A specialized AI agent skill for designing, implementing, and debugging sophisticated gameplay randomness using the RNGNeeds Unity plugin.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install rngneeds-unity
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 rngneeds-unity 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 rngneeds-unity?
The rngneeds-unity skill empowers AI agents to act as expert Unity developers specializing in the RNGNeeds ecosystem. It provides deep technical guidance on building complex probability systems including weighted loot tables, gacha mechanics, and advanced deck-based card logic. By leveraging this skill, developers can ensure their gameplay randomness is mathematically sound, deterministic when needed, and easy to debug.
This integration within the Openclaw Skills library focuses on grounding AI responses in the actual RNGNeeds API. It moves beyond generic C# randomness to offer high-level gameplay patterns like repeat prevention, influence providers, and depleted list management, ensuring that AI-generated code and advice align perfectly with professional game design standards.
rngneeds-unity Use Cases
- Designing complex loot tables and gacha systems with weighted probabilities.
- Implementing card game mechanics like draw piles, discard piles, and graveyards using deck extensions.
- Creating dynamic gameplay odds that react to game state via influence providers.
- Debugging unexpected probability results or bias in random selection logic.
- Refactoring legacy Unity randomness code into structured RNGNeeds patterns.
How rngneeds-unity Works
- The agent identifies the specific task, such as writing a new probability list or explaining a shuffle plan.
- It consults the decision guide and core concept references to select the appropriate RNG pattern for the gameplay goal.
- The skill retrieves specific API signatures from the api-surface reference to ensure code accuracy.
- It applies guardrails to distinguish between complex states like base vs. final probability and disabled vs. removed items.
- The agent generates focused C# code or documentation-style explanations based on established workflows.
rngneeds-unity Setup
To integrate this skill into your workflow, ensure the RNGNeeds package is installed in your Unity project, then add the skill to your agent configuration:
# Example command to add the skill to your Openclaw environment
openclaw install rngneeds-unity
Ensure your agent has access to the /references directory containing the core concept and API surface documentation.
rngneeds-unity Data Schema & Taxonomy
The skill organizes its domain knowledge into a structured reference map to guide the AI:
| Reference File | Purpose |
|---|---|
| core-concepts.md | Mental models and terminology for probability lists. |
| api-surface.md | High-value classes, properties, and C# methods. |
| decision-guide.md | Heuristics for choosing between weighted picks and deck operations. |
| pitfalls.md | Debugging heuristics for common probability 'gotchas'. |
| examples.md | Compact Unity/C# code snippets for rapid implementation. |
rngneeds-unity Advanced Features
- Multi-agent support for collaborative game design and implementation.
- Deterministic seeding via KeepSeed and custom seed providers for reproducible game sessions.
- Advanced deck-order operations including cut, shuffle, and graveyard management.
- Influence provider integration for real-time, state-based probability recalculation.
- Precise handling of depletable lists and pick history to prevent repetitive gameplay outcomes.
SKILL.md
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