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Tencent SkillHub · AI

Code Patent Scanner

Scan your codebase for distinctive patterns — get structured scoring and evidence for patent consultation. NOT legal advice.

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High Signal

Scan your codebase for distinctive patterns — get structured scoring and evidence for patent consultation. NOT legal advice.

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Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
SKILL.md

Validation

  • Use the Yavira download entry.
  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. Paste one of the prompts below and point your agent at the extracted folder.
New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.4.0

Documentation

ClawHub primary doc Primary doc: SKILL.md 25 sections Open source page

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

Step 1: Repository Discovery

First, understand the codebase structure: Check if path is provided, otherwise use current directory Identify primary language(s) by file extensions Count total source files (exclude generated/vendor) 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 CharacteristicsHighCustom algorithms, data structures, core business logicMediumAPI handlers, service layers, utilitiesLowConfig, constants, simple CRUD, boilerplateSkipTests, 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: ElementQuestionProblemWhat 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: Method claim: "A method for [verb]ing, comprising the steps of..." System claim: "A system comprising: [component] configured to..." 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: DimensionRangeCriteriaDistinctiveness0-4How unique vs standard library/common approachesSophistication0-3Engineering complexity and eleganceSystem Impact0-3Effect on overall system behaviorFrame 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: SignalRangeCriteriaMarket 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)

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

Deep Mode (ON REQUEST)

Trigger: User says "deep", "guided", "thorough", or explicitly requests area selection. Categorize files by directory/module Identify high-priority candidates (max 5 areas) Present areas to user and wait for selection Analyze selected area, report findings Ask if user wants to continue with another area

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]*

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.

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
1 Docs
  • SKILL.md Primary doc