How does it optimize for the Claude context window?

It identifies 'Lost-in-the-Middle' issues where critical instructions are buried in the center of a long prompt, recommending they be moved to the beginning or end where the model's attention is highest.

Can the Prompt Optimizer automatically fix my prompt files?

Yes, by using the --fix flag, the skill will automatically apply corrections to high-certainty issues, such as converting negative-only constraints into positive instructions.

What is the difference between enhance-prompts and enhance-agent-prompts?

The enhance-prompts skill focuses on general prompt quality such as clarity and structure, while enhance-agent-prompts is specifically designed for agent configuration files containing YAML frontmatter.

Why does this skill flag 'think step-by-step' as an anti-pattern?

Modern models like Claude 3.5 and 4.x are fine-tuned for internal reasoning. Explicitly asking them to think step-by-step is often redundant and consumes unnecessary tokens without significantly improving accuracy.

Prompt Optimizer

Name: Prompt Optimizer
Author: avifenesh

byavifenesh

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311

Productivity & Workflow

Improves AI prompts by analyzing structure, clarity, and reliability based on Claude-specific best practices.

About

The Prompt Optimizer skill acts as an automated prompt engineer for Claude Code, scanning your markdown or text files to identify clarity issues, structural weaknesses, and anti-patterns. It leverages deep knowledge of Claude’s architecture—including XML tagging, context window behavior, and modern reasoning capabilities—to transform vague or inefficient instructions into high-performing system and user prompts. By applying research-backed patterns like few-shot examples and instruction hierarchy, it ensures your AI interactions are deterministic, reliable, and token-efficient.

Key Features

Automated analysis of prompt clarity, structure, and output reliability
Context window optimization to prevent 'Lost-in-the-Middle' performance degradation
Auto-fix capabilities for high-certainty issues like aggressive emphasis and negative constraints
Claude-specific XML tag optimization and few-shot example validation
Detection and removal of redundant Chain-of-Thought instructions
311 GitHub stars

Use Cases

Refining complex system prompts for AI agents to ensure deterministic output
Standardizing prompt templates across a repository to improve team collaboration
Modernizing legacy prompts for Claude 3.5 and 4.x by removing redundant reasoning steps

About

Key Features

Automated analysis of prompt clarity, structure, and output reliability
Context window optimization to prevent 'Lost-in-the-Middle' performance degradation
Auto-fix capabilities for high-certainty issues like aggressive emphasis and negative constraints
Claude-specific XML tag optimization and few-shot example validation
Detection and removal of redundant Chain-of-Thought instructions
311 GitHub stars

Use Cases

Refining complex system prompts for AI agents to ensure deterministic output
Standardizing prompt templates across a repository to improve team collaboration
Modernizing legacy prompts for Claude 3.5 and 4.x by removing redundant reasoning steps