Auto Review LoopSAFE
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
Overview
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
65f365162a55OBSERVED · 2026-10-25Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned | |
| codex | mentioned | |
| copilot | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: auto-review-loop
description: Autonomous multi-round research review loop. In Copilot CLI it defaults to the native complementary rubber-duck subagent with host-event model evidence; elsewhere it uses Codex, while explicit external reviewer overrides remain available. Implements fixes and re-reviews until a policy-approved positive assessment or max rounds is reached.
argument-hint: "[topic-or-scope]"
allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit, Skill, Task, mcp__codex__codex, mcp__codex__codex-reply, mcp__manual_review__review, mcp__manual_review__review_reply, mcp__antigravity__antigravity, mcp__antigravity__antigravity-reply, mcp__grok__grok, mcp__grok__grok-reply, mcp__claude-review__review, mcp__claude-review__review_reply, mcp__claude-review__review_start, mcp__claude-review__review_reply_start, mcp__claude-review__review_status
---
# 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 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`, `gpt-6.1-sol`). 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 inline or project backend selection, 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. Inline or project selection of `codex`, `antigravity`/`gemini`, `grok`, `claude-code`, `oracle-pro`, `agy`, or `manual` bypasses the probe. 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` + **The selected reviewer reads the repo directly** (`codex exec` on the Codex route) (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 direct repository access and is not compatible with --reviewer: manual. Use difficulty: hard, or select a file-reading reviewer."
> 💡 Override: `/auto-review-loop "topic" — compact: true, human checkpoint: true, difficulty: hard`
## Reviewer Calling Convention
Resolve reviewer selection before any review: invocation `— reviewer:` →
project `.aris/reviewer.json` → host default. Use `reviewer_route.py` with
`--host claude-code --tier regular` (or actual Copilot host), resolved per
`integration-contract.md` §2, and [reviewer-backends.md](../shared-references/reviewer-backends.md).
Only the absence of both inline and project backend selection enters the
automatic host protocol below. Invalid configuration is `REVIEW_UNAVAILABLE`.
After the host default is determined, resolve any project model/Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
65f365162a55full audit observations/trust-audit/skill/wanshuiyin__auto-review-loop.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-25 | 65f365162a55 | SAFE | B | 89 | first audit |
Questions
What does the auto-review-loop — wanshuiyin/auto-claude-code-research-in-sleep skill do?
ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.
Is auto-review-loop — wanshuiyin/auto-claude-code-research-in-sleep safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can auto-review-loop — wanshuiyin/auto-claude-code-research-in-sleep access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does auto-review-loop — wanshuiyin/auto-claude-code-research-in-sleep work with?
Its documentation mentions claude-code, codex and copilot. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (65f365162a55), read on 2026-10-25. The repository is watched, and a new audit runs when it changes — this is the first audit.
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Shows this page's audit result and links back to it. It updates when the audit does.
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