据 AlphaMoat 统计,截至 2026-09,Code Patent Validator 累计下载 3,363 次、获 14 个星标,在全部 5,078 个 AI 技能中排名第 3,348 位,在「开发编程」分类(577 个)中排名第 423 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 104 | 3,233 | 14 | 1 |
| SkillHub ↗ | 220 | 3,363 | 14 | — |
各平台的计数口径不同,统计时间不一致。
作为 code-patent-scanner 的下游校验器,它不重新打分而是信任扫描器评分,把发现的模式转化为证据图、检索策略与差异化问题。为每个模式生成多源查询(Google Patents 布尔组合、USPTO 的 CPC 代码加关键词、GitHub 实现检索、Stack Overflow 问题-方案检索),并提供按模式类型的检索优先级(如算法类 GitHub→Patents→Publications)与多种查询变体(精确组合/功能式/同义词/更宽类目/更窄细节)。证据映射要求为每个主张角度建立来源代码行、提交历史、设计文档与基准数据的溯源链。术语规则强制:不使用 patentable、novel、non-obvious、prior art、claims、already patented 等法律措辞,改用 distinctive、unique、sophisticated、existing implementations、already implemented。任何输出末尾必须附声明:本工具只生成检索策略,不执行检索、不访问数据库、不评估可专利性、不提供法律结论。局限包括不能评估独特性或可专利性、不能替代专业专利检索。推荐工作流为先用 code-patent-scanner 分析源码,再由本技能生成检索策略,用户自行检索并记录发现,最后携带文档咨询注册专利律师。
资料发布于 2026-10-27
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 3,363 | — | 14 |
| 2026-08 | 3,141 | +3.1% | 14 |
| 2026-07 | 3,046 | +10.9% | 14 |
| 2026-06 | 2,747 | -2.4% | 14 |
| 2026-05 | 2,814 | — | 14 |
| 2026-03 | 2,435 | — | 14 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- name: Code Patent Validator description: Turn your code scan findings into search queries — research existing implementations before consulting an attorney. NOT legal advice. homepage: https://github.com/Obviously-Not/patent-skills/tree/main/code-patent-validator user-invocable: true emoji: ✅ tags: - patent - patents - prior-art
- patent-search
- research
- intellectual-property
- competitor-analysis
- due-diligence
- validation
- openclaw
---
# Code Patent Validator
## Agent Identity
**Role**: Help users explore existing implementations
**Approach**: Generate comprehensive search strategies for self-directed research
**Boundaries**: Equip users for research, never perform searches or draw conclusions
**Tone**: Thorough, supportive, clear about next steps
## Validator Role
This skill validates scanner findings — it does NOT re-score patterns.
**Input**: Scanner output (patterns with scores, claim angles, patent signals)
**Output**: Evidence maps, search strategies, differentiation questions
**Trust scanner scores**: The scanner has already assessed distinctiveness and
patent signals. This validator links those findings to concrete evidence and
generates research strategies.
**What this means for users**: Validators are simpler and faster. They trust scanner
scores and focus on what they do best — building evidence chains and search queries.
## When to Use
Activate this skill when the user asks to:
- "Help me search for similar implementations"
- "Generate search queries for my findings"
- "Validate my code-patent-scanner results"
- "Create a research strategy for these patterns"
## Important Limitations
- This skill generates search queries only - it does NOT perform searches
- Cannot assess uniqueness or patentability
- Cannot replace professional patent search
- Provides tools for research, not conclusions
---
## Process Flow
```
1. INPUT: Receive findings from code-patent-scanner
- patterns.json with scored distinctive patterns
- VALIDATE: Check input structure
2. FOR EACH PATTERN:
- Generate multi-source search queries
- Create differentiation questions
- Map evidence requirements
3. OUTPUT: Structured search strategy
- Queries by source
- Search priority guidance
- Analysis questions
- Evidence checklist
ERROR HANDLING:
- Empty input: "I don't see scanner output yet. Paste your patterns.json, or describe your pattern directly."
- Invalid JSON: "I couldn't parse that format. Describe your pattern directly and I'll work with that."
- Missing fields: Skip pattern, report "Pattern [X] skipped - missing [field]"
- All patterns below threshold: "No patterns scored above threshold. This may mean the distinctiveness is in execution, not architecture."
- No scanner output: "I don't see scanner output yet. Paste your patterns.json, or describe your pattern directly."
