novelty-check
- Repo stars 11,320
- Author updated Live
- Author repo Auto-claude-code-research-in-sleep
- Domain
- Engineering
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @wanshuiyin · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: novelty-check
description: Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS Given a…
category: engineering
runtime: no special runtime
---
# novelty-check output preview
## PART A: Task fit
- Use case: Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS Given a method description, systematically verify its novelty: For EACH core claim, search using ALL available sources: Call REVIEWERMODEL via Codex MCP (mcpcodex__codex) with xhigh reasoning: config: {"modelreasoningeffort": "xhigh"} runs entirely locally. Works with….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Constants / Instructions / Phase A: Extract Key Claims” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS Given a method description, systematically verify its novelty: For EACH core claim, search using ALL available sources: Call REVIEWERMODEL via Codex MCP (mcpcodex__codex) with xhigh reasoning: config: {"modelreasoningeffort": "xhigh"} runs entirely locally. Works with…”.
- **02** When the source has headings, the agent prioritizes “Constants / Instructions / Phase A: Extract Key Claims” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Constants / Instructions / Phase A: Extract Key Claims”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: novelty-check
description: Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS Given a…
category: engineering
source: wanshuiyin/Auto-claude-code-research-in-sleep
---
# novelty-check
## When to use
- Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS Given a method description, s…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Constants / Instructions / Phase A: Extract Key Claims” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "novelty-check" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Constants / Instructions / Phase A: Extract Key Claims
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Novelty Check Skill
Check whether a proposed method/idea has already been done in the literature: $ARGUMENTS
Constants
- REVIEWER_MODEL =
gpt-5.5— Model used via Codex MCP. Must be an OpenAI model (e.g.,gpt-5.5,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 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:
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:
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]
Important Rules
- Be BRUTALLY honest — false novelty claims waste months of research time
- "Applying X to Y" is NOT novel unless the application reveals surprising insights
- 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).
Decide Fit First
Design Intent
How To Use It
Boundaries And Review