skills.rest
jvsandhu
jvsandhuCOMMUNITY
Explore all skills by this author →
GitHub

prompt_optimizer

Rewrites and compresses prompts to reduce token usage while preserving output accuracy.

Updated Jan 14, 2026
Search Tags:#prompt engineering#prompt optimization#llm#cost optimization#a/b testing#prompt chaining#token reduction
One-click install
npx skills add https://github.com/jvsandhu/agentic-skills --skill prompt-optimizer-jvsandhu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt_optimizer
Source: https://github.com/jvsandhu/agentic-skills/tree/main/skills/prompt_optimizer
Command: npx skills add https://github.com/jvsandhu/agentic-skills --skill prompt-optimizer-jvsandhu

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Existing prompts often waste tokens through redundant phrasing or fail to produce consistent results, driving up cost and lowering reliability. This Skill provides a structured methodology to analyze, rewrite, and verify prompts so they use fewer tokens without losing accuracy. ## Core Features & Use Cases - Analysis & Benchmarking: Identify where a prompt fails or over-consumes tokens, and record a baseline success rate and average token count. - Rewriting & Compression: Remove unnecessary clauses, use clear imperative verbs, and split large tasks into chained prompts. - A/B Verification: Compare the optimized prompt against the original on identical inputs, scoring accuracy, speed, and cost. - Use Case: A team running a customer-support LLM workflow uses this Skill to cut a 2,000-token system prompt down by over 20% while confirming response quality stays consistent. ## Quick Start Analyze my current prompt, reduce its token count by at least 20 percent, and verify the optimized version still produces accurate and consistent outputs.

Frequently Asked Questions about prompt_optimizer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I reduce token usage in my LLM prompts?▼

Analyze the prompt for redundant clauses, rewrite using clear imperative verbs, and remove stopwords without losing meaning. Splitting one large prompt into smaller chained prompts also reduces per-call token consumption.

How to test if an optimized prompt still works correctly?▼

Run an A/B test by sending identical inputs to both the original and optimized prompts, then compare outputs. Score the results on response accuracy, speed, and cost to confirm no quality regression.

What is prompt chaining and when should I use it?▼

Prompt chaining splits a complex task into a sequence of smaller, focused prompts instead of one giant prompt. Use it when a single prompt produces inconsistent results or consumes excessive tokens on multi-step tasks.

How much token savings can prompt compression achieve?▼

This methodology targets at least 20 percent token savings as its verification checkpoint. Actual savings depend on how verbose the original prompt is and how much redundancy can be removed without losing meaning.

Why does my optimized prompt produce inconsistent output formats?▼

Aggressive compression can remove formatting instructions the model relied on. Check that output format specifications survived the rewrite, and verify format consistency as part of the A/B testing phase.

Related Skills

Explore More Software Engineering Skills→
bmsull560bmsull560

evals

Evaluate agent performance and extraction accuracy in LangGraph pipelines.

Community
Advanced
monamaretmonamaret

prompt-engineer

Design and optimize LLM prompts to reduce token usage, latency, and cost.

Community
Advanced
chicanoandres702chicanoandres702

llm-evaluation

Evaluate LLM outputs with automated metrics, human judgments, and A/B testing.

Community
Advanced
lebsrallebsral
11

ai-fine-tuning

Fine-tune models on labeled datasets using BootstrapFinetune workflows.

Community
Advanced