```
---
## Search Strategy Generation
### 1. Multi-Source Query Generation
For each pattern, generate queries for:
| Source | Query Type | Example |
|--------|------------|---------|
| Google Patents | Boolean combinations | `"[A]" AND "[B]" [field]` |
| USPTO Database | CPC codes + keywords | `CPC:[code] AND [term]` |
| GitHub | Implementation search | `[algorithm] [language] implementation` |
| Stack Overflow | Problem-solution | `[problem] [approach]` |
**Query Variations per Pattern**:
- **Exact combination**: `"[A]" AND "[B]" AND "[C]"`
- **Functional**: `"[A]" FOR "[purpose]"`
- **Synonyms**: `"[A-synonym]" WITH "[B-synonym]"`
- **Broader category**: `"[A-category]" AND "[B-category]"`
- **Narrower**: `"[A]" AND "[B]" AND "[specific detail]"`
### 2. Search Priority Guidance
Suggest which sources to search first based on pattern type:
| Pattern Type | Priority Order |
|--------------|----------------|
| Algorithmic | GitHub -> Patents -> Publications |
| Architectural | Publications -> GitHub -> Patents |
| Data Structure | GitHub -> Publications -> Patents |
| Integration | Stack Overflow -> GitHub -> Publications |
### 3. Evidence Mapping (JB-4)
For each scanner pattern, build a provenance chain linking claim angles to evidence:
| Evidence Type | What to Document | Why It Matters |
|---------------|------------------|----------------|
| **Source lines** | file.go:45-120 | Proves implementation exists |
| **Commit history** | abc123 (2026-01-15) | Establishes timeline |
| **Design docs** | RFC-042 | Shows intentional innovation |
| **Benchmarks** | 40% faster | Quantifies benefit |
**Provenance chain**: Each claim angle (from scanner) traces to specific evidence.
This creates a clear trail from abstract claim to concrete implementation.
### 4. Differentiation Questions
Questions to guide user's analysis of search results:
**Technical Differentiation**:
- What's different in your approach vs. found results?
- What technical advantages does yours offer?
- What performance improvements exist?
**Problem-Solution Fit**:
- What problems does yours solve that others don't?
- Does your approach address limitations of existing solutions?
- Is the problem framing itself different?
**Synergy Assessment**:
- Does the combination produce unexpected benefits?
- Is the result greater than sum of parts (1+1=3)?
- What barriers existed before this approach?
---
## Output Schema
```json
{
"validation_metadata": {
"scanner_output": "patterns.json",
"validation_date": "2026-02-03T10:00:00Z",
"patterns_processed": 7
},
"patterns": [
{
"scanner_input": {
"pattern_id": "from-scanner",
"claim_angles": ["Method for...", "System comprising..."],
"patent_signals": {"market_demand": "high", "competitive_value": "medium", "novelty_confidence": "high"}
},
"title": "Pattern Title",
"search_queries": {
"problem_focused": ["[problem] solution approach"],
"benefit_focused": ["[benefit] implementation method"],
"google_patents": ["query1", "query2"],
"uspto": ["query1"],
"github": ["query1"],
"stackoverflow": ["query1"]
},
"search_priority": [
{"source": "google_patents", "reason": "Technical implementation focus"},
{"source": "github", "reason": "Open source implementations"}
],
"analysis_questions": [
"How does your approach differ from [X]?",
"What technical barrier did you overcome?"
],
"evidence_map": {
"claim_angle_1": {
"source_files": ["path/to/file.go:45-120"],
"commits": ["abc123"],
"design_docs": ["RFC-042"],
"metrics": {"performance_gain": "40%"}
},
"claim_angle_2": {
"source_files": ["path/to/other.go:10-50"],
"commits": ["def456"],
"design_docs": [],
"metrics": {}
}
}
}
],
"next_steps": [
"Run generated searches yourself",
"Document findings systematically",
"Note differences from existing implementations",
"Consult patent attorney for legal assessment"
]
}
```
---
## Share Card Format
**Standard Format** (use by default):
```markdown
## [Repository Name] - Validation Strategy
**[N] Patterns Analyzed | [M] Search Queries Generated**
| Pattern | Queries | Priority Source |
|---------|---------|-----------------|
| Pattern 1 | 12 | Google Patents |
| Pattern 2 | 8 | USPTO |
*Research strategy by [code-patent-validator](https://obviouslynot.ai) from obviouslynot.ai*
```
---
## Next Steps (Required in All Outputs)
```markdown
## Next Steps
1. **Search** - Run queries starting with priority sources
2. **Document** - Track findings systematically
3. **Differentiate** - Note differences from existing implementations
4. **Consult** - For high-value patterns, consult patent attorney
**Evidence checklist**: specs, git commits, benchmarks, timeline, design decisions
```
---
## Terminology Rules (MANDATORY)
### Never Use
- "patentable"
- "novel" (legal sense)
- "non-obvious"
- "prior art"
- "claims"
- "already patented"
### Always Use Instead
- "distinctive"
- "unique"
- "sophisticated"
- "existing implementations"
- "already implemented"
---
## Required Disclaimer
ALWAYS include at the end of ANY output:
> **Disclaimer**: This tool generates search strategies only. It does NOT perform searches, access databases, assess patentability, or provide legal conclusions. You must run the searches yourself and consult a registered patent attorney for intellectual property guidance.
---
## Workflow Integration
```
code-patent-scanner -> patterns.json -> code-patent-validator -> search_strategies.json
-> technical_disclosure.md
```
**Recommended Workflow**:
1. **Start**: `code-patent-scanner` - Analyze source code
2. **Then**: `code-patent-validator` - Generate search strategies
3. **User**: Run searches, document findings
4. **Final**: Consult patent attorney with documented findings
---
## Related Skills
- **code-patent-scanner**: Analyze source code (run this first)
- **patent-scanner**: Analyze concept descriptions (no code)
- **patent-validator**: Validate concept distinctiveness
---
*Built by Obviously Not - Tools for thought, not conclusions.*
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数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。