A sophisticated game discovery skill that uses a 69-dimension Gameplay DNA engine to provide deep, feel-based game recommendations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install gamelegend
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 gamelegend 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 GameLegend Discovery Agent?
GameLegend is a powerful addition to the ecosystem of Openclaw Skills designed to transform how AI agents handle gaming queries. Unlike traditional recommendation engines that rely solely on surface-level genre tags, this skill utilizes the GameLegend Gameplay DNA system. It analyzes over 1,100 titles across 69 distinct dimensions—including mechanics, feel, progression, and social dynamics—to find games that actually play and feel similar to a user's favorites.
By integrating this skill, your AI agent becomes a knowledgeable gaming companion capable of understanding nuanced preferences. Whether a user is looking for something relaxing with base-building elements or a high-stakes competitive strategy game, the GameLegend skill provides data-driven matches backed by cosine similarity vectors and high-intensity DNA highlights.
GameLegend Discovery Agent Use Cases
- Finding similar titles for users who say "I want a game like X."
- Generating personalized "what to play next" suggestions based on specific gameplay traits.
- Explaining the core appeal of a specific game using DNA highlights and intensity scores.
- Building long-term user taste profiles to provide proactive recommendations in chat interfaces.
How GameLegend Discovery Agent Works
- The agent receives a user query regarding a game title or a specific gameplay "vibe."
- For specific titles, the agent performs a search via the
/gamesendpoint to retrieve a unique kebab-case slug. - The agent calls the
/similarendpoint using that slug to fetch games ranked by cosine similarity DNA vectors. - For trait-based requests, the agent browses the 69 gameplay dimensions to map user descriptors (like "relaxing") to specific dimension IDs.
- The agent filters the database using these dimensions to find high-intensity matches (scores of 4-5) and presents the results with similarity percentages.
GameLegend Discovery Agent Setup
To get started with this member of the Openclaw Skills library, no API key is required, though you should respect the rate limit of 100 requests per minute. Configure your agent to point to the base URL:
GameLegend Discovery Agent Data Schema & Taxonomy
The skill organizes game information using a multi-dimensional taxonomy. Data is primarily structured around Slugs and DNA Vectors.
| Data Point | Description | Format |
|---|---|---|
| Slug | Unique identifier for games | kebab-case (e.g., elden-ring) |
| Dimensions | 69 gameplay traits across 9 categories | Integer (1-5 intensity) |
| Categories | Mechanics, Feel, Progression, Social, Aesthetic, etc. | Grouped Metadata |
| Similarity Score | Cosine similarity between game DNA vectors | Float (0.0 - 1.0) |
GameLegend Discovery Agent Advanced Features
- Multi-dimensional filtering: Combine multiple dimension IDs to find games that meet highly specific criteria.
- Taste Profile Persistence: The skill allows agents to maintain a mental model of user preferences, noting dislikes like 'grinding' to filter future results.
- Proactive Discovery: Leverages historical context to suggest games when a user expresses boredom or completes a title.
- DNA Highlight Extraction: Automatically identifies and surfaces traits with an intensity of 4 or 5 for rapid game assessment.
SKILL.md
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