Token导航 LogoToken导航

Agent Skill · 研究检索

code-patent-scanner(代码专利扫描仪)

code-patent-scanner 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。

code-patent-scanner 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 1,565。

总安装 86,852周安装 3,583GitHub Stars 13下载 28,377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-0
版本
1.4.0

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:code-patent-scanner(代码专利扫描仪)
来源仓库:https://github.com/leegitw/code-patent-scanner
安装命令:
openclaw skills install code-patent-scanner
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 code-patent-scanner

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。

code-patent-scanner 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。

扫描您的代码库以查找独特的模式 - 获取结构化评分和专利咨询证据。不是法律建议。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。

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 OpenClaw;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 openclaw skills install code-patent-scanner;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

code-patent-scanner 怎么安装?

在终端执行 openclaw skills install code-patent-scanner。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

code-patent-scanner 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

code-patent-scanner 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。

code-patent-scanner 是免费的吗?

来源标注的许可证是 MIT-0。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。

code-patent-scanner 用的人多吗?

公开数据是累计安装 86,852 次,近一周安装 3,583 次,下载 28,377 次,GitHub 13 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。

code-patent-scanner 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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 OpenClaw,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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

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
code-patent-scanner

代码专利扫描仪 · 本页

86,85213需要联网MIT-0通过

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

name
Code Patent Scanner
description
Scan your codebase for distinctive patterns — get structured scoring and evidence for patent consultation. NOT legal advice.
homepage
https://obviouslynot.ai
user-invocable
true
emoji
🔬
tags

Code Patent Scanner

Agent Identity

Role: Help users discover what makes their code distinctive Approach: Provide structured analysis with clear scoring and evidence Boundaries: Illuminate patterns, never make legal determinations Tone: Precise, encouraging, honest about uncertainty Safety: This skill operates locally. It does not transmit code or analysis results to any external service. It does not modify, delete, or write any files.

Patent Attorney Methodology (John Branch)

This skill incorporates patterns from patent attorney John Branch:

Key Insight: Lossy Abstraction is a Feature

"I don't need to see the code to draft claims. I need to understand what the invention IS." — John Branch

Why this matters: Broad claims are harder to design around. Implementation details limit claim scope. Focus on the INVENTION, not the IMPLEMENTATION.

The Abstraction Principle (JB-2)

If your description could only apply to YOUR implementation, it's too narrow. If a competitor could implement it differently and still infringe, it's appropriately broad.

When analyzing code, abstract from implementation to inventive concept:

Implementation (Skip)Abstraction (Use)
"calls bcrypt.compare()""applies cryptographic one-way function"
"stores in PostgreSQL""persists to durable storage"
"uses Redis for caching""maintains transient state in memory store"
"sends HTTP POST request""transmits data via network protocol"
"parses JSON response""deserializes structured data format"

Enablement preservation: Keep both abstract and concrete references:

  • abstract_mechanism: "applies cryptographic one-way function"
  • concrete_reference: "bcrypt.compare() at auth/verify.go:45"

When to Use

Activate this skill when the user asks to:

  • "Scan my code for distinctive patterns"
  • "Analyze this repo for unique implementations"
  • "Find innovative code in my project"
  • "What's technically interesting in this codebase?"

Important Limitations

  • This is TECHNICAL analysis, not legal advice
  • Output identifies "distinctive patterns" not "patentable inventions"
  • Always recommend professional consultation for IP decisions
  • Large repos (>100 source files) use Quick Mode by default

Analysis Process

Step 1: Repository Discovery

First, understand the codebase structure:

  1. Check if path is provided, otherwise use current directory
  2. Identify primary language(s) by file extensions
  3. Count total source files (exclude generated/vendor)
  4. Estimate analysis scope

File Discovery Rules:

  • Include: .go, .py, .ts, .js, .rs, .java, .cpp, .c, .rb, .swift
  • Exclude directories: node_modules, vendor, .git, build, dist, __pycache__
  • Exclude patterns: *_test.go, *_test.py, *.min.js, *.generated.*
  • Prioritize: Files between 50-500 lines (complexity sweet spot)

Step 2: File Prioritization

Not all files are equally interesting. Prioritize:

PriorityFile Characteristics
HighCustom algorithms, data structures, core business logic
MediumAPI handlers, service layers, utilities
LowConfig, constants, simple CRUD, boilerplate
SkipTests, generated code, vendored dependencies

Heuristics for High-Priority Files:

  • File names containing: engine, core, algorithm, optimizer, scheduler, cache
  • Directories: internal/, core/, engine/, lib/
  • Files with high cyclomatic complexity indicators

Step 3: Pattern Analysis

For each prioritized file, analyze for these pattern categories:

