Atlas / Skills / wanshuiyin / Auto Paper Improvement Loop

Auto Paper Improvement LoopSAFE

skills/wanshuiyin/auto-paper-improvement-loop

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.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
2 documented
License
MIT
Stars
17,956
01

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.

Read from source at commit 39436540fc79OBSERVED · 2026-10-23
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02

Host compatibility

What the documentation claims. We have not run a compatibility test.

HostStatusNotes
claude-codementioned
codexmentioned
03

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-paper-improvement-loop
description: "Autonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds. Use when user says \"改论文\", \"improve paper\", \"论文润色循环\", \"auto improve\", or wants to iteratively polish a generated paper."
argument-hint: "[paper-directory] [— style-ref: <source>] [— edit-whitelist <path>]"
allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Skill, mcp__codex__codex, mcp__codex__codex-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 Paper Improvement Loop: Review → Fix → Recompile

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):
> resolve invocation → `.aris/reviewer.json` → Claude Code default (Codex MCP)
> with `reviewer_route.py` through the canonical helper resolver, preserving this
> slot's tier, fresh contexts, source/image inputs and deterministic gates.
> Codex examples/pins below apply only to `backend=codex`; other routes use their
> own first-call/reply contract. Save the route and actual returned identity;
> unavailable selections are `REVIEW_UNAVAILABLE`. Classify acceptance from actual
> families/completion/scope. Forward invocation reviewer/model/effort overrides
> to reviewer-bearing children. Generation workers are unchanged.


> 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It
> already loops internally (review → fix → recompile) with its own round
> structure and a deliberate fresh-reviewer bias guard each round (no
> `codex-reply`). Re-asking it to "improve the paper" on a
> wall-clock timer produces no new signal — quality changes when the *review*
> changes, not when the clock ticks — and a timed re-run that also accepts its
> own output to decide when to stop crosses into self-acquittal
> (`acceptance-gate.md`). Schedule the *external wait that precedes it*, not the
> improvement loop. See
> [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).

Autonomously improve the paper at: **$ARGUMENTS**

## Context

This skill is designed to run **after** Workflow 3 (`/paper-plan` → `/paper-figure` → `/paper-write` → `/paper-compile`). It takes a compiled paper and iteratively improves it through external LLM review.

Unlike `/auto-review-loop` (which iterates on **research** — running experiments, collecting data, rewriting narrative), this skill iterates on **paper writing quality** — fixing theoretical inconsistencies, softening overclaims, adding missing content, and improving presentation.

## Constants

- **MAX_ROUNDS = 2** — Two rounds of review→fix→recompile. Empirically, Round 1 catches structural issues (4→6/10), Round 2 catches remaining presentation issues (6→7/10). Diminishing returns beyond 2 rounds for writing-only improvements.
- **REVIEWER_MODEL = `gpt-6-astra`** — Model used via Codex MCP for paper review.
- **REVIEWER_BIAS_GUARD = true** — When `true`, every review round uses a fresh `mcp__codex__codex` thread with no prior review context. Never use `mcp__codex__codex-reply` for review rounds. Set to `false` only for deliberate debugging of the legacy behavior. **Empirical evidence:** running the same paper with `codex-reply` + "since last round we did X" prompts inflated scores from real 3/10 → fake 8/10 across multiple rounds; switching to fresh threads recovered the true 3/10 assessment.
- **REVIEW_LOG = `PAPER_IMPROVEMENT_LOG.md`** — Cumulative log of all rounds, stored in paper directory.
- **HUMAN_CHECKPOINT = false** — When `true`, pause after each round's review and present score + weaknesses to the user. The user can approve fixes, provide custom modification instructions, skip specific fixes, or stop early. When `false` (default), runs fully autonomously.
- **EDIT_WHITELIST = `null`** — Optional path to a YAML/JSON whitelist file constraining which paths and operations the fix-implementation step may touch. When `null` (default), all edits proceed unconstrained. When set via `— edit-whitelist <path>` (also accepts `— edit_whitelist <path>`), the loop loads the file at startup and consults it before each edit; rejected edits are logged to `PAPER_IMPROVEMENT_LOG.md` rather than silently dropped. See "Optional: Edit Whitelist" below.

> 💡 Override: `/auto-paper-improvement-loop "paper/" — human checkpoint: true`

## Optional: Style reference (`— style-ref: <source>`, opt-in)

Lets the user steer **structural fixes only** during improvement (section reordering hints, paragraph length nudges, figure density adjustments) toward a reference paper. **Default OFF — when the user does not pass `— style-ref`, do nothing differently from before.**

Only when `— style-ref: <source>` appears in `$ARGUMENTS`, run the helper FIRST, before the loop starts:

```bash
# Resolve $STYLE_HELPER via the canonical strict-safe chain (see
# shared-references/integration-contract.md §2). Policy A — gate:
# unresolved helper means --style-ref cannot be satisfied, so abort.
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true
fi
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
    ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
STYLE_HELPER=".aris/tools/extract_paper_style.py"
[ -f "$STYLE_HELPER" ] || STYLE_HELPER="tools/extract_paper_style.py"
[ -f "$STYLE_HELPER" ] || { [ -n "${ARIS_REPO:-}" ] && STYLE_HELPER="$ARIS_REPO/tools/extract_paper_style.py"; }
[ -f "$STYLE_HELPER" ] || {
  echo "ERROR: extract_paper_style.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2
  echo "       
04

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.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (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.

Audited 2026-10-23 · audit v0.4.1 · source sha 39436540fc79full audit observations/trust-audit/skill/wanshuiyin__auto-paper-improvement-loop.json · Report an issue / request a re-scan
05

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-2339436540fc79SAFEB89first audit
06

Questions

What does the auto-paper-improvement-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-paper-improvement-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-paper-improvement-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-paper-improvement-loop — wanshuiyin/auto-claude-code-research-in-sleep work with?

Its documentation mentions claude-code and codex. 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 (39436540fc79), read on 2026-10-23. The repository is watched, and a new audit runs when it changes — this is the first audit.

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