Is the optimization process fully automated?

No, it uses a human-in-the-loop approach where the AI analyzes the prompt and proposes changes with evidence, but requires explicit user approval before integration.

Does it support multi-agent workflows?

Yes, it includes specific references for multi-turn flows and multi-agent orchestration, covering patterns like Chain-of-Verification and Multi-Chain Reasoning.

What documentation does it use for optimization?

It relies on internal single-turn and multi-turn pattern references to ensure all modifications are grounded in documented behavioral impacts rather than general knowledge.

What is the Claude Prompt Optimizer skill?

It is a specialized capability for Claude Code that helps developers refine their system prompts using proven prompt engineering techniques and research-backed patterns.

When should I use the lightweight process?

The lightweight process is ideal for simple prompts under 20 lines that have a single purpose and do not contain complex branching or conditional logic.

Claude Prompt Optimizer

Name: Claude Prompt Optimizer
Author: timmye

bytimmye

Productivity & Workflow

Refines and optimizes system prompts for Claude Code agents using research-backed prompt engineering patterns.

About

The Claude Prompt Optimizer provides a structured framework for improving the clarity, scope, and behavioral accuracy of LLM system prompts. By applying a human-in-the-loop workflow—ranging from initial triage to pattern-based modification—it ensures every prompt change is grounded in documented best practices. This skill is essential for developers building complex agent workflows who need to eliminate ambiguity, manage edge cases effectively, and maintain token efficiency across single and multi-turn interactions.

Key Features

Triage process to differentiate between simple and complex prompt structures
Anti-pattern detection to prevent common LLM failure modes and hallucinations
0 GitHub stars
Research-backed pattern selection for single and multi-turn prompts
Detailed 'Change Cards' providing evidence-based problem and fix analysis
Human-in-the-loop approval workflow for all system prompt modifications

Use Cases

Optimizing multi-agent orchestration flows and complex handoff logic
Refining lengthy system prompts to improve instruction following and reduce token costs
Improving the reliability and accuracy of tool-use agent instructions

About

Key Features

Triage process to differentiate between simple and complex prompt structures
Anti-pattern detection to prevent common LLM failure modes and hallucinations
0 GitHub stars
Research-backed pattern selection for single and multi-turn prompts
Detailed 'Change Cards' providing evidence-based problem and fix analysis
Human-in-the-loop approval workflow for all system prompt modifications

Use Cases

Optimizing multi-agent orchestration flows and complex handoff logic
Refining lengthy system prompts to improve instruction following and reduce token costs
Improving the reliability and accuracy of tool-use agent instructions