empiricalwiki: Skill for Claude Code

.claude/skills/novelty/SKILL.md

novelty is a skill for Claude Code from Lambenthan/empiricalwiki. It costs 28 tokens per session (2,369 once invoked), scanned A, original, MIT.

A research tool that checks whether an idea or method appears to be new by comparing it with web sources, Semantic Scholar, Wikipedia, and arXiv preprints.

In plain words
What is it for?
Use it to review research ideas, paper methods, or arXiv papers and receive a similarity list, novelty score, differences, and a suggested next action.
Why use it?
It helps reveal similar prior work before you invest time in an idea or claim it is novel.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: reads .claude/ paths; mentions Claude Code.

This is Lambenthan/empiricalwiki's own configuration. It tells Claude Code how to work on empiricalwiki itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything empiricalwiki configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 tools/fetch_s2.py search "<method-keywords>" --limit 20.

Reuse

Borrowing it

Nothing to install: this file belongs to Lambenthan/empiricalwiki. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Lambenthan/empiricalwiki/main/.claude/skills/novelty/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Lambenthan/empiricalwiki

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/novelty/github.svg)](https://agentmods.dev/skills/lambenthan/empiricalwiki/novelty)
Your own site
<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/novelty"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/novelty/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

Your own site · 80×15
<a href="https://agentmods.dev/skills/lambenthan/empiricalwiki/novelty"><img src="https://agentmods.dev/badge/skills/lambenthan/empiricalwiki/novelty.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,369 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 $0.00028 $0.02369
Opus 5 $0.00014 $0.01184
Sonnet 5 $0.00006 $0.00474
Haiku 4.5 $0.00003 $0.00237

Measured 10d ago against content hash 74a0e5c84106, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

novelty 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 10d 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.

.claude/skills/novelty/SKILL.md · 201 lines

How it starts

The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/novelty

对一个研究想法或方法进行多源 novelty 验证。搜索 WebSearch、Semantic Scholar、 wiki 内已有工作和 arXiv 最新预印本,然后由 Review LLM 交叉验证,输出 novelty 评分(1-5)、 最相似已有工作、差异化要点和下一步建议。 可独立使用,也被 /ideate Phase 4 调用。

Inputs

  • target:以下之一:
    • idea 的自由文本描述(一段话或几句话)
    • wiki 中 ideas/ 页面的 slug(如 sparse-lora-for-edge-devices
    • 论文标题或 arXiv URL(检查该论文方法的 novelty)
  • --quick:快速模式,跳过 Review LLM cross-verify(Step 3),仅做搜索
  • --verbose:输出完整搜索结果,不仅是摘要

Outputs

  • Novelty Report(输出到终端,不写入 wiki):
    • Novelty Score(1-5)
    • 最相似的已有工作列表(top 3-5)
    • 与每个已有工作的差异化要点
    • Review LLM 交叉验证意见(除非 --quick)
    • 推荐行动:proceed / modify / abandon
  • 该 skill 是只读查询,不修改 wiki 任何内容

Wiki Interaction

Reads

  • wiki/papers/*.md — 搜索已有论文中是否有类似方法
  • wiki/concepts/*.md — 检查概念重叠
  • wiki/ideas/*.md — 检查是否与已有 idea 重复(特别是 failed ideas 的 failure_reason)
  • wiki/claims/*.md — 检查 idea 所依赖的 claims 当前状态
  • wiki/graph/context_brief.md — 获取全局上下文辅助搜索

Writes

  • 。Novelty check 是纯查询操作,不修改 wiki。

Graph edges created

Workflow

前置:确认工作目录为 wiki 项目根(包含 wiki/raw/tools/ 的目录)。

Step 1: 提取方法签名

  1. 若 target 是 slug:读取 wiki/ideas/{slug}.md,提取 title、Hypothesis、Approach sketch
  2. 若 target 是自由文本:直接使用
  3. 若 target 是 arXiv URL:下载摘要,提取方法描述
  4. 从 target 中提取「方法签名」——方法的核心要素:
    • What:做什么(任务/目标)
    • How:用什么方法(技术路线)
    • Why novel:声称的创新点
  5. 生成 3-5 个核心关键词用于后续搜索

Step 2: 多源搜索

并行执行以下搜索(使用 Agent tool 并发):

Source A — Web Search(5+ 查询):

  1. 直接查询:"<method-name>" + "<task>" 精确短语搜索
  2. 组件查询:<component-1> + <component-2> + <domain> 组件组合搜索
  3. Survey 查询:"survey" OR "review" + <task-area> + 2024 2025
  4. 竞品查询:<alternative-approach> + <same-task>
  5. 最新查询:<method-keywords> + arXiv + 2025 2026

Source B — Semantic Scholar + DeepXiv:

python3 tools/fetch_s2.py search "<method-keywords>" --limit 20
python3 tools/fetch_deepxiv.py search "<method-keywords>" --mode hybrid --limit 20

合并两个来源的结果(按 arxiv_id 去重)。DeepXiv 的混合语义搜索能发现 S2 关键词搜索遗漏的语义相似工作。

  • 对 top 5 结果获取详情和 TLDR:
python3 tools/fetch_s2.py paper <s2_id>
python3 tools/fetch_deepxiv.py brief <arxiv_id>

使用 DeepXiv brief 的 TLDR 辅助快速判断方法相似度。 若 DeepXiv 不可用:仅使用 S2 搜索(回退到原有行为)。

Read the full file on GitHub · 201 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. 10d ago First seen · 201 lines · 28 tokens per session scan A 74a0e5c84106

Subscribe to this mod's changes

novelty is a skill published in the GitHub repository Lambenthan/empiricalwiki (82 stars, last pushed 2mo ago), licensed MIT. It adds 28 tokens to every session and 2,369 once invoked, about $0.0001 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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