SKILL.md
Configuring Guardrails
A guardrail is a safety and compliance check applied to data flowing through a Celigo integration. Guardrails are stored as imports with adaptorType: "GuardrailImport" and accessed via the /v1/imports API, but they have a dedicated page in the Celigo UI.
Guardrails handle three concerns:
- Data validation -- check records against rules before they reach downstream systems (PII detection, content moderation, or custom AI-based evaluation)
- Confidence tuning -- control sensitivity via
confidenceThreshold(0 to 1, default 0.7). Lower values catch more issues but increase false positives - PII masking -- optionally return a redacted copy of the record (
pii.mask: true) under amaskedresponse field. Masking is NOT automatic -- see PII: mask vs flag
No _connectionId is required unless using BYOK credentials for the ai_agent type. Platform-managed credentials cover most use cases.
Guardrails are used across flows, APIs, and tools.
Guardrail or AI agent step? Pick by what the LLM produces. A guardrail renders a verdict (flagged: true|false) that the parent's routing branches on -- reach for it when the user says "verify / check / validate / flag / screen". An AI agent step does work whose output flows onward as data -- reach for it when the user says "classify / extract / generate / summarize / decide"; build that in configuring-ai-agents.