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patent-professional-agents专利专业 Agent 人

Agent Skill

patent-professional-agents 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

总安装

6,961

周安装

299

GitHub Stars

公开资料未说明

下载量

2,440
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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:patent-professional-agents(专利专业 Agent 人)
来源仓库:https://github.com/bigpipihua/patent-professional-agents
安装命令:
openclaw skills install patent-professional-agents
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。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install patent-professional-agents

简介

多代理协作的专利撰写优化套件,覆盖全流程服务。

  • 支持从想法挖掘到权利要求强化的一站式处理。
  • 包含质量检查表和文件组织模板。patent-professional-agents 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 建议分阶段提交并迭代优化草案。
  • 注意最终申请仍需专业代理人签署提交。

SKILL.md

name
patent-professional-agents
version
1.0.1
description
|-
dependencies
skills
python

Professional Patent Agents Suite

License

MIT License

Copyright (c) 2026 BigPiPiHua

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.


Core Positioning

ScenarioInputOutputGoal
Scenario 1User idea (vague description)Complete patent document + Search reportGrant rate + Inventiveness
Scenario 2User draft (existing document)Optimized patent documentGrant rate + Inventiveness
Scenario 3Agency feedback (search report/prior art)Optimization suggestions / Cancellation adviceDecision support

Boundary: Does not handle Office Action (OA) responses - leave that to professional patent agencies.


Dependencies

Required Skills

SkillPurposeInstall Command
tavily-searchAI-optimized searchclawhub install tavily-search
aminer-open-academicAcademic paper searchclawhub install aminer-open-academic

Python Dependencies

pip install requests python-docx

Agent List (9 Core Agents)

AgentRoleCore CapabilityPriority
tech-minerTechnology Mining ExpertIdea analysis, innovation extraction, technical disclosure framework⭐⭐⭐⭐⭐
prior-art-researcherPrior Art Search ExpertKeyword strategy + Multi-source search + Analysis⭐⭐⭐⭐⭐
inventiveness-evaluatorInventiveness Evaluation ExpertInventiveness analysis, risk scoring, grant rate prediction⭐⭐⭐⭐⭐
patent-drafterPatent Drafting Expert7-section drafting, Mermaid diagrams, document conversion⭐⭐⭐⭐⭐
claims-architectClaims ArchitectClaims design, scope optimization⭐⭐⭐⭐
patent-analystPatent AnalystDraft analysis, issue identification, optimization suggestions⭐⭐⭐⭐
patent-auditorPatent Audit ExpertQuality review, grant rate prediction, revision suggestions⭐⭐⭐⭐⭐
patent-value-appraiserPatent Value Appraiser5-dimension value assessment, market value estimation⭐⭐⭐⭐
patent-converterDocument Conversion ExpertMarkdown→Word, Mermaid diagram embedding⭐⭐⭐⭐

Workflows

Scenario 1: User Idea → Drafting + Search

flowchart TB
    subgraph S1[Phase 1: Tech Mining]
        M1[tech-miner<br/>Understand idea] ~~~ M2[Extract innovations] ~~~ M3[Disclosure framework]
    end
    
    subgraph S2[Phase 2: Search]
        R1[prior-art-researcher<br/>Keyword strategy] ~~~ R2[Multi-source search] ~~~ R3[Analyze results]
    end
    
    subgraph S3[Phase 3: Evaluation]
        E1[inventiveness-evaluator<br/>Inventiveness analysis] ~~~ E2[Risk scoring] ~~~ E3[Differentiation advice]
    end
    
    subgraph S4[Phase 4: Drafting]
        D1[patent-drafter<br/>Draft specification] ~~~ C1[claims-architect<br/>Design claims]
    end
    
    subgraph S5[Phase 5: Review]
        A1[patent-auditor<br/>Full review] ~~~ A2[Grant rate prediction]
    end
    
    S1 --> S2 --> S3 --> S4 --> S5

Trigger:

"Help me write a patent: [technical idea]"
"I have an idea and want to apply for a patent"

Output Files:

  • TECH_DISCLOSURE.md - Technical disclosure framework
  • KEYWORD_STRATEGY.md - Keyword strategy
  • PATENT_SEARCH_REPORT.md - Search report
  • INVENTIVENESS_REPORT.md - Inventiveness evaluation report
  • Patent-*.md - Complete patent document (7-section standard format)
  • PATENT_AUDIT_REPORT.md - Audit report (with grant rate prediction)
  • Patent-*.docx - Word document (auto-converted)

