# Auto Review Loop

> Autonomous multi-round research review loop. Repeatedly reviews via Codex MCP, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says "auto review loop", "review until it passes", or wants autonomous iterative improvement.

- Skill: `qhjqhj00/auto-review-loop` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/auto-review-loop`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/auto-review-loop/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/auto-review-loop

---


# Auto Review Loop: Autonomous Research Improvement

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, or verdict contains "accept", "sufficient", "ready for submission"
- REVIEW_DOC: `AUTO_REVIEW.md` in project root (cumulative log)
- REVIEWER_MODEL = `gpt-5.4` — Model used via Codex MCP. Must be an OpenAI model (e.g., `gpt-5.4`, `o3`, `gpt-4o`)
- **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.

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

## State Persistence (Compact Recovery)

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

```json
{
  "round": 2,
  "threadId": "019cd392-...",
  "status": "in_progress",
  "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.

## Workflow

### Initialization

1. **Check for `REVIEW_STATE.json`** in project root:
   - If it does not exist: **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 `AUTO_REVIEW.md` to restore full context of prior rounds
     - 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 `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 `AUTO_REVIEW.md` with header and timestamp

### Loop (repeat up to MAX_ROUNDS)

#### Phase A: Review

Send comprehensive context to the external reviewer:

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

    [Full research context: claims, methods, results, known weaknesses]
    [Changes since last round, if any]

    Please act as a senior ML reviewer (NeurIPS/ICML level).

    1. Score this work 1-10 for a top venue
    2. List remaining critical weaknesses (ranked by severity)
    3. For each weakness, specify the MINIMUM fix (experiment, analysis, or reframing)
    4. State clearly: is this READY for submission? Yes/No/Almost

    Be brutally honest. If the work is ready, say so clearly.
```

If this is round 2+, use `mcp__codex__codex-reply` with the saved threadId to maintain conversation context.

#### Phase B: Parse Assessment

**CRITICAL: Save the FULL raw response** from the external reviewer verbatim (store in a variable for Phase E). Do NOT discard or summarize — the raw text is the primary record.

Then extract structured fields:
- **Score** (numeric 1-10)
- **Verdict** ("ready" / "almost" / "not ready")
- **Action items** (ranked list of fixes)

**STOP CONDITION**: If score >= 6 AND verdict contains "ready" or "almost" → stop loop, document final state.

#### Human Checkpoint (if enabled)

**Skip this step entirely if `HUMAN_CHECKPOINT = false`.**

When `HUMAN_CHECKPOINT = true`, present the review results and wait for user input:

```
📋 Round N/MAX_ROUNDS review complete.

Score: X/10 — [verdict]
Top weaknesses:
1. [weakness 1]
2. [weakness 2]
3. [weakness 3]

Suggested fixes:
1. [fix 1]
2. [fix 2]
3. [fix 3]

Options:
- Reply "go" or "continue" → implement all suggested fixes
- Reply with custom instructions → implement your modifications instead
- Reply "skip 2" → skip fix #2, implement the rest
- Reply "stop" → end the loop, document current state
```

Wait for the user's response. Parse their input:
- **Approval** ("go", "continue", "ok", "proceed"): proceed to Phase C with all suggested fixes
- **Custom instructions** (any other text): treat as additional/replacement guidance for Phase C. Merge with reviewer suggestions where appropriate
- **Skip specific fixes** ("skip 1,3"): remove those fixes from the action list
- **Stop** ("stop", "enough", "done"): terminate the loop, jump to Termination

#### Feishu Notification (if configured)

After parsing the score, check if `~/.claude/feishu.json` exists and mode is not `"off"`:
- Send a `review_scored` notification: "Round N: X/10 — [verdict]" with top 3 weaknesses
- If **interactive** mode and verdict is "almost": send as checkpoint, wait for user reply on whether to continue or stop
- If config absent or mode off: skip entirely (no-op)

#### Phase C: Implement Fixes (if not stopping)

For each action item (highest priority first):

1. **Code changes**: Write/modify experiment scripts, model code, analysis scripts
2. **Run experiments**: Deploy to GPU server via SSH + screen/tmux
3. **Analysis**: Run evaluation, collect results, update figures/tables
4. **Documentation**: Update project notes and review document

Prioritization rules:
- Skip fixes requiring excessive compute (flag for manual follow-up)
- Skip fixes requiring external data/models not available
- Prefer reframing/analysis over new experiments when both address the concern
- Always implement metric additions (cheap, high impact)

#### Phase D: Wait for Results

If experiments were launched:
- Monitor remote sessions for completion
- Collect results from output files and logs
- **Training quality check** — if W&B is configured, invoke `/training-check` to verify training was healthy (no NaN, no divergence, no plateau). If W&B not available, skip silently. Flag any quality issues in the next review round.

