Context Engineering Optimizer

Optimize content for AI consumption by reducing tokens while preserving essential information.

Core Principle

Context is finite. Every token depletes the attention budget. Optimize ruthlessly.

Reference: context-engineering-guide.md

Optimization Workflow

Use TodoWrite

Step 1: Create Draft

Analyze input and create optimized version:

Remove/Reduce:

• Redundant explanations (AI already knows common knowledge)
• Verbose transitions ("In this section, we will discuss...")
• Duplicate information across sections
• Filler words and phrases
• Overly detailed examples when one suffices
• Charts/diagrams/tables if text conveys same info more efficiently

Preserve:

• Domain-specific knowledge AI lacks
• Concrete examples that demonstrate behavior
• Critical constraints and rules
• Structural organization (headers, sections)
• Actionable instructions

Format choices:

• Prefer bullet points over prose
• Use imperative form
• Choose text OR graphical representation, not both
• Keep code examples minimal but complete

Language:

• English no matter the original language unless otherwise specified through prompt
• Most token-efficient for current models
• Preserves technical terminology accuracy

XML + Markdown tables:

<!-- WRONG -->
<data>
| Col | Val |
|-----|-----|
| A   | B   |
</data>

<!-- CORRECT - empty lines required -->
<data>

| Col | Val |
|-----|-----|
| A   | B   |

</data>

Step 2: Compare and Refine

Compare draft against original:

Check for information loss:

• Critical details missing?
• Context for understanding removed?
• Edge cases dropped that matter?

Check for over-optimization:

• Can sections be further condensed?
• Any remaining redundancy?
• Self-evident comments still present?

Restore if:

• Meaning becomes ambiguous
• Important nuance lost
• Actionability reduced

Step 3: Output

For materials (files to overwrite):

• Overwrite original with optimized version

For transient content:

• Output the optimized version as final result

Quality Criteria

Optimized content should:

• Reduce token count by 30-70% typically
• Preserve all actionable information
• Maintain clear structure
• Be self-contained (no dangling references)
• Target the Goldilocks zone: specific enough to guide, flexible enough to adapt