An intelligent architecture refactoring skill that converts user design ideas into formal RFC documents and enforces them via automated Cursor rules.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tvs-inksnow-arch
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install tvs-inksnow-arch using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is TVS Inksnow Arch?
TVS Inksnow Arch is a specialized structural design and policy-enforcement skill tailored for AI-assisted software development. It acts as a professional architectural consultant that interviews the developer, diagnoses system bottlenecks, and captures refactoring goals inside a structured Request for Comments (RFC) document. By integrating with Openclaw Skills, this agent turns abstract designs into concrete software policies.
Once the RFC is approved, the skill automatically scans the existing directory structure and builds customized, machine-readable rule files (.cursor/rules/*.mdc) that govern the codebase. These rules ensure that any future code generated by your AI copilot strictly adheres to your chosen design principles, such as Domain-Driven Design (DDD), Clean Architecture, or Progressive Layer Separation, maintaining high code quality and preventing architectural drift over time.
TVS Inksnow Arch Use Cases
- Clean Architecture Refactoring: Splitting coupled systems into structured layers including core business logic, adapters, infrastructure, and presentation views.
- Modularizing Legacy Projects: Transitioning flat or cluttered codebases into domain-driven micro-modules or progressive multi-platform projects.
- Rule Enforcement: Establishing automated system boundaries in Cursor to prevent AI assistants from putting platform-specific dependencies inside pure business logic directories.
- State Management Restructuring: Organizing or replacing complex data stores such as Redux, Zustand, or Pinia with clear layer contracts.
How TVS Inksnow Arch Works
- Dependency Analysis: Scans the workspace to check if the specialized interview package from Openclaw Skills is present to guide the consultation phase.
- Interactive Interviewing: Conducts a step-by-step architectural interview querying code dependencies, target frameworks, state models, platform requirements, and refactoring governance.
- Crystallizing the RFC: Formulates a detailed technical blueprint saved directly to
docs/architecture-refactor-rfc.mdmapping target topologies and execution roadmaps. - Workspace Topology Analysis: Investigates package configurations, build scripts, entry files, and existing rules to analyze current-state constraints.
- Automated Rule Generation: Compiles the approved architectural patterns into strict, negative-pattern-supported
.mdcrules placed inside.cursor/rules/. - Handoff Coordination: Integrates with local memory and agent state systems to prepare the workspace for the active refactoring phase.
TVS Inksnow Arch Setup
To deploy this refactoring workflow within your project ecosystem, create the skill files in your active workspace or global user directory:
# Initialize the skill in your project's local directory
mkdir -p .cursor/skills/tvs-inksnow-arch
# Populate the SKILL.md file with the rule configurations
touch .cursor/skills/tvs-inksnow-arch/SKILL.md
# Recommended: Add companion capabilities to get advanced interview pipelines
mkdir -p .cursor/skills/tvs-deep-interview
Once loaded, the AI agent will automatically trigger the architecture workflow when it detects expressions like "I want to refactor standard folder structures" or "clean up global state mechanics."
TVS Inksnow Arch Data Schema & Taxonomy
The TVS Inksnow Arch skill organizes its structural outputs across two primary locations to separate developer-facing plans from AI-facing rules:
| Location | File Pattern | Type | Target Audience |
|---|---|---|---|
docs/ |
architecture-refactor-rfc.md |
Human Blueprint | Developers / System Tech Leads |
.cursor/rules/ |
01-架构边界.mdc, 02-*.mdc |
Machine Constraints | Cursor Agent / LLM Copilot |
RFC Blueprint Structure
The output blueprint contains detailed sections including:
- Meta-data: Refactoring versioning, target dates, and governance approaches.
- Topology Mapping: Comprehensive text-based folder visualization.
- Dependency Maps: Monolithic or multi-tier import rules with clear allowed/forbidden states.
- Governance Paradigm: Defined boundaries separating incremental adaptation from total system rewrites.
TVS Inksnow Arch Advanced Features
- Context-Aware Downgrades: Seamlessly switches to a local, lightweight 8-question consultation sequence if the parent interview module is missing.
- Layered Code Separation Templates: Bundles out-of-the-box configuration systems separating core logic, technological drivers, and UI layouts.
- Double-Sided Governance Modes: Empowers users to run strict one-time overhauls or set up gradual transitional guardrails using custom project markers.
- Negative Spec Enforcement: Embeds explicit anti-patterns inside rulesets to significantly improve LLM rule adherence by providing examples of forbidden code.
SKILL.md
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