auto-review-loop

Original：🇺🇸 English

Translated

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.

16installs

Sourcewanshuiyin/auto-claude-code-research-in-sleep

Added on2026-03-19

NPX Install

npx skill4agent add wanshuiyin/auto-claude-code-research-in-sleep auto-review-loop

SKILL.md Content

View Translation Comparison →

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.

💡 Override:

/auto-review-loop "topic" — 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

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."
Read project narrative documents, memory files, and any prior review documents
Read recent experiment results (check output directories, logs)
Identify current weaknesses and open TODOs from prior reviews
Initialize round counter = 1 (unless recovered from state file)
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):

Code changes: Write/modify experiment scripts, model code, analysis scripts
Run experiments: Deploy to GPU server via SSH + screen/tmux
Analysis: Run evaluation, collect results, update figures/tables
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

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.

Increment round counter → back to Phase A.

Termination

When loop ends (positive assessment or max rounds):

Update
```
REVIEW_STATE.json
```
with
```
"status": "completed"
```
Write final summary to
```
AUTO_REVIEW.md
```
Update project notes with conclusions
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).
If stopped at max rounds without positive assessment:
- List remaining blockers
- Estimate effort needed for each
- Suggest whether to continue manually or pivot
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
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)
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.