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.
npx skills add raja21068/AutoResearch --skill research-reviewgit clone --depth 1 https://github.com/raja21068/AutoResearchWrote 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/skills/raja21068/autoresearch/research-review)<a href="https://agentmods.dev/skills/raja21068/autoresearch/research-review"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/research-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/raja21068/autoresearch/research-review"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/research-review.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00050 | $0.01183 |
| Opus 5 | $0.00025 | $0.00592 |
| Sonnet 5 | $0.00010 | $0.00237 |
| Haiku 4.5 | $0.00005 | $0.00118 |
Grade A, and why
research-review 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 8d 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 — 112 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 =
gpt-5.4— Model used via Codex MCP. Must be an OpenAI model (e.g.,gpt-5.4,o3,gpt-4o) - REVIEWER_BACKEND =
codex— Default: Codex MCP (xhigh). Override with— reviewer: oracle-profor GPT-5.4 Pro via Oracle MCP. Seeshared-references/reviewer-routing.md.
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__codexandmcp__codex__codex-replytools
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:
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"
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.
- 8d ago First seen · 112 lines · 50 tokens per session scan A 53161dc6317c
research-review is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 50 tokens to every session and 1,183 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
literature-review-agent
Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), cross-corroborate against Crossref + OpenAlex to…
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
agent-research-aggregator
Pre-pipeline aggregator that scans AI agent cache directories (.claude, .cursor, .antigravity, .openclaw) or any user-specified directory for experimentation logs, extracts insights and numeric results, and formats them as PaperOrchestra-ready inputs (idea.md + experimentallog.md). TRIGGER when the user says…
paper-orchestra
Orchestrate the full PaperOrchestra (Song et al., 2026, arXiv:2604.05018) five-agent pipeline to turn unstructured research materials (idea, experimental log, LaTeX template, conference guidelines, optional figures) into a submission-ready LaTeX manuscript and compiled PDF. TRIGGER when the user asks to "write a paper…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…