collect-evidence

To collect normalized diagnostic evidence for cloud-native auditing, orchestrate session, project, asset, run, and feedback lanes to produce a standardized findings.json artifact. This ensures objective, non-interpretive observations for deterministic state tracking without manual intervention.

Standardizing fragmented project findings into a single report requires a neutral orchestration layer that gathers observations across multiple lanes. This approach yields a uniform findings.json artifact, ensuring consistent data handling and cross-platform compatibility for downstream analysis.

Yes, you need Node.js and Python3 to run automated evidence collection. A built-in capability diagnostic performs a doctor check before execution to verify Node.js availability, engine integrity, and toolchain compliance, ensuring deterministic evidence state tracking.

Yes, you can use this for deterministic state tracking across different cloud-native environments. It orchestrates normalized evidence collection across five distinct lanes, ensuring privacy-safe data handling and cross-platform compatibility to satisfy objective auditing requirements.

Cloud-native unit audits require non-interpretive observation gathering to maintain objective downstream analysis. Capturing structured, normalized evidence in a findings.json artifact prevents fragmented data and ensures privacy-safe, deterministic state tracking across your project.