Scenario 2: User Draft → Optimization

flowchart TB
    subgraph P1[Phase 1: Analysis]
        A1[patent-analyst<br/>Parse draft] ~~~ A2[Identify issues] ~~~ A3[Extract keywords]
    end
    
    subgraph P2[Phase 2: Search]
        R1[prior-art-researcher<br/>Targeted search] ~~~ R2[Analyze results]
    end
    
    subgraph P3[Phase 3: Evaluation]
        E1[inventiveness-evaluator<br/>Inventiveness risk] ~~~ E2[Enhancement suggestions]
    end
    
    subgraph P4[Phase 4: Optimization]
        D1[patent-drafter<br/>Optimize specification] ~~~ C1[claims-architect<br/>Strengthen claims]
    end
    
    subgraph P5[Phase 5: Review]
        Q1[patent-auditor<br/>Full review] ~~~ Q2[Grant rate prediction]
    end
    
    P1 --> P2 --> P3 --> P4 --> P5

Trigger:

"Help me optimize this patent: /path/to/patent.md"
"Review this patent and provide optimization suggestions"

Output Files:

  • PATENT_ANALYSIS_REPORT.md - Analysis report
  • PATENT_SEARCH_REPORT.md - Search report
  • INVENTIVENESS_REPORT.md - Inventiveness evaluation report
  • PATENT_OPTIMIZATION_SUGGESTIONS.md - Optimization suggestions
  • Patent-*.md - Optimized patent document
  • PATENT_AUDIT_REPORT.md - Audit report

Scenario 3: Agency Feedback → Evaluation

Use Case: User has submitted to a patent agency, received a search report or prior art, needs to evaluate whether to continue optimizing or cancel the application.

flowchart TB
    subgraph F1[Phase 1: Read Feedback]
        R1[Read search report] ~~~ R2[Read prior art] ~~~ R3[Read original draft]
    end
    
    subgraph F2[Phase 2: Comparative Analysis]
        A1[inventiveness-evaluator<br/>Comparison] ~~~ A2[Identify conflicts] ~~~ A3[Assess differentiation space]
    end
    
    subgraph F3[Phase 3: Decision]
        D1{Inventiveness space?}
        D2[🟢 Sufficient<br/>Recommend optimization]
        D3[🟡 Limited<br/>User confirmation needed]
        D4[🔴 No space<br/>Recommend cancellation]
        D1 --> D2
        D1 --> D3
        D1 --> D4
    end
    
    subgraph F4[Phase 4: Optimization]
        O1[patent-drafter<br/>Targeted optimization] ~~~ O2[Strengthen differences] ~~~ O3[patent-auditor<br/>Final review]
    end
    
    F1 --> F2 --> F3
    D2 --> F4
    D3 -->|User confirms| F4
    D4 --> X[Output cancellation report]

Trigger:

"The agency gave me a search report, help me see if I can optimize: /path/to/report.pdf"
"Here's the prior art, help me evaluate if I need to modify the patent"
"The agency says there's risk, should I continue or cancel?"

Output Files:

  • AGENCY_FEEDBACK_ANALYSIS.md - Agency feedback analysis
  • DECISION_RECOMMENDATION.md - Decision recommendation (continue/cancel)
  • PATENT_OPTIMIZATION_SUGGESTIONS.md - Targeted optimization suggestions

Decision Criteria:

Inventiveness SpaceBasisRecommendation
🟢 SufficientCore features not disclosed, clear differentiation pointsContinue optimization, strengthen differences
🟡 LimitedSome features disclosed, need repositioningUser confirmation required
🔴 No spaceCore features already disclosed, cannot circumventRecommend cancellation or redesign

Agent Details

Tech Miner (Technology Mining Expert)

Identity: Dr. Li, 15 years of technology assessment and innovation mining experience

Trigger: When user provides vague idea

Output: Technical disclosure framework

Use tech-miner to analyze the technical idea:
[User description]

Prior Art Researcher (Prior Art Search Expert)

Identity: Dr. Chen, 15 years of patent search experience

Trigger: When search is needed

Output: Search report + analysis

Use prior-art-researcher to search:
Keywords: [keywords]
Technical field: [field]

Search Channels (Default):

PriorityChannelToolPurpose
1Tavilytavily-searchQuick search, technical overview
2AMineraminer-open-academicAcademic papers + patent database
3Google Patentsweb_fetchGlobal patent full text
4GitHubtavily site:Open source projects
5Tech blogstavily site:Technical articles

⚠️ Patent Database APIs Recommended:

Default channels may not be sufficient for accurate patent prior art search. For professional patent search, recommend users to connect patent database APIs:

DatabaseAPI TypeCoverageBest For
Google PatentsPublic API100+ officesGlobal search
USPTOPublic APIUS patentsUS details
EPO (Espacenet)Public APIEuropean patentsEP search
CNIPAPublic APIChinese patentsCN search
WIPOPublic APIPCT applicationsInternational
Lens.orgFree APIGlobal patentsAcademic research

