optimize-prompt-token-efficiency

name	optimize-prompt-token-efficiency
description	Iteratively optimizes prompts for token efficiency by maximizing information density - reduces verbosity, removes redundancy, tightens phrasing while preserving semantic content. Use when asked to compress, shorten, reduce tokens, tighten, maximize density, increase information density, or make a prompt more concise.

Optimize Prompt Token Efficiency

Iteratively optimize prompt token efficiency by maximizing information density through verification loops. Primary goal: reduce token consumption while preserving all semantic content for AI-consumed prompts (CLAUDE.md, skills, agent prompts, specs).

Overview

This skill transforms verbose prompts into token-efficient versions through:

1. Verification First - prompt-token-efficiency-verifier checks for inefficiencies before any changes
2. Optimization - Apply targeted compression based on verifier feedback
3. Re-verification - Verify compression is lossless, iterate if issues remain (max 5 iterations)
4. Output - Atomic replacement only after verification passes

Loop: Read → Verify → (Exit if efficient) → Optimize based on feedback → Re-verify → (Iterate if issues) → Output

Key principle: Don't try to optimize in one pass. The verifier drives all changes - if it finds no inefficiencies, the prompt is already token-efficient.

Workflow

Phase 0: Create Todo List (TodoWrite immediately)

Create todos tracking workflow phases. List reflects areas of work, not fixed steps.

Starter todos:

- [ ] Input validation
- [ ] Initial verification
- [ ] (Expand: optimization iterations on INEFFICIENCIES_FOUND)
- [ ] Output optimized prompt

Phase 1: Input Validation

Mark "Input validation" todo in_progress.

Step 1.1: Parse arguments

Extract file path from $ARGUMENTS. If no path provided, error with usage instructions.

Step 1.2: Validate file

• Check file exists using Read tool
• Verify supported type: .md, .txt, .yaml, .json
• If unsupported, error: "Unsupported file type. Supported: .md, .txt, .yaml, .json"

Step 1.3: Read and measure original

• Read file content
• Estimate token count: Math.ceil(content.length / 4) (approximate)
• Store original content and token count for comparison

Step 1.4: Store metadata

• original_path: Source file path
• original_content: Full prompt text
• original_tokens: Estimated token count
• working_path: /tmp/optimized-efficiency-{timestamp}.{ext} for iterations

Mark "Input validation" todo completed.

Phase 2: Initial Verification

Mark "Initial verification" todo in_progress.

Step 2.1: Copy to working path

Copy original content to working_path using Write tool (verification needs a file path).

Step 2.2: Run verifier first

Launch prompt-token-efficiency-verifier agent via Task tool BEFORE any optimization:

• subagent_type: "prompt-engineering:prompt-token-efficiency-verifier"
• prompt: "Verify prompt token efficiency. File: {working_path}. Check for redundancy, verbose phrasing, filler words, structural inefficiencies, and compression opportunities. Report VERIFIED if already efficient, or INEFFICIENCIES_FOUND with specific details."

Step 2.3: Handle verifier response

• If "VERIFIED": Mark todo completed, proceed directly to Phase 4 (Output) with message: "Prompt is already token-efficient. No changes needed."
• If "INEFFICIENCIES_FOUND": Mark todo completed, save the issues list, add "Optimization iteration 1" todo and proceed to Phase 3
• If verifier fails or returns unexpected format: Retry once with identical parameters. If retry fails, report error: "Verification failed - cannot proceed without verifier."

Step 2.4: Display verifier findings

If inefficiencies found, show user summary and proceed:

Verifier found {count} token efficiency issues. Proceeding with optimization...

Mark "Initial verification" todo completed.

Phase 3: Optimization Loop (Verifier-Driven)

Mark "Optimization iteration 1" todo in_progress.

Key principle: All fixes are driven by verifier feedback. Do NOT analyze the prompt independently - only fix the specific inefficiencies the verifier reported.

For each iteration from 1 to 5:

1. Apply compressions from verifier feedback: For each inefficiency in the verifier's report, apply the suggested compression. Write optimized version to working_path.

   • Only fix inefficiencies the verifier identified - do not add your own improvements

2. Re-verify: Launch prompt-token-efficiency-verifier agent via Task tool:

   • subagent_type: "prompt-engineering:prompt-token-efficiency-verifier"
   • prompt: "Verify compression is lossless. Original file: {original_path}. Compressed file: {working_path}. Compare semantic content - check for missing facts, altered meaning, lost emphasis, removed nuance. Report VERIFIED if lossless, or ISSUES_FOUND with specific gaps."

3. Handle response:

   • If "VERIFIED": mark todo completed, exit loop, proceed to Phase 4
   • If "ISSUES_FOUND" and iteration < 5: mark todo completed, save new issues list, add "Optimization iteration {next}" todo, continue to next iteration
   • If "ISSUES_FOUND" and iteration = 5: mark todo completed with note about unresolved issues, proceed to Phase 4 with warning
   • If verifier fails or returns unexpected format: display error to user, retry once with identical parameters. If retry fails, proceed to Phase 4 with warning: "Verification incomplete - manual review recommended."

