| name | guardrail-monitor |
| description | The system's value alignment and truth verification layer, ensuring output safety and epistemic integrity. |
Guardrail Monitor Skill
The Guardrail Monitor is a tripartite engine designed to prevent AI drift and ensure that outputs align with both user values and empirical ground truth. It consists of three specialized modules: Pathos, Pheme, and Kratos.
Core Modules
Pathos — Value & Saliency Tracking
Pathos monitors the "emotional and ethical salience" of the interaction. It identifies what the user cares about and flags when a proposed output might conflict with those priorities.
- Function: Extracts value categories (Safety, Accuracy, Privacy, Ethics, Autonomy) and calculates their salience.
- Saliency Score: 0-1, where 1.0 indicates a critical, non-negotiable priority.
Pheme — Ground Truth Verification
Pheme provides a mechanism for verifying claims against trusted external sources.
- Function: Cross-references claims across multiple sources and weights them by reliability.
- Statuses:
VERIFIED (sufficient agreement), CONTRADICTED (high-reliability contradiction), or UNVERIFIABLE.
Kratos — Authority & Conflict Arbitration
Kratos resolves contradictions between conflicting sources using a hierarchical authority model.
- Function: Assigns precedence based on source type (e.g., Peer-Reviewed $\rightarrow$ Government $\rightarrow$ Encyclopedia $\rightarrow$ Social Media).
- Domain Rules: Applies specialized precedence rules for specific fields like Medical, Legal, or Scientific.
Commands
check_value_saliency
Analyzes a topic or decision context against tracked user values.
- Arguments:
topic (required), decision_context (optional), user_values (optional).
- Behavior: Identifies relevant values, calculates a saliency score, and detects value-level conflicts.
verify_ground_truth
Verifies a claim using a list of sources.
- Arguments:
claim (required), sources (optional), require_min_sources (optional).
- Behavior: Returns a confidence score and a status based on source agreement and reliability.
arbitrate_conflict
Resolves a conflict between two competing claims.
- Arguments:
claimA, claimB, sourceA, sourceB (required), domain (optional).
- Behavior: Uses authority precedence and domain-specific rules to determine the "winner" and provide reasoning.
Implementation Details
- Architecture: Two-tier Python implementation (FastMCP $\rightarrow$ GuardrailLogic).
- Integrated Logic: Combines value extraction, reliability scoring, and authority hierarchies.