#### Phase E: Document Round

Append to `AUTO_REVIEW.md`:

```markdown
## Round N (timestamp)

### Assessment (Summary)
- Score: X/10
- Verdict: [ready/almost/not ready]
- Key criticisms: [bullet list]

### Reviewer Raw Response

<details>
<summary>Click to expand full reviewer response</summary>

[Paste the COMPLETE raw response from the external reviewer here — verbatim, unedited.
This is the authoritative record. Do NOT truncate or paraphrase.]

</details>

### Actions Taken
- [what was implemented/changed]

### Results
- [experiment outcomes, if any]

### Status
- [continuing to round N+1 / stopping]
```

**Write `REVIEW_STATE.json`** with current round, threadId, score, verdict, and any pending experiments.

**Append to `findings.md`** (when `COMPACT = true`): one-line entry per key finding this round:

```markdown
- [Round N] [positive/negative/unexpected]: [one-sentence finding] (metric: X.XX → Y.YY)
```

Increment round counter → back to Phase A.

### Termination

When loop ends (positive assessment or max rounds):

1. Update `REVIEW_STATE.json` with `"status": "completed"`
2. Write final summary to `AUTO_REVIEW.md`
3. Update project notes with conclusions
4. **Write method/pipeline description** to `AUTO_REVIEW.md` under a `## Method Description` section — a concise 1-2 paragraph description of the final method, its architecture, and data flow. This serves as input for `/paper-illustration` in Workflow 3 (so it can generate architecture diagrams automatically).
5. **Generate claims from results** — invoke `/result-to-claim` to convert experiment results from `AUTO_REVIEW.md` into structured paper claims. Output: `CLAIMS_FROM_RESULTS.md`. This bridges Workflow 2 → Workflow 3 so `/paper-plan` can directly use validated claims instead of extracting them from scratch. If `/result-to-claim` is not available, skip silently.
6. If stopped at max rounds without positive assessment:
   - List remaining blockers
   - Estimate effort needed for each
   - Suggest whether to continue manually or pivot
5. **Feishu notification** (if configured): Send `pipeline_done` with final score progression table

## Key Rules

- **Large file handling**: If the Write tool fails due to file size, immediately retry using Bash (`cat << 'EOF' > file`) to write in chunks. Do NOT ask the user for permission — just do it silently.

- ALWAYS use `config: {"model_reasoning_effort": "xhigh"}` for maximum reasoning depth
- Save threadId from first call, use `mcp__codex__codex-reply` for subsequent rounds
- **Anti-hallucination citations**: When adding references during fixes, NEVER fabricate BibTeX. Use the same DBLP → CrossRef → `[VERIFY]` chain as `/paper-write`: (1) `curl -s "https://dblp.org/search/publ/api?q=TITLE&format=json"` → get key → `curl -s "https://dblp.org/rec/{key}.bib"`, (2) if not found, `curl -sLH "Accept: application/x-bibtex" "https://doi.org/{doi}"`, (3) if both fail, mark with `% [VERIFY]`. Do NOT generate BibTeX from memory.
- Be honest — include negative results and failed experiments
- Do NOT hide weaknesses to game a positive score
- Implement fixes BEFORE re-reviewing (don't just promise to fix)
- **Exhaust before surrendering** — before marking any reviewer concern as "cannot address": (1) try at least 2 different solution paths, (2) for experiment issues, adjust hyperparameters or try an alternative baseline, (3) for theory issues, provide a weaker version of the result or an alternative argument, (4) only then concede narrowly and bound the damage. Never give up on the first attempt.
- If an experiment takes > 30 minutes, launch it and continue with other fixes while waiting
- Document EVERYTHING — the review log should be self-contained
- Update project notes after each round, not just at the end

## Prompt Template for Round 2+

```
mcp__codex__codex-reply:
  threadId: [saved from round 1]
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    [Round N update]

    Since your last review, we have:
    1. [Action 1]: [result]
    2. [Action 2]: [result]
    3. [Action 3]: [result]

    Updated results table:
    [paste metrics]

    Please re-score and re-assess. Are the remaining concerns addressed?
    Same format: Score, Verdict, Remaining Weaknesses, Minimum Fixes.
```