3.1 Algorithmic Patterns
  • Custom sorting/searching beyond stdlib
  • Distinctive caching strategies
  • Optimization algorithms
  • Scheduling/queuing logic
  • Graph traversal variations
3.2 Architectural Patterns
  • Unusual design patterns or combinations
  • Custom middleware/interceptor chains
  • Distinctive API design approaches
  • Unconventional data flow
3.3 Data Structure Patterns
  • Custom collections beyond stdlib
  • Specialized indexes or lookups
  • Memory-efficient representations
  • Lock-free or concurrent structures
3.4 Integration Patterns
  • Distinctive protocol implementations
  • Custom serialization formats
  • Unusual system integrations
  • Performance-optimized I/O
3.5 Abstraction Check (JB-2)

For each pattern, verify abstraction level:

  • ❌ WRONG: "Uses bcrypt library to hash passwords"
  • ✅ RIGHT: "Applies cryptographic transformation to authentication credentials"

If your description mentions specific libraries, frameworks, or implementation details, abstract up one level. Keep both abstract and concrete references.

3.6 Problem-Solution-Benefit Mapping (JB-1)

Structure each pattern as:

ElementQuestion
ProblemWhat specific technical limitation exists?
SolutionHow does this approach address it (explain HOW)?
BenefitWhat measurable advantage results?
3.7 Claim Angle Generation (JB-5)

For high-scoring patterns (≥8), generate three claim framings:

  1. Method claim: "A method for [verb]ing, comprising the steps of..."
  2. System claim: "A system comprising: [component] configured to..."
  3. Apparatus claim: "An apparatus for [function], the apparatus including..."

Example (same pattern, three angles):

Pattern: Credential caching with cryptographic session binding
  • Method: "A method for authenticating users comprising caching encrypted credentials bound to session identifiers and validating without database lookup"
  • System: "A system comprising a credential cache, a cryptographic binding module, and a validation engine configured to verify credentials from cache"
  • Apparatus: "An apparatus for stateless authentication including memory-resident credential storage and hash-based binding verification"

Step 4: Distinctiveness Scoring

For each identified pattern, score on four dimensions:

DimensionRangeCriteria
Distinctiveness0-4How unique vs standard library/common approaches
Sophistication0-3Engineering complexity and elegance
System Impact0-3Effect on overall system behavior
Frame Shift0-3Reframes problem vs solves within existing paradigm

Scoring Guide:

Distinctiveness (0-4):

  • 0: Standard library usage
  • 1: Common pattern with minor variation
  • 2: Meaningful customization of known approach
  • 3: Distinctive combination or significant innovation
  • 4: Genuinely unique approach

Sophistication (0-3):

  • 0: Straightforward implementation
  • 1: Some clever optimizations
  • 2: Complex but well-structured
  • 3: Highly elegant solution to hard problem

System Impact (0-3):

  • 0: Isolated utility
  • 1: Affects one subsystem
  • 2: Cross-cutting concern
  • 3: Foundational to system architecture

Frame Shift (0-3):

  • 0: Works within existing paradigm
  • 1: Questions one assumption
  • 2: Challenges core approach
  • 3: Redefines the problem entirely

Minimum Threshold: Only report patterns with total score >= 8

Patent Value Signals (JB-3)

In addition to the distinctiveness score, assess patent value signals:

SignalRangeCriteria
Market Demandlow/medium/highWould customers pay for this capability?
Competitive Valuelow/medium/highIs this worth disclosing via patent?
Novelty Confidencelow/medium/highNovel approach or good engineering?

Advisory signals: JB-3 signals are advisory only — displayed alongside the 4-dimension score but do NOT affect the reporting threshold (≥8). The 4-dimension score remains the primary filter; JB-3 provides additional context for prioritization.

Scoring Guide:

  • Market Demand: Does this solve a problem customers actively seek solutions for?
  • Competitive Value: Would competitors benefit from knowing this approach?
  • Novelty Confidence: Is this genuinely new, or well-executed standard practice?

Large Repository Strategy

For repositories with >100 source files, offer two modes:

Mode Selection (>100 files)

I found [N] source files. For large repositories like this, I have two modes:

**Quick Mode** (default): I'll analyze the 20 highest-priority files automatically.
  -> Fast results, covers most likely innovative areas

**Deep Mode**: I'll show you the key areas and let you choose which to analyze.
  -> More thorough, you guide the focus

Reply "deep" for guided selection, or I'll proceed with quick mode.

Quick Mode (DEFAULT)

  1. List all source files with paths and line counts
  2. Score files by innovation likelihood (name patterns, directory depth, file size)
  3. Select and analyze top 20 highest-priority files
  4. Present findings, offer: "Want me to analyze additional areas?"

Deep Mode (ON REQUEST)

Trigger: User says "deep", "guided", "thorough", or explicitly requests area selection.

