input classification

This skill appears internally consistent and fits its stated purpose, but review a few operational details before installing: - Data privacy: the skill logs 'full context' and forwards clarified_input/original_input to downstream systems. Confirm where logs and downstream queues are stored, who can access them, and whether inputs may contain PII or secrets. Ask whether logs are encrypted, whether inputs are redacted or hashed (and if hashes are salted) before long-term retention. - Retention and audit: the documentation prescribes multi-tier log retention (DEBUG 7d ... AUDIT 2y). Ensure those retention policies align with your data governance and that audit logs do not retain sensitive content longer than allowed. - Integration endpoints and authorization: the SKILL.md references JSON handoffs to Clarification, Task Decomposition, Human Review, and queues. Confirm the actual endpoints, authentication, and transport security used in your deployment so classification results are not leaked to unauthorized systems. - Behavior guarantees: the skill claims deterministic 100% single-category classification and tight latency (<100ms). Those are strong guarantees — validate on representative inputs (including ambiguous/edge cases) to ensure the tie-breaking/escalation behavior works as intended and does not produce forced misclassifications. - Escalation and human review: confirm human-review workflows and who can override classifications; verify audit trails record overrides as specified. If these operational questions are answered satisfactorily (privacy controls, secure endpoints, and realistic performance expectations), the skill is appropriate to install. If you cannot confirm log handling or downstream endpoints, treat the integration as a potential privacy risk and seek remediation before enabling it.