novelty-check

novelty-check is a skill for Claude Code from raja21068/AutoResearch. It costs 49 tokens per session (861 once invoked), scanned A, original, MIT.

A literature-checking workflow examines recent research to see whether a proposed idea or method has already been done.

In plain words
What is it for?
Use it to search papers, read abstracts and related work, and assess the novelty of a research proposal.
Why use it?
It reduces the risk of building a project around an idea that is not new. It also identifies similar papers and technical claims that need closer comparison.

Skill for Claude Code

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

Good fit Use it to search papers, read abstracts and related work, and assess the novelty of a research proposal.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/raja21068/autoresearch/novelty-check
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 raja21068/AutoResearch --skill novelty-check
Clone the repo
git clone --depth 1 https://github.com/raja21068/AutoResearch

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/raja21068/autoresearch/novelty-check/github.svg)](https://agentmods.dev/skills/raja21068/autoresearch/novelty-check)
Your own site
<a href="https://agentmods.dev/skills/raja21068/autoresearch/novelty-check"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/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/raja21068/autoresearch/novelty-check"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/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 861 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.
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 $0.00861
Opus 5 $0.00024 $0.00430
Sonnet 5 $0.00010 $0.00172
Haiku 4.5 $0.00005 $0.00086

Measured 8d ago against content hash dc85da9188a0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, 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 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.

skills/aris/novelty-check/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 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-5.4 — Model used via Codex MCP. Must be an OpenAI model (e.g., gpt-5.4, 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 would need to be novel:
    • 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:

config: {"model_reasoning_effort": "xhigh"}

Prompt 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?"

Phase D: Novelty Report

Output a structured report:

## Novelty Check Report

### Proposed Method
[1-2 sentence description]

### Core Claims
1. [Claim 1] — Novelty: HIGH/MEDIUM/LOW — Closest: [paper]
2. [Claim 2] — Novelty: HIGH/MEDIUM/LOW — Closest: [paper]
...

### Closest Prior Work
| Paper | Year | Venue | Overlap | Key Difference |
|-------|------|-------|---------|----------------|

### Overall Novelty Assessment
- Score: X/10
- Recommendation: PROCEED / PROCEED WITH CAUTION / ABANDON
- Key differentiator: [what makes this unique, if anything]
- Risk: [what a reviewer would cite as prior work]

### Suggested Positioning
[How to frame the contribution to maximize novelty perception]

Read the full file on GitHub · 91 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. 8d ago First seen · 91 lines · 49 tokens per session scan A dc85da9188a0

Subscribe to this mod's changes

novelty-check is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 861 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 2026-09-03.

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