novelty-check

novelty-check is a skill for Claude Code from wanshuiyin/Auto-claude-code-research-in-sleep. It costs 49 tokens per session (1,759 once invoked), scanned A, original, MIT.

A literature search for checking whether a research idea has already been published. It compares the idea with recent papers and preprints, including work from major machine-learning conferences.

In plain words
What is it for?
Use it before implementing a research method or claiming novelty. It breaks the idea into key technical claims, searches multiple academic sources, and examines potentially overlapping papers.
Why use it?
It reduces the risk of developing or presenting an idea that closely matches existing research. It also helps identify which parts of an idea may actually be new.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions Codex.

Good fit Use it before implementing a research method or claiming novelty. It breaks the idea into key technical claims, searches multiple academic sources, and examines potentially overlapping papers.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/wanshuiyin/auto-claude-code-research-in-sleep/novelty-check
About the project

ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.

wanshuiyin/Auto-claude-code-research-in-sleep · 16,030 stars · on GitHub

Install

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.

Any agent
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill novelty-check
Clone the repo
git clone --depth 1 https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep

Made for: Claude Code.

Wrote 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.

agentmods badge for novelty-check

README.md
[![agentmods](https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/novelty-check/github.svg)](https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/novelty-check)
Your own site
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/novelty-check"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/novelty-check/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.

agentmods 80×15 button for novelty-check

Your own site · 80×15
<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/novelty-check"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/novelty-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 49 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,759 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 18 May 2026
  • Snyk warn 18 May 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00049 $0.01759
Opus 5 $0.00024 $0.00879
Sonnet 5 $0.00010 $0.00352
Haiku 4.5 $0.00005 $0.00176

Measured 5d ago against content hash 7738e665e8a7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

novelty-check 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 5d 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.

skills/novelty-check/SKILL.md · 143 lines

How it starts

The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Novelty Check Skill

Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS

Constants

  • REVIEWER_MODEL = gpt-6-astra — Model used via Codex MCP. Must be an OpenAI model (e.g., gpt-6-astra, o3, gpt-4o)

Instructions

Given a method description, systematically verify its novelty:

Phase A: Extract Key Claims

  1. Read the user's method description
  2. Identify 3-5 core technical claims that carry the claimed delta:
    • What is the method?
    • What problem does it solve?
    • What is the mechanism?
    • What makes it different from obvious baselines?

Phase B: Multi-Source Literature Search

For EACH core claim, search using ALL available sources:

  1. Web Search (via WebSearch):

    • Search arXiv, Google Scholar, Semantic Scholar
    • Use specific technical terms from the claim
    • Try at least 3 different query formulations per claim
    • Include year filters for 2024-2026
  2. Known paper databases: Check against:

    • ICLR 2025/2026, NeurIPS 2025, ICML 2025/2026
    • Recent arXiv preprints (2025-2026)
  3. Read abstracts: For each potentially overlapping paper, WebFetch its abstract and related work section

Phase C: Cross-Model Verification

Call REVIEWER_MODEL via Codex MCP (mcp__codex__codex) with xhigh reasoning. When the method description plus the Phase-B paper list is more than a short note, avoid pasting it inline into the MCP prompt. Write a dossier file such as NOVELTY_DOSSIER.md (or a project-local equivalent) containing the method description, core claims, candidate papers, and the exact questions below, then send only the file path:

mcp__codex__codex:
  model: gpt-6-astra
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    Read the novelty dossier at <absolute path to NOVELTY_DOSSIER.md> and
    follow all instructions in it.

Dossier contents should include:

  • The proposed method description
  • All papers found in Phase B
  • Ask: "Is this method novel? What is the closest prior work? What is the delta?"
  • The NOVELTY VERDICT LIMITS block below, verbatim — the reviewer judges under it

Read the full file on GitHub · 143 lines

Changes

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.

  1. 5d ago Changed 7738e665e8a7
  2. 13d ago First seen · 143 lines · 49 tokens per session scan A ec22d57b2784

Subscribe to this mod's changes

novelty-check is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (16,030 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 1,759 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 2026-08-30.

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