Solution
Over-blocking Too strict Tune sensitivity
Under-blocking Too permissive Add patterns
False positives Ambiguous content Context-aware rules
Inconsistent Rule conflicts Prioritize rules

References

See: Anthropic Constitutional AI, OpenAI Moderation API