output-guardrail-critic

Review AI agent guardrail configurations and execution results for coverage gaps, threshold calibration, and safety gate completeness

SKILL.md

GitHub

Output Guardrail Critic

When to Use

Use when you have an existing guardrail configuration or execution results and need an evidence-backed review before deployment. Best for finding coverage gaps, threshold calibration issues, and safety gate problems.

Trigger patterns:

  • "Review these guardrails"
  • "Are our output filters sufficient?"
  • "Check guardrail coverage for this agent"
  • "Review the safety gates before we deploy"

What It Does

Reviews guardrail configurations and/or execution results with a 6-phase investigation protocol covering:

  • Output quality gates (hallucination, citation, format, completeness)
  • Safety gates (secret leakage, PII exposure, harmful content, bias)
  • Runtime approval gates (human-in-the-loop coverage for high-consequence/irreversible actions)
  • Configuration coverage and threshold calibration
  • 3 perspectives: security engineer, end user, compliance officer

Invocation

/output-guardrail-critic

Or via meta-router:

/meta-critic "review guardrails for [agent description]"

Input

Provide one or both of:

  1. Guardrail configuration (YAML, JSON, or descriptive)
  2. Guardrail execution results (trigger logs, false positive reports)

Also helpful:

  • Agent purpose and deployment context
  • Compliance requirements
  • Known false positive complaints

Output

  • Quality gate and safety gate review tables
  • Configuration coverage assessment
  • Multi-perspective findings (security, user, compliance)
  • Severity-rated findings with evidence
  • Verdict: REJECT / REVISE / ACCEPT-WITH-RESERVATIONS / ACCEPT

Companion Skills

  • input-guardrail-planner — when REVISE/REJECT, route back for guardrail redesign
  • security-threat-model-planner — if the review reveals the agent needs a broader threat model
  • proposal-critic — for reviewing the overall guardrail strategy at a higher level