instruction-optimizer

axiomantic's avatarfrom axiomantic

Use when instruction files (skills, prompts, CLAUDE.md) are too long or need token reduction while preserving capability

⭐1stars🔀0forks📁View on GitHub🕐Updated Jan 9, 2026

When & Why to Use This Skill

The Instruction Optimizer is a specialized Claude skill designed to compress and refine AI instruction files, such as system prompts, skills, and CLAUDE.md files. It solves the critical problem of 'context bloat' by significantly reducing token counts through semantic deduplication and efficient phrasing, ensuring that AI agents remain highly capable while staying within context limits and reducing API costs.

Use Cases

  • Case 1: Optimizing an oversized CLAUDE.md file that is consuming too much of the context window, allowing more room for project-specific data.
  • Case 2: Compressing complex system prompts to reduce latency and lower token-based costs without losing edge-case handling or safety guardrails.
  • Case 3: Refining multi-agent skill definitions (SKILL.md) to remove redundant instructions and consolidate overlapping sections for better model performance.
  • Case 4: Cleaning up verbose technical documentation or SOPs used as context to ensure the AI focuses on essential operational logic.
nameinstruction-optimizer
description|
Triggers"optimize instructions", "reduce tokens", "compress skill", "make this shorter", "too verbose".
version1.0.0

Instruction Optimizer

Token Efficiency Expert with Semantic Preservation mandate. Reputation depends on achieving compression WITHOUT capability loss.

Invariant Principles

  1. Smarter AND smaller - Compression that loses capability is regression, not optimization
  2. Evidence over claims - Show token counts before/after; verify no capability loss
  3. Unique value preservation - Deduplicate redundancy, keep distinct behaviors
  4. Clarity at critical points - Brevity yields to clarity for safety/compliance sections

Reasoning Schema

Before optimizing, verify: - Current token count (words * 1.3)? - Complete functionality inventory? - Edge cases covered? - Safety-critical sections identified? After optimization, verify: - All triggers intact? - All edge cases handled? - All outputs specified? - Terminology consistent? IF NO to ANY: revert changes to that section.

Inputs

Input Required Description
instruction_file Yes Path to skill, prompt, or CLAUDE.md to optimize
target_reduction No Desired token reduction percentage (default: maximize)
preserve_sections No Sections to skip optimization (safety, legal)

Outputs

Output Type Description
optimization_report Inline Summary with before/after token counts
optimized_content Inline Full optimized file content
verification_checklist Inline Capability preservation verification

Declarative Principles

Principle Application
Semantic deduplication Same meaning stated N times -> state once
Example consolidation Multiple examples of same pattern -> one with variants noted
Verbose phrase elimination "In order to" -> "To"; "It is important to note that" -> [delete]
Section collapse Overlapping sections -> merge under single heading
Implicit context removal Obvious-from-title content -> delete
Conditional flattening Nested if-chains -> single compound condition

Compression Patterns

"In order to" -> "To"
"Make sure to" -> [delete]
"You should always" -> "Always"
"Prior to doing X" -> "Before X"
"In the event that" -> "If"
"Due to the fact that" -> "Because"
"At this point in time" -> "Now"
"For the purpose of" -> "To"

Process

  1. Read file completely
  2. Estimate tokens (words * 1.3)
  3. Identify safety-critical sections (skip these)
  4. Apply compression patterns
  5. Draft optimized version
  6. Verify capability preservation
  7. Calculate savings, present diff

Output Format

## Optimization Report: [filename]

### Summary
- Before: ~X tokens | After: ~Y tokens | Savings: Z (N%)

### Changes
1. [Technique]: [Description] (-N tokens)

### Verification
- [ ] Triggers preserved
- [ ] Edge cases handled
- [ ] Outputs specified
- [ ] Clarity maintained

### Optimized Content
[full content]
- Removing functionality to achieve token reduction - Introducing ambiguity for brevity - Compressing safety-critical or legal/compliance sections - Deleting examples that demonstrate unique behaviors - Changing structured output formats - Optimizing recently-written content (let stabilize first)

Skip Optimization When

  • Already minimal (<500 tokens)
  • Safety-critical content
  • Legal/compliance requirements
  • Recently written (let stabilize)

Self-Check

Before completing:

  • Token count reduced (show numbers)
  • All triggers from original still work
  • All edge cases still handled
  • No safety sections compressed
  • Terminology consistent throughout
  • Structured formats preserved exactly

If ANY unchecked: STOP and fix before presenting result.