research-review

name	research-review
description	Get a deep critical review of research from an external LLM reviewer via llm-chat MCP. 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, Agent, mcp__llm-chat__chat

Research Review via llm-chat MCP

Get a multi-round critical review of research work from an external LLM.

Constants

• REVIEWER_BACKEND = llm-chat — External LLM reviewer via llm-chat MCP (model defers to LLM_MODEL env). Always ask the external reviewer for strict, high-rigor feedback.
• Override with — reviewer: oracle-pro via Oracle MCP.

Reviewer LLM Configuration (mandatory, read first)

This skill calls an external LLM reviewer. Never hardcode a model name and never read the reviewer model from task.md / project READMEs / source comments. Project-level files may list available API keys for unrelated purposes (e.g., LLM-as-judge inside experiment code); those are not the reviewer config.

Resolve LLM_MODEL, LLM_BASE_URL, LLM_API_KEY strictly in this priority order before any reviewer call:

1. Project MCP config — ${PROJECT_ROOT}/.mcp.json, field mcpServers["llm-chat"].env.{LLM_MODEL,LLM_BASE_URL,LLM_API_KEY}.
2. User MCP config — ~/.claude/settings.json, same field.
3. Shell environment — $LLM_MODEL, $LLM_BASE_URL, $LLM_API_KEY.

Pre-flight check (run before Step 2, mandatory)

LLM_MODEL_SRC=""
if [ -f .mcp.json ] && jq -e '.mcpServers["llm-chat"].env.LLM_MODEL' .mcp.json >/dev/null 2>&1 ; then
  export LLM_MODEL=$(jq -r '.mcpServers["llm-chat"].env.LLM_MODEL' .mcp.json)
  export LLM_BASE_URL=$(jq -r '.mcpServers["llm-chat"].env.LLM_BASE_URL' .mcp.json)
  export LLM_API_KEY=$(jq -r '.mcpServers["llm-chat"].env.LLM_API_KEY' .mcp.json)
  LLM_MODEL_SRC="project .mcp.json"
elif [ -f ~/.claude/settings.json ] && jq -e '.mcpServers["llm-chat"].env.LLM_MODEL' ~/.claude/settings.json >/dev/null 2>&1 ; then
  export LLM_MODEL=$(jq -r '.mcpServers["llm-chat"].env.LLM_MODEL' ~/.claude/settings.json)
  export LLM_BASE_URL=$(jq -r '.mcpServers["llm-chat"].env.LLM_BASE_URL' ~/.claude/settings.json)
  export LLM_API_KEY=$(jq -r '.mcpServers["llm-chat"].env.LLM_API_KEY' ~/.claude/settings.json)
  LLM_MODEL_SRC="user ~/.claude/settings.json"
elif [ -n "$LLM_MODEL" ] && [ -n "$LLM_BASE_URL" ] && [ -n "$LLM_API_KEY" ] ; then
  LLM_MODEL_SRC="shell env"
fi
echo "[reviewer-config] LLM_MODEL=$LLM_MODEL  LLM_BASE_URL=$LLM_BASE_URL  source=$LLM_MODEL_SRC"

Hard-fail rule: If LLM_MODEL is empty after this resolution (none of the three sources provides it), the skill MUST abort with:

"Reviewer model not configured. Add mcpServers.llm-chat.env.{LLM_MODEL,LLM_BASE_URL,LLM_API_KEY} to .mcp.json (project) or ~/.claude/settings.json (user)."

Do not guess a default. Do not fall back to a model name read from task.md or any other project file.

Context: $ARGUMENTS

Prerequisites

• llm-chat MCP Server configured in ~/.claude/settings.json with LLM_API_KEY, LLM_BASE_URL, and LLM_MODEL.
• This gives Claude Code access to the mcp__llm-chat__chat tool.

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. Always ask the external reviewer for strict, high-rigor feedback.

mcp__llm-chat__chat:
  prompt: |
    [Full research context + specific questions]

    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.

Step 3: Iterative Dialogue (Rounds 2-N)

llm-chat is stateless — every call is a fresh conversation. For follow-up rounds, include a verbatim summary of the prior round's review (criticisms, author responses, open questions) inside the new mcp__llm-chat__chat prompt.

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 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.

Key Rules

• Always ask the external reviewer for strict, high-rigor feedback
• Send comprehensive context in Round 1 — the external model cannot read your files
• 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?"
• llm-chat is stateless — include prior-round context in every follow-up prompt
• 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 mcp__llm-chat__chat reviewer call, save the trace following shared-references/review-tracing.md. Write files directly to .mechanist/traces/<skill>/<date>_run<NN>/. Respect the --- trace: parameter (default: full).