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prompt-optimization

Analyzes and improves existing prompts through a five-step audit and correction workflow.

1|Updated Jul 29, 2026
Search Tags:#prompt engineering#optimization#chain-of-thought#llm#guardrails#few-shot#prompt audit
One-click install
npx skills add https://github.com/fusengine/kimi-code --skill prompt-optimization-fusengine
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-optimization
Source: https://github.com/fusengine/kimi-code/tree/main/plugins/prompt-engineer/skills/prompt-optimization
Command: npx skills add https://github.com/fusengine/kimi-code --skill prompt-optimization-fusengine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Existing prompts often suffer from vague instructions, missing context, undefined output formats, or weak guardrails, leading to inconsistent AI behavior. This Skill audits a prompt against a structured checklist and produces a corrected version with a documented before/after report. ## Core Features & Use Cases - Four-Dimension Audit: Scores prompts on clarity, structure, completeness, and guardrails using a fixed checklist. - Guided Fix Patterns: Applies proven before/after corrections for vague instructions, missing context, undefined formats, absent error handling, and weak emphasis. - Strengthening Techniques: Adds Chain-of-Thought steps, few-shot examples, or reinforced guardrails where justified. - Use Case: You have a production prompt that returns inconsistent outputs. Run the optimization workflow to diagnose issues, apply targeted fixes, and receive a scorecard comparing before/after quality across all four dimensions. ## Quick Start Audit this prompt for clarity, structure, completeness, and guardrails, then produce an optimized version with a before/after scorecard.

Frequently Asked Questions about prompt-optimization

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

FAQPage Schema
How do I improve a vague or ambiguous prompt?▼

Run the five-step optimization workflow: analyze the current prompt, identify issues against the clarity and completeness checklist, apply corrections, validate the improvement, and document changes. Common fixes include adding word counts, output formats, and explicit error-handling branches.

What criteria should a prompt audit checklist cover?▼

A prompt audit should cover four dimensions: clarity (unambiguous instructions, precise vocabulary), structure (delimited sections, logical order), completeness (output format, error cases, examples), and guardrails (explicit limits, forbidden behaviors, security).

How do I add Chain-of-Thought or few-shot examples to a prompt?▼

Add a step-by-step reasoning block instructing the model to identify the request, available information, best approach, and pitfalls before answering. For few-shot, include one good input/output example and one bad example with an explanation of why it fails.

Can prompt optimization change the original intent of my prompt?▼

No. The workflow explicitly forbids changing the original meaning, adding unrequested features, removing existing guardrails, or lengthening the prompt without justification. Every change must be documented with a reason in the scorecard.

How do I measure whether a rewritten prompt is actually better?▼

Use the scorecard template to rate clarity, structure, completeness, and guardrails from 0-10 before and after optimization, totaling 40 points. Then run recommended tests covering the standard case and at least two edge cases.

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