Co2 Chunking

---
name: co2-chunking
description: Apply CO2 Chunking to group related elements into meaningful units to reduce cognitive load.
version: 1.0.0
metadata: {"moltbot":{"nix":{"plugin":"github:hummbl-dev/hummbl-agent?dir=skills/CO-composition/co2-chunking","systems":["aarch64-darwin","x86_64-linux"]}}}
---

# CO2 Chunking

Apply the CO2 Chunking transformation to group related elements into meaningful units to reduce cognitive load.

## What is CO2?

**CO2 (Chunking)** Group related elements into meaningful units to reduce cognitive load.

## When to Use CO2

### Ideal Situations

- Assemble components into a coherent whole
- Integrate multiple solutions into a unified approach
- Design systems that depend on clear interfaces and seams

### Trigger Questions

- "How can we use Chunking here?"
- "What changes if we apply CO2 to this integrating two services?"
- "Which assumptions does CO2 help us surface?"

## The CO2 Process

### Step 1: Define the focus

```typescript
// Using CO2 (Chunking) - Establish the focus
const focus = "Group related elements into meaningful units to reduce cognitive load";
```

### Step 2: Apply the model

```typescript
// Using CO2 (Chunking) - Apply the transformation
const output = applyModel("CO2", focus);
```

### Step 3: Synthesize outcomes

```typescript
// Using CO2 (Chunking) - Capture insights and decisions
const insights = summarize(output);
```

## Practical Example

```typescript
// Using CO2 (Chunking) - Example in a integrating two services
const result = applyModel("CO2", "Group related elements into meaningful units to reduce cognitive load" );
```

## Integration with Other Transformations

- **CO2 -> DE3**: Pair with DE3 when sequencing matters.
- **CO2 -> SY8**: Use SY8 to validate or stress-test.
- **CO2 -> RE2**: Apply RE2 to compose the output.

## Implementation Checklist

- [ ] Identify the context that requires CO2
- [ ] Apply the model using explicit CO2 references
- [ ] Document assumptions and outputs
- [ ] Confirm alignment with stakeholders or owners

## Common Pitfalls

- Treating the model as a checklist instead of a lens
- Skipping documentation of assumptions or rationale
- Over-applying the model without validating impact

## Best Practices

- Use explicit CO2 references in comments and docs
- Keep the output focused and actionable
- Combine with adjacent transformations when needed

## Measurement and Success

- Clearer decisions and fewer unresolved assumptions
- Faster alignment across stakeholders
- Reusable artifacts for future iterations

## Installation and Usage

### Nix Installation

```nix
{
  programs.moltbot.plugins = [
    { source = "github:hummbl-dev/hummbl-agent?dir=skills/CO-composition/co2-chunking"; }
  ];
}
```

### Manual Installation

```bash
moltbot-registry install hummbl-agent/co2-chunking
```

### Usage with Commands

```bash
/apply-transformation CO2 "Group related elements into meaningful units to reduce cognitive load"
```

---
*Apply CO2 to create repeatable, explicit mental model reasoning.*