auto-review-loop

Auto Review Loop: Autonomous Research Improvement

🔒 Do not wrap this skill in /loop, /schedule, or CronCreate. It already loops internally (review → fix → re-review) and the reviewer carries round-to-round memory in one threadId (codex-reply). An external timer re-enters from the top each tick — fresh threadId, reviewer memory reset — firing the verdict on wall-clock time instead of on artifact change: zero new signal, full token cost. If you want to schedule something, schedule the external wait that precedes it (experiments done → then run this once). See shared-references/external-cadence.md.

Autonomously iterate: review → implement fixes → re-review, until an independent reviewer gives a policy-approved positive assessment or MAX_ROUNDS is reached.

Context: $ARGUMENTS

Constants

• MAX_ROUNDS = 4
• POSITIVE_THRESHOLD: score >= 6/10 AND verdict ∈ {"ready", "almost"} — both must hold. This matches the operative Phase-E STOP CONDITION exactly; the verdict vocabulary is {"ready", "almost", "not ready"} (a high score with a "not ready" verdict does NOT stop the loop). Earlier wording here used or and a stale verdict set ("accept"/"sufficient"/"ready for submission") — that was an internal inconsistency; the AND form is authoritative.
• REVIEW_DOC: review-stage/AUTO_REVIEW.md (cumulative log) (fall back to ./AUTO_REVIEW.md for legacy projects)
• REVIEWER_MODEL = gpt-6-astra — Default model for the Codex backend. Must be an OpenAI model (e.g., gpt-6-astra, o3, gpt-4o). Manual backend uses a model the user chooses — it must be a recognized model from a different family (OpenAI, Anthropic, Google, DeepSeek, Moonshot/Kimi, Qwen).
• REVIEWER_BACKEND — With no reviewer directive, start as auto; Step -1 runs exactly one two-call native marker/challenge probe for the first review. A bound Copilot CLI root session uses copilot-native (built-in complementary rubber-duck subagent); an unbound/non-Copilot host keeps the existing codex default. Explicit — reviewer: codex, oracle-pro, agy, or manual bypasses the probe and selects that external backend. Explicit — reviewer: copilot retains the compatibility copilot --agent drive mode and its later Codex/manual finalizer. The native path gets both actual model IDs from host session events; it never needs COPILOT_CLI or caller-provided --executor-model. See shared-references/reviewer-routing.md.
• OUTPUT_DIR = review-stage/ — All review-stage outputs go here. Create the directory if it doesn't exist.
• HUMAN_CHECKPOINT = false — When true, pause after each round's review (Phase B) and present the score + weaknesses to the user. Wait for user input before proceeding to Phase C. The user can: approve the suggested fixes, provide custom modification instructions, skip specific fixes, or stop the loop early. When false (default), the loop runs fully autonomously.
• COMPACT = false — When true, (1) read EXPERIMENT_LOG.md and findings.md instead of parsing full logs on session recovery, (2) append key findings to findings.md after each round.
• REVIEWER_DIFFICULTY = medium — Controls how adversarial the reviewer is. Three levels:
  • medium (default): Current behavior — MCP-based review, the executor controls what context the reviewer sees.
  • hard: Adds Reviewer Memory (the reviewer tracks its own suspicions across rounds) + Debate Protocol (the executor can rebut, the reviewer rules).
  • nightmare: Everything in hard + Codex exec reviewer reads the repo directly via codex exec (the executor cannot filter what the reviewer sees) + Adversarial Verification (the reviewer independently checks if code matches claims).
• RENDER_HTML = true — When true (default), auto-render review-stage/AUTO_REVIEW.md to HTML on loop termination via /render-html. Uses --no-review (the loop itself IS the cross-model review; the HTML is a structural conversion). Set false to skip, or pass — render html: false.