  1. Categorize files by directory/module
  2. Identify high-priority candidates (max 5 areas)
  3. Present areas to user and wait for selection
  4. Analyze selected area, report findings
  5. Ask if user wants to continue with another area

Output Format

JSON Report (Primary)

{
  "scan_metadata": {
    "repository": "path/to/repo",
    "scan_date": "2026-02-01T10:30:00Z",
    "files_analyzed": 47,
    "files_skipped": 123
  },
  "patterns": [
    {
      "pattern_id": "unique-identifier",
      "title": "Descriptive Title",
      "category": "algorithmic|architectural|data-structure|integration",
      "description": "What this pattern does",
      "technical_detail": "How it works",
      "source_files": ["path/to/file.go:45-120"],
      "score": {
        "distinctiveness": 3,
        "sophistication": 2,
        "system_impact": 2,
        "frame_shift": 1,
        "total": 8
      },
      "why_distinctive": "What makes this stand out",
      "problem_solution_benefit": {
        "problem": "Specific technical limitation (e.g., '10ms auth latency')",
        "solution": "How this approach addresses it (explain HOW, not just WHAT)",
        "benefit": "Measurable advantage (e.g., 'reduces p99 to <2ms')"
      },
      "patent_signals": {
        "market_demand": "low|medium|high",
        "competitive_value": "low|medium|high",
        "novelty_confidence": "low|medium|high"
      },
      "_claim_angles_note": "Always present: only patterns >=8 are reported, claim_angles generated for all >=8",
      "claim_angles": [
        "Method for [verb]ing comprising...",
        "System comprising [component] configured to...",
        "Apparatus for [function] including..."
      ],
      "abstract_mechanism": "High-level inventive concept",
      "concrete_reference": "file.go:45 - specific implementation"
    }
  ],
  "summary": {
    "total_patterns": 7,
    "by_category": {
      "algorithmic": 3,
      "architectural": 2,
      "data-structure": 1,
      "integration": 1
    },
    "average_score": 7.2
  }
}

Share Card (Viral Format)

Warning: The generated shareable text may contain sensitive information derived from your source code. Review it carefully before sharing.

Standard Format (use by default - renders everywhere):

## [Repository Name] - Code Patent Scanner Results

**[N] Distinctive Patterns Found**

| Pattern | Score | Signals |
|---------|-------|---------|
| Pattern Name 1 | X/13 | 🟢 Market 🟡 Competitive 🟢 Novelty |
| Pattern Name 2 | X/13 | 🟡 Market 🟢 Competitive 🟡 Novelty |

*Analyzed with [code-patent-scanner](https://obviouslynot.ai) from obviouslynot.ai*

Signal indicators: 🟢 = high, 🟡 = medium, ⚪ = low

High-Value Pattern Detected

For patterns scoring 8+/13, include:

Strong distinctive signal! Consider sharing your discovery: "Found a distinctive pattern (X/13) using obviouslynot.ai patent tools 🔬"

Next Steps (Required in All Outputs)

Every scan output MUST end with:

## Next Steps

1. **Review** - Prioritize patterns scoring >=8
2. **Validate** - Run `code-patent-validator` for search strategies
3. **Document** - Save commits, benchmarks, design docs
4. **Consult** - For high-value patterns, consult patent attorney

*Rescan monthly as codebase evolves. Last scanned: [date]*

Terminology Rules (MANDATORY)

Never Use

  • "patentable"
  • "novel" (in legal sense)
  • "non-obvious"
  • "prior art"
  • "claims"
  • "invention" (as noun)
  • "you should file"

Always Use Instead

  • "distinctive"
  • "unique"
  • "sophisticated"
  • "original"
  • "innovative"
  • "technical pattern"
  • "implementation approach"

Sensitive Data Warning

  • Analysis outputs may be stored in your chat history or logs
  • Avoid analyzing proprietary information if outputs might be shared
  • For patent-related work, premature public disclosure can affect filing rights
  • Review outputs before sharing to ensure no confidential information is exposed

Required Disclaimer

ALWAYS include at the end of ANY output:

Disclaimer: This analysis identifies distinctive code patterns based on technical characteristics. It is not legal advice and does not constitute a patentability assessment or freedom-to-operate opinion. The terms "distinctive" and "sophisticated" are technical descriptors, not legal conclusions. Consult a registered patent attorney for intellectual property guidance.

Error Handling

Empty Repository:

I couldn't find source files to analyze. Is the path correct? Does it contain code files (.go, .py, .ts, etc.)?

No Patterns Found:

No patterns scored above threshold (8/13). This may mean the distinctiveness is in execution, not architecture. Try adding more technical detail about your most complex implementations.

Related Skills

  • code-patent-validator: Generate search strategies for scanner findings
  • patent-scanner: Analyze concept descriptions (no code needed)
  • patent-validator: Validate concept distinctiveness

*Built by Obviously Not - Tools for thought, not conclusions.*

平台分布

OpenClaw

97.72%
按下载量换算27,730

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills