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Agent Skill · 研究检索

prior-art-search(现有技术检索)

prior-art-search 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

prior-art-search 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,674 个 Skill 里排第 30,605。

总安装 2,851周安装 120GitHub Stars 96下载 998核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:prior-art-search(现有技术检索)
来源仓库:https://github.com/robthepcguy/claude-patent-creator
仓库路径:skills/prior-art-search
安装命令:
npx skills add https://github.com/robthepcguy/claude-patent-creator --skill prior-art-search
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/robthepcguy/claude-patent-creator --skill prior-art-search

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

prior-art-search 用于查找、检索和筛选相关信息,适合专利或技术研究中的背景调研。适用于根据关键词或技术点快速获取候选资料,需结合具体领域线索使用。通过 npx skills add 命令从指定 GitHub 仓库安装,建议核对原始 README 了解数据覆盖范围。

安装前应确认是否会触发联网查询或访问外部数据库等操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/prior-art-search

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/robthepcguy/claude-patent-creator --skill prio…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

prior-art-search 怎么安装?

在终端执行 npx skills add https://github.com/robthepcguy/claude-patent-creator --skill prior-art-sear…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

prior-art-search 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

prior-art-search 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

prior-art-search 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

prior-art-search 用的人多吗?

公开数据是累计安装 2,851 次,近一周安装 120 次,下载 998 次,GitHub 96 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

prior-art-search 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
prior-art-search

现有技术检索 · 本页

2,85196需要联网MIT有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Prior Art Search Skill

Systematic 7-step methodology for comprehensive patent prior art searches and patentability assessments.

When to Use

Invoke this skill when users ask to:

  • Conduct prior art search for an invention
  • Assess patentability of an idea
  • Perform freedom-to-operate analysis
  • Find blocking patents
  • Research patent landscapes
  • Prepare for patent filing

What This Skill Does

Implements a professional 7-step prior art search methodology combining:

  • Keyword searches across 100M+ patents (BigQuery)
  • CPC classification searches
  • USPTO API searches
  • Timeline analysis
  • Patentability assessment
  • IDS (Information Disclosure Statement) preparation

The 7-Step Methodology

Step 1: Invention Definition (2-3 min)

Goal: Extract key features and define innovation scope

Process:

  1. Interview user about invention
  2. Extract core technical elements
  3. Identify novel features
  4. List all components/steps
  5. Define search scope

Output: Structured invention summary with key features

Questions to Ask:

  • What problem does this solve?
  • What are the key components/steps?
  • What makes this different from existing solutions?
  • What is the core innovation?

Step 2: Keyword Strategy (2-3 min)

Goal: Develop comprehensive search keyword list

Process:

  1. Primary keywords from invention
  2. Synonyms and variations
  3. Technical terminology
  4. Industry-specific terms
  5. Boolean search strings

Output: Keyword search strategy document

Example:

Primary: blockchain authentication
Synonyms: distributed ledger verification, cryptographic authentication
Technical: public key infrastructure, digital signature
Related: decentralized identity, trustless verification
Searches:
- "blockchain AND (authentication OR verification)"
- "(distributed ledger) AND (identity OR credential)"
- "cryptographic AND (login OR access control)"

Step 3: Broad Keyword Search (3-5 min)

Goal: Cast wide net to find relevant patents

Process:

  1. Run keyword searches on BigQuery
  2. Review top 20-30 results per query
  3. Identify most relevant patents
  4. Refine keyword strategy based on results
  5. Document relevant patents found

Code:

from python.bigquery_search import BigQueryPatentSearch
searcher = BigQueryPatentSearch()

results = searcher.search_patents(
    query="blockchain authentication",
    limit=30,
    country="US",
    start_year=2015  # Look back 5-10 years
)

Output: List of 10-20 potentially relevant patents


Step 4: CPC Code Identification (2-3 min)

Goal: Find relevant classification codes

Process:

  1. Extract CPC codes from relevant patents found in Step 3
  2. Analyze CPC code descriptions
  3. Identify primary classification areas
  4. Select 3-5 most relevant CPC codes
  5. Note CPC hierarchies

Common CPC Categories:

  • G06F: Computing/data processing
  • H04L: Digital communication/networks
  • G06Q: Business methods
  • H04W: Wireless communication
  • G06N: AI/neural networks
  • G06T: Image processing

Output: List of relevant CPC codes with descriptions


Step 5: Deep CPC Search (5-10 min)

Goal: Comprehensive search within classifications

Process:

  1. Search each CPC code identified
  2. Review 50-100 patents per CPC code
  3. Read abstracts and claims of top matches
  4. Document closest prior art
  5. Note key differences from invention

Code:

results = searcher.search_by_cpc(
    cpc_code="G06F21/",  # Security arrangements
    limit=100,
    country="US"
)

Output: Comprehensive list of potentially blocking patents


Step 6: Timeline Analysis (2-3 min)

