Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/llv22/AutoResearchWithEyesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/llv22/autoresearchwitheyes/research-reviewer)<a href="https://agentmods.dev/agents/llv22/autoresearchwitheyes/research-reviewer"><img src="https://agentmods.dev/badge/agents/llv22/autoresearchwitheyes/research-reviewer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.00952 |
| Opus 5 | $0.00024 | $0.00476 |
| Sonnet 5 | $0.00010 | $0.00190 |
| Haiku 4.5 | $0.00005 | $0.00095 |
Grade A, and why
research-reviewer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Review via Codex MCP (xhigh reasoning)
Get a multi-round critical review of research work from an external LLM with maximum reasoning depth.
Constants
REVIEWER_MODEL is defined in the project's CLAUDE.md. Read it from there before proceeding. Codex MCP is auto-configured via .mcp.json.
Context: $ARGUMENTS
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:
mcp__codex__codex:
model: REVIEWER_MODEL
config: {"model_reasoning_effort": "xhigh"}
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)
Use mcp__codex__codex-reply with the returned threadId to continue the conversation:
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 98 lines · 48 tokens per session scan A e120b9a3dccc
research-reviewer is an agent published in the GitHub repository llv22/AutoResearchWithEyes (5 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 952 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
Geoprocessing Specialist
ArcPy and Python toolbox expert who automates spatial workflows — builds .pyt toolboxes, Model Builder processes, batch geoprocessing automation, and custom analysis scripts for ArcGIS Pro.
research-scout
Scans the NeqSim codebase to discover scientific paper opportunities that will drive code improvement. Every paper must improve NeqSim — adding tests, validating models against data, hardening algorithms, or implementing new capabilities. Produces ranked, actionable topics that feed into the planner agent.
mathodology-problem-analyst
Use for contest problem decomposition, scoring criteria, constraints, variables, assumptions, and deliverable mapping.
astronomical-instrumentation-scientist
Reasons from system-level error budgets, the diffraction limit and Strehl ratio, detector figures of merit, and resolving power through Zemax/Code V tolerancing, ETC radiometry, AO modeling, and on-sky standard-star commissioning while treating flexure drift, IR persistence, ghosts, and quasi-static speckles as…
eic_agent
Journal-Fit Reviewer seat; contributes the journal-fit / originality / overall-quality review card — the final editorial decision is editorialsynthesizeragent's Phase 2 work.