AlphaMoatによると、2026-09時点でCode Patent Validatorのダウンロード数は3,363、スター数は14で、AIスキル全体5,078件中3,348位、開発では577件中423位です。
| プラットフォーム | インストール数 | ダウンロード数 | スター数 | コメント |
|---|---|---|---|---|
| ClawHub ↗ | 104 | 3233 | 14 | 1 |
| SkillHub ↗ | 220 | 3363 | 14 | — |
プラットフォームごとに集計方法と集計時点が異なります。
code-patent-scanner の下流のバリデーターとして、パターンの再スコアリングは行わずスキャナーのスコアを信頼し、発見されたパターンを証拠マップ・検索戦略・差別化の質問に変えます。各パターンについて複数ソースのクエリ(Google Patents のブール組み合わせ、USPTO の CPC コード+キーワード、GitHub の実装検索、Stack Overflow の問題-解決検索)を生成し、パターンタイプ別の検索優先順位(例:アルゴリズム系は GitHub → Patents → Publications)と複数のクエリ変種(完全一致/機能型/同義語/より広いカテゴリ/より狭い詳細)を提供します。証拠マッピングでは、各クレーム角度についてソース行・コミット履歴・設計文書・ベンチマークの由来チェーンを構築します。用語規則は必須で、patentable、novel、non-obvious、prior art、claims、already patented を使わず、distinctive、unique、sophisticated、existing implementations、already implemented を使います。すべての出力の末尾には、検索戦略のみを生成し検索の実行・データベースアクセス・特許性の評価・法的結論は提供しないという免責文を付けます。独自性や特許性を評価できず、専門的な特許調査の代わりにもならないという制約もあります。推奨ワークフローは、まず code-patent-scanner でソースコードを分析し、本スキルが検索戦略を生成し、ユーザーが検索を実行して発見を記録し、最後に登録弁理士に相談します。
資料公開日 2026-10-27
スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。
2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。
| 月 | ダウンロード数 | 前月比 | スター数 |
|---|---|---|---|
| 2026-09 | 3363 | — | 14 |
| 2026-08 | 3141 | +3.1% | 14 |
| 2026-07 | 3046 | +10.9% | 14 |
| 2026-06 | 2747 | -2.4% | 14 |
| 2026-05 | 2814 | — | 14 |
| 2026-03 | 2435 | — | 14 |
作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 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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データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。