novelty-check
Verify research idea novelty against recent literature
Install / Use
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill novelty-checkInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AI & Machine LearningSupported Platforms
Our assessment of novelty-check
novelty-check scores 82/100 on our quality scale, 449th of 729 AI & Machine Learning skills we index.
Its SKILL.md is 7.0 KB long, well organised into 16 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.
With 16,644 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 8 days ago, so novelty-check is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
novelty-check compared with similar skills
All 4 of these similar skills score higher than novelty-check; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| novelty-check (this skill)by wanshuiyin | 82 | 16.6k | 8d ago | SKILL.md |
| claude-memby thedotmack | 100 | 94.7k | today | CLAUDE.md |
| Agent-Reachby Panniantong | 100 | 85.6k | 11d ago | CLAUDE.md |
| Understand-Anythingby Egonex-AI | 100 | 84.3k | 14d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 73.9k | today | CLAUDE.md |
Frequently asked questions
- How do I install novelty-check?
- Run
npx skills add wanshuiyin/Auto-claude-code-research-in-sleep --skill novelty-check. The install tabs above show the steps for each supported agent. - Which AI agents does novelty-check work with?
- It is written for OpenAI Codex, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is novelty-check safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is novelty-check still maintained?
- The repository was last updated 8 days ago, so novelty-check is actively maintained.
Skill content
View source on GitHubname: novelty-check description: Verify research idea novelty against recent literature. Use when user says "查新", "novelty check", "有没有人做过", "check novelty", or wants to verify a research idea is novel before implementing. argument-hint: "[method-or-idea-description]" allowed-tools: WebSearch, WebFetch, Grep, Read, Glob, mcp__codex__codex
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
- Read the user's method description
- 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:
-
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
-
Known paper databases: Check against:
- ICLR 2025/2026, NeurIPS 2025, ICML 2025/2026
- Recent arXiv preprints (2025-2026)
-
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
The verdict limits
Copy this block verbatim into the reviewer's briefing; the report in Phase D is judged under it too.
=== NOVELTY VERDICT LIMITS (these bound how you judge, never how widely you search) ===
Search exhaustively; judge calibrated. Two failures waste months equally:
passing an idea a published paper already contains, and killing a viable idea
because the territory has neighbors.
1. Proximity is information, not a verdict. Someone working nearby goes in the
report; it is not by itself a reason to reject.
2. ABANDON has exactly one qualification: a specific published paper already
contains this result — name that paper. No named paper, no ABANDON.
3. Crowded-but-deltaed is PROCEED: state the delta in one sentence a reviewer
could verify. Thin or contested delta is PROCEED WITH CAUTION — say what
would make it carry, not why it should die. CAUTION is not a safe middle:
if you cannot name the specific thing that makes the delta thin, the
verdict is PROCEED.
4. Concurrent or competing work is not a veto. That is a race — report it and
let the user decide whether to run it.
5. A direct attack on a central problem is legitimate novelty when nobody has
executed it well. "This area is hot" does not mean "this area is taken."
6. This check is an early gate, never the last one — more triage, pilots, or
external review still stand between any idea and a paper, whatever order
this run uses. A wrongly passed idea dies cheaply at one of them; a wrongly
killed idea is never seen again. When torn between two verdicts, choose the
more permissive one.
Say plainly when an idea clears the check. Do not manufacture overlap.
Phase D: Novelty Report
Output a structured report:
## Novelty Check Report
### Proposed Method
[1-2 sentence description]
### Core Claims
1. [Claim 1] — Closest: [paper] — What stays unknown or different: [delta]
2. [Claim 2] — Closest: [paper] — What stays unknown or different: [delta]
...
### Closest Prior Work
| Paper | Year | Venue | Overlap | Key Difference |
|-------|------|-------|---------|----------------|
### Overall Novelty Assessment
- Score: X/10 (anchor: 5/10 = has clear neighbors but a defensible delta worth
a pilot; reserve 1-3 for results a named published paper already contains)
- Recommendation: PROCEED / PROCEED WITH CAUTION / ABANDON (per the verdict
limits: crowded-but-deltaed ground is PROCEED; ABANDON must name the paper)
- Key differentiator: [what makes this unique, if anything]
- Risk: [what a reviewer would cite as prior work]
### Suggested Positioning
[State the delta honestly in one sentence a reviewer could verify]
Important Rules
- Two failures waste months equally: a false novelty claim, and a viable idea abandoned because the territory has neighbors. Be brutally honest in both directions — and when an idea clears the check, say so plainly.
- Novelty can live in the combination or the finding even when every individual claim rates LOW — judge the idea, not each claim in isolation. Known parts arranged to reveal something unknown are novel.
- "Applying X to Y" earns novelty by what the application reveals — a non-obvious interaction, failure mode, or insight. Judge the revelation, not the template.
- Check both the method AND the experimental setting for novelty
- If the method is not novel but the FINDING would be, say so explicitly
- Always check the most recent 6 months of arXiv — the field moves fast
- Anti-hallucination for Closest Prior Work. Every paper in the prior-work table must pass pre-search verification via
verify_papers.py(canonical name resolved pershared-references/integration-contract.md§2; 3-layer arXiv / CrossRef / Semantic Scholar fallback inside the helper itself). Policy D1 (primary + degraded-output fallback): if the helper is unresolved or its invocation fails, tag candidate entries[UNVERIFIED]and surface the uncertainty rather than dropping them. Never fabricate arXiv IDs, DOIs, or titles from memory. Full protocol inshared-references/citation-discipline.md§ Pre-Search Verification Protocol.
Review Tracing
After each mcp__codex__codex or mcp__codex__codex-reply reviewer call, save the trace following shared-references/review-tracing.md (Policy C — forensic; never silently skip). Use save_trace.sh (resolved per the chain in shared-references/integration-contract.md §2) or write files directly to .aris/traces/<skill>/<date>_run<NN>/. Respect the --- trace: parameter (default: full).
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Languages
Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
