Audithub Orca Call Metrics Analyzer

OrCa call metrics analyzer

Overview

Turn OrCa metrics into a prioritized explanation of what the fuzzer can and cannot currently exercise.

Required reference

Read ../references/call-metrics-and-tuning.md.

Sub-agent use

This skill is a strong default candidate for sub-agent execution because call_metrics.json can be large. When used as a sub-agent, return normalized analysis instead of raw metrics:

• top blockers and strategic functions
• dominant revert reasons/selectors and suspected source checks
• hint usage or hint failure issues
• functions that look healthy enough to leave alone
• concrete tuning hypotheses with expected validation metrics

For later campaign stages, save the full raw metrics to orca_config/analysis/call_metrics_<run>.json or another local artifact, then pass only this summary back to the orchestrator. If comparing runs, read both metrics files locally and return a compact diff of changed success rates, new blockers, resolved blockers, and hint effectiveness.

Workflow

1. Parse call_metrics.json; do not rely on visual inspection when structured parsing is available.
2. Rank functions by low success rate, high revert volume, and strategic importance.
3. Review dominant revert messages/selectors and map them to source checks.
4. Identify likely blockers: arguments, access, balances, approvals, state ordering, or bad setup.
5. Review hint usage and hint failures when hints exist.
6. Store normalized analysis in orca_config/analysis/call_metrics_<run>.json when writing artifacts.

Output

Report:

• top poorly performing functions and success rates
• dominant revert reasons and suspected source checks
• functions that are acceptable as-is and why
• hint usage issues
• concrete tuning hypotheses for audithub-orca-setup-tuner

Treat low success as a signal for investigation, not an automatic defect. Preserve fuzzing breadth in recommendations.