peer-review

1# Peer Review
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3Classify → gather context → **run bundled adversarial-review script** → root cause → fix/report → repeat until clean.
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5"Owner" below means whoever owns the work, usually the user.
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7Prerequisites: Linux, bash, python3, jq, GNU `date`, `timeout`, `sha1sum`, `pgrep` and GNU `xargs`, Sno's quota pick and answer-provider commands, plus an installed, logged-in reviewer CLI on another vendor. Without python3 the findings ledger and receipts do not work.
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9The bundled script's adversarial-review pass is the authoritative reviewer. It runs in a fresh, separate headless session that has none of the caller's conversation. Only an answer from a different model vendor counts: the script walks Sno's ordered reviewer candidates and discards failed, empty, unknown-vendor or same-vendor answers. There is no same-vendor fallback. The active agent gathers context, investigates root causes, and applies fixes — it never overrides, filters, softens, or rewords the nested reviewer's findings. Every authoritative finding reaches the user; tag suspected false positives `[Possible FP]` with your reasoning instead of dropping them. Findings the active agent adds on its own are labeled "Active-agent supplementary" and kept clearly separate.
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11Works for both code and documents: classify first, then load the right verification layers. Code gets a root-cause-driven fix loop; documents get a structured report with optional edits.
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13## Review Calibration
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