A portable Domain Specific Language using six universal primitives to design, translate, and explain complex game engine architectures.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install primitives-dsl
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 primitives-dsl 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 primitives-dsl?
The primitives-dsl skill provides a robust framework for building and analyzing game systems through a standardized vocabulary of six core components: LOOP, TILEGRID, CONTROLBLOCK, POOL, EVENT, and DISPATCHER. By using this Openclaw Skills resource, developers can create architectures that remain portable across vastly different environments, from legacy 68K systems to modern GPU-driven CUDA kernels and Entity Component Systems (ECS).
This skill is particularly valuable for developers who need to bridge the gap between low-level hardware constraints and high-level architectural design. It ensures that game logic is decoupled from platform-specific implementations, making it an essential tool for cross-platform engine development and refactoring legacy codebases into modern, maintainable structures through the power of Openclaw Skills.
primitives-dsl Use Cases
- Designing portable game or simulation loops for multi-platform deployment.
- Translating existing architectures between 68K, Cell/PPU, CUDA, and ECS environments.
- Streamlining the communication of complex engine structures to AI coding agents.
- Refactoring disorganized code into an explicit, state-and-flow-based architecture.
- Creating constrained-device or edge-computing game loops with stable memory footprints.
How primitives-dsl Works
- Identify the core components of your game system and map them to the six universal primitives defined in Openclaw Skills.
- Define the LOOP phase ordering and time-slicing logic to control the simulation cycle.
- Structure spatial indices using TILEGRID rules for adjacency and zone management.
- Organize behavior and constraints within the authoritative CONTROLBLOCK state records.
- Manage memory allocation through a bounded POOL to prevent performance bottlenecks in hot paths.
- Configure the DISPATCHER to route EVENT messages and manage scheduling policies across CPU threads or GPU kernels.
- Generate a comprehensive Primitive Map, Dataflow Sketch, and Portability Notes to finalize the architectural blueprint.
primitives-dsl Setup
To integrate this Openclaw Skills module into your workflow, include the primitives-dsl definition in your agent's skill library. You can invoke the skill using standardized prompts within your development environment:
# Example invocation for the AI agent
"Apply primitives-dsl to design a loop for [YOUR_SUBSYSTEM]. Provide a Primitive Map + Dataflow + Portability."
Ensure you have access to the quick card reference for a technical overview of primitive definitions and constraints as specified in the Openclaw Skills documentation.
primitives-dsl Data Schema & Taxonomy
The primitives-dsl skill organizes architectural data using a structured output contract to ensure consistency across different Openclaw Skills implementations.
| Primitive | Data Responsibility | Key Metadata |
|---|---|---|
| LOOP | Execution Phase ordering | Time-slice budgeting |
| TILEGRID | Spatial adjacency rules | Grid dimensions (2D/3D) |
| CONTROLBLOCK | Authoritative state | Flags, counters, handles |
| POOL | Bounded memory allocation | Entity/Job capacity |
| EVENT | Message payload | Routing key/channel |
| DISPATCHER | Work scheduling | FIFO, Priority, Fixed-step |
primitives-dsl Advanced Features
- Multi-architecture translation: Seamlessly map logic between 68K-era loops and modern parallelized GPU kernels using Openclaw Skills.
- AI Agent Optimization: Uses a high-density architectural vocabulary to minimize ambiguity when explaining engine structures to LLMs.
- Deterministic State Control: Provides explicit CONTROLBLOCK field definitions to ensure state predictability across different hardware targets.
- Performance Budgeting: Integrated DISPATCHER policies allow for fixed-step or budgeted simulation cycles within the Openclaw Skills framework.
SKILL.md
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