ClawHub Skill Discovery:

# Always check ClawHub for patent search skills
clawhub search patent
clawhub search "prior art"

Inventiveness Evaluator (Inventiveness Evaluation Expert)

Identity: Dr. Zhao, former examiner, 12 years of evaluation experience

Trigger: After search completion

Output: Inventiveness evaluation report (with risk score)

Use inventiveness-evaluator to evaluate inventiveness:
Patent document: /path/to/patent.md
Search report: PATENT_SEARCH_REPORT.md

Patent Drafter (Patent Drafting Expert)

Identity: Patricia, 12 years of drafting experience, 92% grant rate

Trigger: After inventiveness evaluation passes

Output: Complete patent document (7 sections)

Use patent-drafter to draft patent:
Patent title: [title]
Technical disclosure: TECH_DISCLOSURE.md
Search report: PATENT_SEARCH_REPORT.md
Inventiveness evaluation: INVENTIVENESS_REPORT.md

Claims Architect (Claims Architect)

Identity: Claude, 1000+ claims experience

Trigger: Parallel participation during drafting phase

Output: Claims document

Use claims-architect to design claims:
Patent document: /path/to/patent.md
Core inventive points: [points]

Patent Analyst (Patent Analyst)

Identity: Dr. Zhang, 12 years of patent analysis experience

Trigger: First step in optimization scenario

Output: Analysis report + optimization suggestions

Use patent-analyst to analyze patent:
Patent document: /path/to/patent.md

Patent Auditor (Patent Audit Expert)

Identity: Judge Wu, former examiner, reviewed 3000+ applications

Trigger: After drafting/optimization completion

Output: Audit report (with grant rate prediction)

Use patent-auditor to audit patent:
Patent document: /path/to/patent.md

Patent Value Appraiser (Patent Value Appraiser)

Identity: Ms. Lin, 10 years of patent valuation experience, certified IP asset appraiser

Trigger: User needs to evaluate existing patent value (transfer, pledge, financing)

Output: Patent value assessment report (5-dimension radar chart + market value range)

Use patent-value-appraiser to evaluate patent value:
Patent title: [title]
Or patent number: [number]
Purpose: pledge financing / licensing negotiation / technology transfer / M&A transaction

5 Evaluation Dimensions:

DimensionWeightContent
Technical Value25%Innovation degree, technical complexity, substitution difficulty
Legal Value25%Claim breadth, stability, invalidation resistance
Market Value25%Application scenarios, market size, competitive alternatives
Economic Value15%Cost savings, revenue potential, licensing income
Strategic Value10%Supply chain position, barrier strength, negotiation leverage

Patent Converter (Document Conversion Expert)

Identity: Alex, document conversion expert, proficient in Markdown → Word conversion

Trigger: Auto-triggered after patent-auditor review passes

Output: Word document (.docx), with embedded Mermaid diagrams

Conversion Flow:

  1. Parse 7 sections of patent Markdown
  2. Extract Mermaid code blocks and render to PNG
  3. Use Pandoc to convert section content
  4. Fill into Word template at corresponding positions
  5. Embed images into document
  6. Output to same directory as source file

Dependencies:

ToolPurpose
PandocMarkdown → Word conversion
mmdc (mermaid-cli)Mermaid → PNG rendering
python-docxWord document operations

Standard Patent Template (7 Sections)

# [Patent Title]

## 1. Related Prior Art and Their Defects or Deficiencies
### 1.1 Description of Prior Art
### 1.2 Defects or Deficiencies of Prior Art

## 2. Technical Improvements to Overcome the Above Defects

## 3. Alternative Solutions for Technical Improvements

## 4. Detailed Embodiments of the Technical Solution
### 4.1 System Architecture
### 4.2 Signal Logic Relationships
### 4.3 Implemented Functions
### 4.4 Specific Implementation Steps
### 4.5 Embodiment 1
(Describe included components/modules and their connections, signal logic; implemented functions and specific implementation steps)
## 5. Advantages of This Proposal Over Prior Art
(Achieved technical effects)
## 6. Related Drawings
(Structure diagrams with labeled component/module names; flowcharts with clear steps and process directions)
## 7. Claims
(Optional)

Language Adaptation

The agents automatically detect and use the user's language for output.