Goal: Understand technology evolution

Process:

  1. Filter results by date ranges
  2. Identify filing trends over time
  3. Find recent developments (last 2 years)
  4. Check priority dates
  5. Note technology progression

Code:

# Search by year ranges
recent = searcher.search_patents(query, start_year=2022, end_year=2024)
older = searcher.search_patents(query, start_year=2015, end_year=2021)

Output: Timeline showing technology development


Step 7: Patentability Report (5-10 min)

Goal: Professional assessment and recommendations

Process:

  1. Analyze top 10 closest prior art
  2. Assess novelty (35 USC 102)
  3. Assess non-obviousness (35 USC 103)
  4. Rank prior art by relevance
  5. Provide claim strategy recommendations
  6. Generate IDS list

Output: Comprehensive patentability report


Report Format

# PRIOR ART SEARCH REPORT

## Executive Summary
- Invention: [Brief description]
- Search Date: [Date]
- Searcher: Claude Patent Creator
- Databases: BigQuery (100M+ patents), USPTO API
- Time Period: [Year range]

## Patentability Assessment

### Novelty (35 USC 102)
[Assessment of whether invention is novel]

Score: [High/Medium/Low]

Analysis:
- No exact matches found
- Closest prior art: US10123456
- Key differences: [List]

### Non-Obviousness (35 USC 103)
[Assessment of whether invention is non-obvious]

Score: [High/Medium/Low]

Analysis:
- Combinations considered: [List]
- Motivation to combine: [Analysis]
- Unexpected results: [If any]

## Top 10 Most Relevant Prior Art

### 1. US10123456B2 - [Title] (95% Relevance)
**Assignee**: Example Corp
**Filed**: 2018-03-15
**Granted**: 2019-09-30
**CPC**: G06F21/31, H04L29/06

**Summary**: [Brief abstract]

**Similarities**:
- Uses blockchain for authentication
- Employs public key cryptography
- Distributed verification

**Differences**:
- Does not use [novel feature 1]
- Lacks [novel feature 2]
- Different approach to [aspect]

**Relevance**: High - core technology overlap

---

[Continue for top 10 patents...]

## Search Methodology

### Keywords Used
- Primary: blockchain, authentication, distributed ledger
- Synonyms: cryptographic verification, decentralized identity
- Technical: public key infrastructure, digital signature

### CPC Codes Searched
- G06F21/31 (Authentication)
- H04L29/06 (Security arrangements)
- G06Q20/40 (Payment authentication)

### Databases
- Google BigQuery: 247 results reviewed
- USPTO API: 89 results reviewed
- Total patents analyzed: 336
- Relevant patents identified: 47
- Top prior art selected: 10

## Claim Strategy Recommendations

### Recommended Approach
1. **Focus on novel aspects**: [Specific features]
2. **Claim breadth**: Start broad, add dependent claims
3. **Avoid prior art**: Distinguish from US10123456 by [...]

### Suggested Independent Claim Language

A system for [invention], comprising: [novel element 1]; [novel element 2]; wherein [novel relationship/function]

### Dependent Claim Opportunities
- Specific implementations of [feature]
- Combinations with [technology]
- Variations in [parameter/configuration]

## IDS (Information Disclosure Statement) List

Patents to be disclosed to USPTO:

1. US10123456B2 - [Title]
2. US10234567A1 - [Title]
3. US10345678B1 - [Title]
4. US10456789A1 - [Title]
5. US10567890B2 - [Title]
6. EP3123456A1 - [Title]
7. WO2019/123456 - [Title]
8. US2020/0123456A1 - [Title]
9. US10678901B2 - [Title]
10. US10789012A1 - [Title]

## Conclusion

**Patentability**: [High/Medium/Low]

**Rationale**:
[Summary of why invention is or is not patentable]

**Recommended Next Steps**:
1. [Action item 1]
2. [Action item 2]
3. [Action item 3]

Integration Points

This skill integrates with:

  • BigQuery Patent Search skill (Step 3, 5, 6)
  • MPEP Search skill (For legal guidance)
  • Patent Claims Analyzer (For claim drafting)

Required Data Access

  • Google Cloud BigQuery (100M+ patents)
  • USPTO API (optional, for additional coverage)
  • Internet access for patent retrieval

Estimated Time

  • Quick Search (Steps 1-3): 10-15 minutes
  • Thorough Search (Steps 1-6): 25-35 minutes
  • Complete Report (All 7 steps): 40-60 minutes

Tools Available

  • Bash: To run Python searches
  • Write: To save report and findings
  • Read: To load invention descriptions
  • Grep: To search through results

平台分布

Claude Code

32.5%
按下载量换算324

OpenCode

20.72%
按下载量换算207

Gemini CLI

16.34%
按下载量换算163

windsurf

12.32%
按下载量换算123

trae

7.52%
按下载量换算75

Antigravity

3.22%
按下载量换算32

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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