OpenClaw skill
cellcog
Cellcog is an OpenClaw skill that enables agents to create and manage cellular automata simulations on 2D grids. Agents can initialize grids with custom cell states, define and apply evolution rules, step through generations, and query grid or cell properties. It supports visualization of grid states for analysis.
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How this skill works
- Validates and normalizes input parameters.
- Initializes the CA grid based on `initial_state` or random.
- For each generation in `generations`: Computes the next state for every cell using the Moore neighborhood (for 2D) or adjacent cells (1D).
- For each generation in `generations`: Applies birth/survival rules from `rules` to determine live/dead status.
- After evolution, serializes the `evolved_state` to JSON-compatible format.
- Runs a simple pattern matcher on the evolved state to produce `interpretation`, e.g., detecting stable patterns or oscillators mapped to predefined cognitive outputs.
When to use it
- When an agent needs to initialize and manage cellular grids for cognitive computation
- When evolving cellular automata states according to custom rules is part of the task
- When extracting or analyzing emergent patterns from cellular grid evolutions
Best practices
- Set the CELLCOG_API_KEY environment variable before running the skill
- Ensure OpenAI API key is configured for underlying model access
- Test the skill with sample inputs to verify functionality
- Monitor token usage to stay within rate limits
Example use cases
- Cellular Process Simulation: Simulate key biological processes such as signal transduction, gene expression, and metabolic pathways as documented in the capabilities.
- Single-Cell Cognitive Modeling: Model cognitive-like behaviors including decision-making and learning in single cells as described in the functionality.
- Cell Population Cognitive Modeling: Model interactions and cognitive behaviors in cell populations directly implied by the skill's modeling capabilities.
- Biological Hypothesis Testing: Test biological hypotheses by running simulations with varying parameters as listed in the capabilities.
- Simulation Data Analysis: Analyze simulation outputs for patterns and predictions as specified in the documented functionality.
FAQs
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