Prompt Optimizer
FlaggedPrompt engineering agent that analyzes, refines, and tests prompts for better LLM output quality, consistency, and token efficiency.
$ Copy the SKILL.md file to your project's .claude/skills/ directory About This Skill
Prompt Optimizer takes your existing prompts and applies proven prompt engineering techniques to improve output quality, consistency, and token efficiency. It understands the nuances of different LLM architectures and tailors optimization strategies accordingly.
How It Works
- Prompt analysis — Evaluates your current prompt for common issues: ambiguity, missing constraints, implicit assumptions, and token waste
- Technique application — Applies relevant techniques such as chain-of-thought, few-shot examples, role framing, structured output formats, and negative constraints
- Token optimization — Identifies redundant instructions and verbose phrasing that can be condensed without losing meaning
- Variant generation — Creates 2-3 optimized variants with different approaches (concise vs. detailed, structured vs. conversational)
- Evaluation criteria — Provides a testing rubric with sample inputs so you can objectively compare variant performance
Best For
- AI engineers building production prompt pipelines
- Developers integrating LLMs into applications for the first time
- Teams standardizing prompts across multiple AI features
- Anyone frustrated by inconsistent LLM outputs
Use Cases
- Improving prompts for code generation tasks
- Optimizing system prompts for production AI features
- Reducing token usage while maintaining output quality
- Creating prompt templates for repeatable workflows
- A/B testing prompt variants for quality comparison
Pros & Cons
Pros
- +Applies proven prompt engineering techniques systematically
- +Generates multiple variants for A/B testing
- +Reduces token usage without sacrificing quality
- +Includes testing rubrics for objective evaluation
Cons
- -Optimal prompts vary by LLM model — may need re-optimization when switching models
- -Cannot fully test prompts without actual LLM API calls
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