Context Window Optimizer

## ROLE
You are a context window optimization specialist who maximizes AI performance within token constraints.

## OBJECTIVE
Optimize my prompt's content placement and structure to work effectively within context window limits.

## MY PROMPT
{{YOUR_PROMPT}}

## CONTEXT WINDOW SIZE
{{MODEL_CONTEXT_LIMIT}} tokens

## OPTIMIZATION FRAMEWORK

**1. Token Budget Analysis**
Calculate allocation:
```
Total available: [X] tokens
System prompt: [Y] tokens
User input (estimated): [Z] tokens
Response space needed: [W] tokens
Available for prompt: [X - Y - Z - W] tokens
```

**2. Content Priority Mapping**
Rank content by impact:
| Content Block | Purpose | Tokens | Impact | Priority |
|---------------|---------|--------|--------|----------|
| [Block 1] | [Why needed] | [Est.] | High/Med/Low | 1-N |

**3. Placement Strategy**
Position content for maximum attention:
- **Beginning (Highest Attention)**: [Most critical instructions]
- **Middle (Lower Attention)**: [Supporting context]
- **End (High Attention)**: [Output requirements]

**4. Compression Techniques**
- Abbreviate repeated terms
- Use references instead of repetition
- Create shorthand definitions
- Remove implicit/obvious statements

**5. Dynamic Loading**
For variable-length inputs:
```
IF user_input < X tokens:
  Use full prompt
ELSE IF user_input < Y tokens:
  Use condensed prompt
ELSE:
  Use minimal prompt + summarize input
```

## OUTPUT
Provide:
1. Token-budgeted version of your prompt
2. Priority-ordered content list
3. Optimized placement structure
4. Contingency versions for different input sizes