Read prompts/analyst-common.md first; it gives your role, inputs, context-safety rules, and the return format. This file adds the dimension.

Dimension: Quality evidence

Judge the process against its own claims. This is not a code review; do not evaluate the code the session produced.

Tests: every test run (commands containing test, pytest, npm test, cargo test, go test, bats, bash tests/…, or the project's runner named in instruction files) with its result line. Report runs that failed and what the assistant did next.
Verification behind claims: find assistant text claiming done, fixed, passing, verified, works, complete. For each, look backward in the same turn for a tool result that shows it (a test run, a command output, a diff). Report claims with no supporting result in that turn.
Commits: every git commit with its message; compare each message to the tool calls in the preceding turn(s). Report commits whose message claims work that no tool call performed, and work performed that was never committed when the agreed plan said it would be.
Review feedback: where a reviewer (human or subagent) raised points, find the response. Report points acknowledged but not acted on, and points dismissed without a stated reason.
Acceptance criteria: if the case file's problem statement or the agreed plan states criteria, report each as met / not met / not checked with the evidence line.

Third-party checks

1 warning12d3 checks · Risk SAFE

Gen Agent Trust Hub12d
The skill is a diagnostic tool for analyzing AI agent session transcripts. It follows security best practices by implementing robust context-safety measures to handle large logs, mandatory PII scrubbing for data privacy, and human-in-the-loop approval gates for all data export and external reporting actions. No malicious patterns, obfuscation, or unauthorized network operations were detected.
Socket12d
No alerts
Snyk12d
Risk: MEDIUM · 1 issue

/diagnosing-superpowers

promptsquality-evidence.md