Compression Techniques

Apply these techniques to fix inefficiencies flagged by the verifier:

Technique	Description	Before → After
Redundancy removal	Eliminate repeated concepts	"It is important to note that you should always remember to..." → "Always..."
Terse phrasing	Replace verbose constructions	"In order to accomplish this task, you will need to..." → "To do this:"
Filler elimination	Remove hedging, qualifiers, throat-clearing	"Make sure that you do not forget to include..." → "Include:"
Structural optimization	Merge/reorganize sections	"First X. After that Y. Then Z." → "Steps: X → Y → Z"
Context-aware abbreviation	Abbreviate terms after first mention	"Model Context Protocol server" (×10) → "MCP server" (after first)
Dense formatting	Use lists, tables, compact notation	Prose paragraphs → Tables, bullet lists

Transformation Rules:

1. Preserve ALL semantic information - Every fact, instruction, constraint, and example must be present
2. Preserve nuance and emphasis - Bold, caps, repetition, ordering that signals priority; intentional hedging (uncertainty was meaningful)
3. Restructuring allowed - Reorder, merge sections if it increases density WITHOUT losing priority signals
4. Format preservation - Output must be same format as input (markdown stays markdown)
5. No reduction target - 10% reduction with nuance preserved > 40% reduction with nuance lost

Avoid creating ambiguity:

• Don't merge conditions with different triggers ("when A, do X; when B, do Y" ≠ "when A/B, do X/Y")
• Keep explicit referents (don't reduce "Use Read tool" to "Use the tool" if context is unclear)
• Don't flatten relationships ("A requires B, C requires D" ≠ "A, C require B, D")
• Ensure scope is clear (qualifier applies to which items?)

Phase 4: Output

Mark "Output optimized prompt" todo in_progress.

Step 4.1: Calculate metrics

• Original token count (from Phase 1)
• Compressed token count: Math.ceil(compressed_content.length / 4)
• Reduction percentage: ((original - compressed) / original * 100).toFixed(0)

Step 4.2: Apply changes (atomic replacement)

If verification passed:

# Replace original atomically
mv {working_path} {original_path}

Step 4.3: Display results

If verification passed:

Optimized: {path}
Iterations: {count}
Original:  {original_tokens} tokens
Optimized: {compressed_tokens} tokens
Reduction: {percentage}%

Changes applied:
- {summary of compressions}

Status: Token-efficient and lossless

If verification failed after 5 iterations:

Optimized with warnings: {path}
Iterations: 5
Original:  {original_tokens} tokens
Optimized: {compressed_tokens} tokens
Reduction: {percentage}%

Unresolved issues:
- {list from last verification}

Review the changes manually.

Mark "Output optimized prompt" todo completed. Mark all todos complete.

Key Principles

Principle	Rule
Verify first	Always run verifier before any optimization; maybe prompt is already efficient
Verifier-driven	Only fix inefficiencies the verifier identifies - no independent analysis or improvements
Track progress	TodoWrite to track phases; expand todos on iteration
Losslessness	Never sacrifice semantic information for density; every fact must be preserved
Nuance preservation	Keep emphasis, intentional hedging, priority signals; 10% with nuance > 40% without
No ambiguity	Compressed must be as unambiguous as original
Verification required	Never output without verifier checking
Atomic output	Original untouched until verification passes

Edge Cases

Scenario	Handling
No input provided	Error: "Usage: /optimize-prompt-token-efficiency "
File not found	Error: "File not found: {path}"
Unsupported type	Error: "Unsupported file type. Supported: .md, .txt, .yaml, .json"
Already efficient	Verifier returns VERIFIED on first check → Report: "Prompt is already token-efficient. No changes needed."
Initial verifier fails	Retry once; if still fails, Error: "Verification failed - cannot proceed without verifier."
Re-verification fails	Display error, retry once; if retry fails, output with warning
YAML/JSON structure	Preserve structure validity, compress string values only
Very large file (>50KB)	Process as single unit
0-10% reduction	Success: "Content was already near-optimal density"
Verification fails 5x	Output best attempt with warning

Example Usage

# Optimize a verbose CLAUDE.md
/optimize-prompt-token-efficiency CLAUDE.md

# Optimize a skill file
/optimize-prompt-token-efficiency claude-plugins/my-plugin/skills/my-skill/SKILL.md

# Optimize an agent prompt
/optimize-prompt-token-efficiency agents/code-reviewer.md

Example Output

Optimized: docs/README.md
Iterations: 2
Original:  4,250 tokens
Optimized: 3,612 tokens
Reduction: 15%

Changes applied:
- Removed redundant intro section
- Consolidated overlapping examples
- Tersified verbose instructions
- Preserved emphasis markers and conditional logic

Status: Token-efficient and lossless

Prompt is already token-efficient. No changes needed.
Original: 1,995 tokens