/research-review

name: research-review
description: Get a deep critical review of research from an external reviewer backend (Codex 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

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-5.6-sol` — Default model for the Codex backend, reasoning effort `ultra` (deep-audit tier). Must be an OpenAI model (e.g., `gpt-5.6-sol`, `gpt-5.5`, `o3`). Manual backend uses whatever model the user chooses.
• **REVIEWER_BACKEND = `codex`** — Default: Codex MCP (ultra). Override with `— reviewer: oracle-pro` for Oracle MCP, or `— reviewer: manual` for Manual Review MCP. If manual-review MCP is unavailable, stop and print the install command; do not fall back to Codex. See `shared-references/reviewer-routing.md`.

Reviewer Calling Convention

When calling the reviewer, branch on REVIEWER_BACKEND:

**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"} 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"}

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

• **Codex MCP Server** configured in Claude Code:

  claude mcp add codex -s user -- codex mcp-server

• 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-5.6-sol
  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
    Please be brutally honest.

The review brief should contain the full research context, the specific questions, and the primary artifact / raw-result paths the reviewer should inspect.

*For manual backend:* use `mcp__manual_review__review` with the same brief contents. If the manual-review UI supports attachments, attach `RESEARCH_REVIEW_REQUEST.md`; otherwise paste the brief inline. Save the returned `threadId`.

Step 3: Iterative Dialogue (Rounds 2-N)

For `codex` backend: use `mcp__codex__codex-reply` with the returned `threadId`. For `manual` backend: use `mcp__manual_review__review_reply` with the same `threadId`. Use the appropriate tool to continue the conversation. For Codex follow-up rounds, write an updated brief such as `RESEARCH_REVIEW_ROUND_2.md` and send only the path:

mcp__codex__codex-reply:
  threadId: [saved reviewer threadId from Step 2]
  # replies inherit the thread's model/effort (gpt-5.6-sol ultra)
  prompt: |
    Read the updated review brief at <absolute path to
    RESEARCH_REVIEW_ROUND_2.md>.
    Focus on unresolved weaknesses and whether the revision actually fixed them.

For manual follow-up rounds, attach that same updated brief if possible; otherwise paste it inline.

For each round: 1. **Respond** to criticisms with evidence/counterarguments 2. **Ask targeted follow-ups** on the most actionable points 3. **Request specific deliverables**: experiment designs, paper outlines, claims matrices

Key follow-up patterns:

• "If we reframe X as Y, does that change your assessment?"
• "What's the minimum experiment to satisfy concern Z?"
• "Please