Research Review via External Reviewer Backend (xhigh 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.

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

Constants

REVIEWER_MODEL = gpt-5.5 — Default model for the Codex backend. Must be an OpenAI model (e.g., gpt-5.5, o3, gpt-4o). Manual backend uses whatever model the user chooses.
REVIEWER_BACKEND = codex — Default: Codex MCP (xhigh). 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:

Read project narrative documents (e.g., STORY.md, README.md, paper drafts)
Read any memory/notes files for key findings and experiment history
Identify: core claims, methodology, key results, known weaknesses

Step 2: Initial Review (Round 1)

Send a detailed prompt with xhigh 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:
  config: {"model_reasoning_effort": "xhigh"}
  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). 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]
  config: {"model_reasoning_effort": "xhigh"}
  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:

Respond to criticisms with evidence/counterarguments
Ask targeted follow-ups on the most actionable points
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 design the minimal additional experiment package (highest acceptance lift per GPU week)"
"Please write a mock NeurIPS/ICML review with scores"
"Give me a results-to-claims matrix for possible experimental outcomes"

Step 4: Convergence

Stop iterating when:

Both sides agree on the core claims and their evidence requirements
A concrete experiment plan is established
The narrative structure is settled

Step 5: Document Everything

Save the full interaction and conclusions to a review document in the project root:

Round-by-round summary of criticisms and responses
Final consensus on claims, narrative, and experiments
Claims matrix (what claims are allowed under each possible outcome)
Prioritized TODO list with estimated compute costs
Paper outline if discussed

Update project memory/notes with key review conclusions.

Composed mode — if invoked with — composed: <canonical-report-path> (an orchestrator like /idea-discovery passes this), do not write a standalone review .md in the project root. The raw conversation is already persisted to .aris/traces/… (see Review Tracing below — that audit copy is kept in every mode); fold the review conclusions (consensus, claims matrix, prioritized TODOs) into the orchestrator's canonical report and cite the trace path there. Default (no — composed: directive): behave exactly as above — write the standalone review document. Never infer composed mode from a report file merely existing. Full rules: shared-references/output-composition.md.

Key Rules

ALWAYS use config: {"model_reasoning_effort": "xhigh"} for reviews
Put comprehensive context in the review brief. Codex can read local files when you pass an absolute path; manual reviewers usually cannot, so attach or paste the same brief there.
Be honest about weaknesses — hiding them leads to worse feedback
Push back on criticisms you disagree with, but accept valid ones
Focus on ACTIONABLE feedback — "what experiment would fix this?"
Document the threadId for potential future resumption
The review document should be self-contained (readable without the conversation)

Prompt Templates

For initial review:

"I'm going to present a complete ML research project for your critical review. Please act as a senior ML reviewer (NeurIPS/ICML level)..."

For experiment design:

"Please design the minimal additional experiment package that gives the highest acceptance lift per GPU week. Our compute: [describe]. Be very specific about configurations."

For paper structure:

"Please turn this into a concrete paper outline with section-by-section claims and figure plan."

For claims matrix:

"Please give me a results-to-claims matrix: what claim is allowed under each possible outcome of experiments X and Y?"

For mock review:

"Please write a mock NeurIPS review with: Summary, Strengths, Weaknesses, Questions for Authors, Score, Confidence, and What Would Move Toward Accept."

Review Tracing

After each reviewer call (mcp__codex__codex, mcp__codex__codex-reply, mcp__manual_review__review, or mcp__manual_review__review_reply), save the trace following shared-references/review-tracing.md (Policy C — forensic; never silently skip). Use save_trace.sh (resolved per the chain in shared-references/integration-contract.md §2) or write files directly to .aris/traces/<skill>/<date>_run<NN>/. Respect the --- trace: parameter (default: full).

Research Review