What problem does it solve? Understanding what a codebase can reveal about itself in production requires reading scattered logging calls, metric definitions, tracing setup, and exporter configuration across the entire repository. This Skill automates that audit, producing a structured findings report on what signals exist, where telemetry data flows, what monitoring is possible, and which failure modes stay invisible. ## Core Features & Use Cases - Emission Surface Inventory: Greps for logging, metrics, tracing, and error-reporting vocabulary across Rust, JavaScript/TypeScript, and Python ecosystems, then reads initialization and exporter code closely. - Blind Spot Detection: Probes load-bearing paths for swallowed errors and silent failures, and infers what alerting and dashboards the emitted signals can or cannot support. - Privacy and Hygiene Audit: Flags sensitive data in logs, unbounded metric cardinality, telemetry defaults, and redaction gaps with calibrated severity levels. - Use Case: Point it at a repository with a Sokrates analysis to receive a validated observability-scan.json report plus an interactive HTML explorer summarizing the system's monitoring posture. ## Quick Start Run an observability scan on this repository and tell me what monitoring and alerting its telemetry can support and where its blind spots are.
