domains / families / agent-tooling / agenttrace-session-audit

agenttrace-session-audit

combined

Audit local AI coding-agent sessions with agenttrace for cost, tokens, tool failures, latency, anomalies, health, diffs, and CI gates. Audit local AI coding-agent sessions with agenttrace for cost, tool failures, latency, anomalies, health, diffs, and CI gates.

When to use

• Use when a user asks why an AI coding run was slow, expensive, shallow, or unreliable.
• Use when reviewing local agent logs before retrying a failed or suspicious task.
• Use when building a lightweight CI health gate for AI-assisted coding sessions.
• Use when comparing two attempts and looking for changed tool paths, retries, or cost patterns.

When not to use

• agenttrace can only analyze logs that are present locally or provided as exports.
• Some agents do not expose enough fields to infer cost, model, cache use, or latency.
• Healthy trace metrics do not prove the final code is correct; still run tests and review diffs.
• CI gates should start as advisory until the team understands normal baseline behavior.

Skill page

Conflict

The two skills cover the same core workflow (discovery, reporting, CI gates) but contain distinct, non-overlapping material. The first copy (davepoon) provides specific guardrails regarding 'capability levels' (Detailed/Aggregate/Limited) and treats Tool Steps as metadata-only, which is a critical constraint for interpreting data fidelity. The second copy (sickn33) adds a 'Compare Attempts' step for semantic drift detection, specific 'When to Use' triggers, and a 'Common Pitfalls' section. Additionally, they disagree on the fallback command for the source repository (`cargo run` vs `go run`). Crowning one would hide either the data-fidelity constraints or the semantic-drift workflow.