User Input LanguageOutput LanguageTemplate Format
EnglishEnglish7-Section Standard (English)
中文中文7章节标准模板(中文)
Other languagesUser's language7-Section Standard (translated)

Chinese 7-Section Template (中文七章节模板)

# [专利名称]

## 一、相关的现有技术及现有技术的缺陷或不足
### 1.1 现有技术描述
### 1.2 现有技术的缺陷或不足

## 二、为克服上述缺陷本提案的技术改进点

## 三、技术改进点的其他替代方案

## 四、详细的技术方案具体实施例
### 4.1 系统架构
### 4.2 信号逻辑关系
### 4.3 实现功能
### 4.4 具体实现步骤
### 4.5 实施例一

## 五、本提案相对现有技术的优点

## 六、相关附图

## 七、权利要求书

Key Rules for All Languages

  1. 7-Section Structure — Must follow the standard template regardless of language
  2. No Executable Code — Use pseudocode or flowcharts instead
  3. Quantified Technical Effects — Always quantify improvements (e.g., "30% efficiency increase")
  4. Comparison Table — Include comparison with prior art
  5. Mermaid Diagrams — Use flowcharts and architecture diagrams

Output Files Summary

Scenario 1: User Idea → Drafting

FileContentPhase
TECH_DISCLOSURE.mdTechnical disclosure frameworkTech mining
KEYWORD_STRATEGY.mdKeyword strategySearch
PATENT_SEARCH_REPORT.mdSearch reportSearch
INVENTIVENESS_REPORT.mdInventiveness evaluation reportEvaluation
Patent-*.mdComplete patent documentDrafting
PATENT_AUDIT_REPORT.mdAudit report (with grant rate)Review

Scenario 2: User Draft → Optimization

FileContentPhase
PATENT_ANALYSIS_REPORT.mdAnalysis reportAnalysis
PATENT_SEARCH_REPORT.mdSearch reportSearch
INVENTIVENESS_REPORT.mdInventiveness evaluation reportEvaluation
PATENT_OPTIMIZATION_SUGGESTIONS.mdOptimization suggestionsOptimization
PATENT_AUDIT_REPORT.mdAudit report (with grant rate)Review

Scenario 3: Agency Feedback → Evaluation

FileContentPhase
AGENCY_FEEDBACK_ANALYSIS.mdFeedback analysisAnalysis
DECISION_RECOMMENDATION.mdDecision recommendationDecision
PATENT_OPTIMIZATION_SUGGESTIONS.mdOptimization suggestions (if chosen)Optimization

Patent Value Assessment

FileContent
PATENT_VALUE_REPORT.md5-dimension value assessment + market value range

Installation Location

skills/
└── professional-patent-agents/
    ├── SKILL.md
    ├── agents/
    │   ├── tech-miner/
    │   ├── prior-art-researcher/
    │   ├── inventiveness-evaluator/
    │   ├── patent-drafter/
    │   ├── claims-architect/
    │   ├── patent-analyst/
    │   ├── patent-auditor/
    │   ├── patent-value-appraiser/
    │   └── patent-converter/
    └── skills/
        └── continuous-learning/

Auto-Conversion Flow

Trigger Conditions

Auto-invoke patent-converter to convert Markdown to Word when:

ConditionRequirement
patent-auditor review resultPassed
Grant rate prediction≥ 60% (low/medium risk)
User confirmationHigh risk (< 60%) requires user confirmation

Conversion Flow

flowchart TB
    A[patent-auditor<br/>Review complete] --> B{Grant rate ≥ 60%?}
    B -->|Yes| C[Auto-invoke<br/>patent-converter]
    B -->|No| D[Prompt user confirmation]
    D -->|Confirm convert| C
    D -->|Don't convert| E[Output Markdown only]
    C --> F[Output .docx file]
    F --> G[Notify user<br/>Ready for agency submission]

Output Location

Source directory/
├── Patent-1-xxx.md      # Source Markdown
├── Patent-1-xxx.docx    # Converted output (same directory)
├── Patent-2-xxx.md
├── Patent-2-xxx.docx
└── ...

Innovation Mining & Notifications

Work Record Source

memory/
├── 2026-03-16.md    # Daily work records
├── 2026-03-17.md
├── 2026-03-18.md
└── ...

Innovation Mining Rules

  1. Extract technical points from work records: Code commits, architecture designs, problem solutions
  2. Combine with industry trends: Search for latest technical developments
  3. Evaluate grant rate: Prior art search + Inventiveness evaluation
  4. Selection criteria: Grant rate ≥ 65%, Low/Medium risk level

Notification Format

📢 Innovation Mining Report

📊 X potential innovations discovered this week:

1. 【Highly Recommended】A method for XXX
   - Grant rate prediction: 70-80%
   - Risk level: Low
   - Innovation: XXX

2. 【Recommended】A system for XXX
   - Grant rate prediction: 65-75%
   - Risk level: Medium
   - Innovation: XXX

📁 Detailed documents: /patent/weekly/

💡 Suggestion: Prioritize applying for patent #1

*Professional Patent Agents Suite v1.0* *Author: BigPiPiHua* *Mail: 775262592@qq.com* *License: MIT* *Updated: 2026-03-19*

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

73.23%
按下载量换算1,787

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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