git:20260710.c3f9813 to git:20260826.363589d

26 added, 1 removed. Audit A to A.

---

name: "research-review"

description: "Get a deep critical review of research from GPT using a secondary Codex agent. Use when user says \"review my research\", \"help me review\", \"get external review\", or wants critical feedback on research ideas, papers, or experimental results."

---

# Research Review via a secondary Codex agent (ultra reasoning)

> **Codex assurance:** the fresh base reviewer is same-family. Record

> `review_independence: same-family` and `acceptance_status: provisional` in

> traces and deliverables. A Claude/Gemini overlay may record cross-family

> accepted; an unavailable reviewer is BLOCKED, never a fabricated PASS.

Get a multi-round critical review of research work from an external LLM with maximum reasoning depth.

## Constants

- REVIEWER_MODEL = `gpt-5.6-sol` — Model used via a secondary Codex agent, reasoning effort `ultra` (deep-audit tier). Must be an OpenAI model (e.g., `gpt-5.6-sol`, `gpt-5.5`, `o3`)

- **REVIEWER_BACKEND = `codex`** — Default: Codex ultra reviewer (deep-audit tier). Use `--reviewer: oracle-pro` only when explicitly requested; if Oracle is unavailable, warn and fall back to Codex at this skill's declared tier (`ultra`). **Same-family note:** this default reviewer is a second Codex/GPT agent — valid for Type-A completeness/drive review, but not a cross-family Type-B verdict; install a `skills-codex-claude-review` / `skills-codex-gemini-review` overlay for a cross-family acquittal (see `shared-references/reviewer-routing.md`).

## Context: $ARGUMENTS

## Prerequisites

- Use `spawn_agent` and `send_input` when the user has explicitly allowed delegation or subagents.

- If delegation is not allowed, run the same review loop locally and preserve the same deliverable structure.

## 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:

```

spawn_agent:

model: gpt-5.6-sol

reasoning_effort: ultra

message: |

[Full research context + specific questions]

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.

+ === 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 local user is NOT in the threat model.

+ 2. Do NOT propose SHA / hash / content-fingerprint / digest-binding schemes.

+ Reporting a real defect in hashing code that already exists is fine.

+ 3. NO speculative machinery: do not add feature flags, migration frameworks,

+ compat layers, wrappers, pins, or similar mechanisms unless evidence shows

+ a current repo defect they fix or an explicit existing invariant they must

+ preserve. "Load-bearing", "compatibility", and "not scaffolding" are labels,

+ not evidence. Point to the failing path/artifact or invariant, and check the

+ proposal's factual premises, such as whether a named package version exists.

+ 4. NO corner-case obsession: exotic encodings, symlink races, RTL text and

+ millisecond races are out of scope unless you can show the case arises here.

+ 5. Where a rubric or checklist is genuinely needed, do not over-mechanize

+ judgement. A clear sentence a human reads beats a scored table nobody

+ maintains.

+ Exception: code that runs remote commands, starts a network service, or installs

+ an MCP server runs on the user's machine with their credentials — trust-boundary

+ findings there are in scope and the default is strict.

+ Say plainly when something is correct. Do not manufacture findings.

+ Be brutally honest. If, after genuinely trying to break it, the work

+ holds up and is ready, say so clearly.

```

### Step 3: Iterative Dialogue (Rounds 2-N)

Use `send_input` with the returned agent id to continue the conversation:

```text

send_input:

target: [saved reviewer id from Step 2]

message: |

Please continue the review using the revised materials below.

Revised files:

- /absolute/path/to/file1

- /absolute/path/to/file2

Focus on unresolved weaknesses and whether the revision actually fixed them.

```

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.

If `— composed: <canonical-report-path>` is explicitly present, fold consensus,

claims matrix, TODOs, and trace links into that report instead of writing a

standalone review document. Without the directive, write the standalone review

as documented; never infer composed mode from an existing file. `— standalone`

always wins. See

[`output-composition.md`](../shared-references/output-composition.md).

### Step 6: Review Tracing

Save a trace for every `spawn_agent`, `send_input`, or `oracle-pro` review call following `../shared-references/review-tracing.md`. Record the reviewer route, saved agent id, prompt summary, raw response path, decisions, and action items. This preserves the Claude mainline Review Tracing semantics while using Codex-native reviewer calls.

## Key Rules

- ALWAYS use `model: gpt-5.6-sol` + `reasoning_effort: ultra` for reviews (deep-audit tier; capability fallback per `reviewer-routing.md`, never below `xhigh`)

- 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?"

- Document the agent id 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."