What problem does it solve? ORC runs leave permanent behavior traces on disk, but raw trace files do not answer whether the scoring rubric is calibrated, whether workers ran at their assigned model tiers, or where the pipeline leaks. This Skill mines those traces and turns them into an actionable calibration report delivered upstream to the ORC repository. ## Core Features & Use Cases - Trace aggregation: Parses the closed verb set (SCORE, OUTCOME, VERIFY, GATE, FINDING, VERDICT) across run traces, preferring structured .jsonl sidecars, and aggregates per-band retries, requeues, downgrades, and gate bounce rates. - Read-only, report-only operation: Never edits the rubric, skills, config, or project code; it dispatches mining to a cheap subagent and validates the return against a strict contract. - Upstream delivery: Files the AI-readable report (YAML frontmatter plus human sections) as a PR, with issue fallback, to the configured retro_repo via the gh CLI or a GitHub MCP; a P0 preflight stops the run entirely if no delivery channel exists. - Use Case: After a week of ORC runs, ask how well the scoring is calibrated. The Skill mines all traces, reports per-band outcomes and two silent model downgrades, and opens a PR titled orc-retro: 130726 — 3 runs, 2 recommendations against the ORC repo. ## Quick Start Run /orc-retro to analyze the logged ORC traces and file a calibration report to the ORC repository.