/research-review

name: research-review
description: Get a deep critical review of research from a selected reviewer (Codex by default, Gemini, Grok, Claude Code, or manual). Use when user says "review my research", "help me review", "get external review", or wants critical feedback on research ideas, papers, or experimental results.
argument-hint: "[topic-or-scope]"
allowed-tools: Bash(*), Read, Grep, Glob, Write, Edit, 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

Research Review via External Reviewer Backend (ultra reasoning)

> 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** It is > verdict-bearing — it produces a cross-model review verdict, multi-round with > reviewer thread continuity. An external timer re-fires the verdict on > wall-clock time and breaks the reviewer's round-to-round memory: zero new > signal, full token cost. Schedule the *external wait that precedes it* (work > ready → then review once), not the verdict. See > [`shared-references/external-cadence.md`](../shared-references/external-cadence.md).

Get a multi-round critical review of research work from the selected external reviewer backend with maximum reasoning depth.

Constants

• REVIEWER_MODEL = `gpt-6-astra` — Default model for the Codex backend, reasoning effort `ultra` (deep-audit tier). 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 = `codex`** — Default: Codex MCP (ultra). Resolve `— reviewer:`, project `.aris/reviewer.json`, then this default using `reviewer_route.py` (`--host claude-code --tier deep`), per [reviewer-backends.md](../shared-references/reviewer-backends.md). Gemini/Antigravity, Grok, and Claude Code use that contract at every call below. Legacy Oracle/manual contracts remain in `reviewer-routing.md`; unavailable selected routes do not silently fall back.

Reviewer Calling Convention

When calling the reviewer, branch on REVIEWER_BACKEND:

**If REVIEWER_BACKEND ∈ {antigravity, grok, claude-code}:** Use the selected first/reply protocol in `reviewer-backends.md`, with the same review prompt, explicit cwd containing the primary files, and resolved provider-specific model/effort. Save route, cwd and session id for continuation. A Claude Code bridge may run DeepSeek; derive family from its reported model.

**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", "executor_model": "<actual executor model>", "require_reviewer_model": true} 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", "executor_model": "<actual executor model>", "require_reviewer_model": true}

Content fidelity: the manual reviewer should see the same substantive review brief Codex would read. If the manual UI supports file upload / attachment, reuse the same brief file; otherwise paste the brief contents inline because remote web UIs cannot read your local filesystem paths. Review tracing applies equally to both backends.

Context: $ARGUMENTS

Prerequisites

• The selected MCP must be available. For the default **Codex MCP Server**:

  claude mcp add codex -s user -- python3 "$HOME/aris_repo/mcp-servers/codex-exec/server.py"   # your ARIS clone's path

• This gives Claude Code access to `mcp__codex__codex` and `mcp__codex__codex-reply` tools

Workflow

Step 1: Gather Research Context

Before calling the external reviewer, compile a comprehensive briefing: 1. Read project narrative documents (e.g., STORY.md, README.md, paper drafts) 2. Read any memory/notes files for key findings and experiment history 3. Identify: core claims, methodology, key results, known weaknesses

Step 2: Initial Review (Round 1)

Send a detailed prompt with ultra reasoning, using the selected backend. For the `codex` backend, keep the MCP payload short: write the full briefing to `RESEARCH_REVIEW_REQUEST.md`, then point Codex at that file.

*For codex backend:*

mcp__codex__codex:
  model: gpt-6-astra
  config: {"model_reasoning_effort": "ultra"}
  prompt: |
    Read the review brief at <absolute path to RESEARCH_REVIEW_REQUEST.md>.
    Executor notes are not evidence beyond the files they cite, so verify the
    referenced artifacts before judging.
    Please act as a senior ML reviewer (NeurIPS/ICML level). Start from the
    assumption that the work is broken somewhere — your job is to find where.
    Be adversarial. Trust nothing the author tells you — verify everything
    yourself. Identify:
    1. Logical gaps or unjustified claims
    2. Missing experiments that would strengthen the story
    3. Narrative weaknesses
    4. Whether the contribution is sufficient for a top venue

    === SCOPE LIMITS (these bound what you PROPOSE, never what you look for) ===
    Report anything that is actually wrong here — including a rare-looking case, if
    this repo actually produces it. Then keep the fix in scope:
    1. This is a RESEARCH-WORKFLOW tool, not a security paper. Verification is
       welcome; over-defense is not. Assume a cooperating operator on their own
       machine — a malicious l