auto-review-loop

What this skill does

# 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`](../shared-references/external-cadence.md).

Autonomously iterate: review → implement fixes → re-review, until the external reviewer gives a 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-5.5` — Default model for the Codex backend. Must be an OpenAI model (e.g., `gpt-5.5`, `o3`, `gpt-4o`). Manual backend uses whatever model the user chooses.
- **REVIEWER_BACKEND = `codex`** — Default: Codex MCP (xhigh). Override with `— reviewer: oracle-pro` for Oracle MCP, or `— reviewer: manual` for Manual Review MCP. If manual-review MCP is unavailable, stop and print the install command; do not fall back to Codex. 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`.

> ⚠️ **Nightmare + Manual incompatibility**: If `REVIEWER_BACKEND = manual` and `REVIEWER_DIFFICULTY = nightmare`, STOP with:
> "difficulty: nightmare requires Codex CLI / codex exec and is not compatible with --reviewer: manual. Use difficulty: hard, or switch reviewer to codex."

> 💡 Override: `/auto-review-loop "topic" — compact: true, human checkpoint: true, difficulty: hard`

## Reviewer Calling Convention

When calling the reviewer, branch on REVIEWER_BACKEND:

**If REVIEWER_BACKEND = `codex`:**
  Use `mcp__codex__codex` for new review threads.
  Use `mcp__codex__codex-reply` for follow-up rounds (reuse threadId).

**If REVIEWER_BACKEND = `manual`:**
  Use `mcp__manual_review__review` for new review threads with:
    prompt: [exact same prompt that would go to Codex]
    config: {"model_reasoning_effort": "xhigh"}
  Save the returned `threadId`.
  Use `mcp__manual_review__review_reply` for follow-up rounds with:
    threadId: [saved manual-review threadId]
    prompt: [follow-up prompt]
    config: {"model_reasoning_effort": "xhigh"}

Prompt fidelity: the manual prompt must be exactly the same text that Codex would receive.
Review tracing applies equally to both backends.

## State Persistence (Compact Recovery)

Long-running loops may hit the context window limit, triggering automatic compaction. To survive this, persist state to `review-stage/REVIEW_STATE.json` after each round:

```json
{
  "round": 2,
  "threadId": "019cd392-...",
  "status": "in_progress",
  "difficulty": "medium",
  "last_score": 5.0,
  "last_verdict": "not ready",
  "pending_experiments": ["screen_name_1"],
  "timestamp": "2026-03-13T21:00:00"
}
```

**Write this file at the end of every Phase E** (after documenting the round). Overwrite each time — only the latest state matters.

**On completion** (positive assessment or max rounds), set `"status": "completed"` so future invocations don't accidentally resume a finished loop.

## Output Protocols

> Follow these shared protocols for all output files:
> - **[Output Versioning Protocol](../shared-references/output-versioning.md)** — write timestamped file first, then copy to fixed name
> - **[Output Manifest Protocol](../shared-references/output-manifest.md)** — log every output to MANIFEST.md
> - **[Output Language Protocol](../shared-references/output-language.md)** — respect the project's language setting

## Workflow

### Initialization

1. **Check for `review-stage/REVIEW_STATE.json`** *(fall back to `./REVIEW_STATE.json` if not found — legacy path)*:
   - If neither path exists: **fresh start** (normal case, identical to behavior before this feature existed)
   - If it exists AND `status` is `"completed"`: **fresh start** (previous loop finished normally)
   - If it exists AND `status` is `"in_progress"` AND `timestamp` is older than 24 hours: **fresh start** (stale state from a killed/abandoned run — delete the file and start over)
   - If it exists AND `status` is `"in_progress"` AND `timestamp` is within 24 hours: **resume**
     - Read the state file to recover `round`, `threadId`, `last_score`, `pending_experiments`
     - Read `review-stage/AUTO_REVIEW.md` to restore full context of prior rounds *(fall back to `./AUTO_REVIEW.md`)*
     - If `pending_experiments` is non-empty, check if they have completed (e.g., check screen sessions)
     - Resume from the next round (round = saved round + 1)
     - Log: "Recovered from context compaction. Resuming at Round N."
2. Read project narrative documents, memory files, and any prior review documents. **When `COMPACT = true` and compact files exist**: read `findings.md` + `EXPERIMENT_LOG.md` instead of full `review-stage/AUTO_REVIEW.md` and raw logs — saves context window.
3. Read recent experiment results (check output directories, logs)
4. Identify current weaknesses and open TODOs from prior reviews
5. Initialize round counter = 1 (unless recovered from state file)
6. Create/update `review-stage/AUTO_REVIEW.md` with header and timestamp

### Loop (repeat up to MAX_ROUNDS)

#### Phase A: Review

**Route by REVIEWER_DIFFICULTY:**

##### Medium (default) — MCP Review

Send comprehensive context to the external reviewer using the selected backend.

*For codex backend:*

```
mcp__codex__codex:
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    [Round N/MAX_ROUNDS of autonomous review loop]

    Review the work directly from its artifacts — executor notes are not
    evidence, so read the f