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Llm Application Dev Prompt Optimize

ASecurity

You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati

274 stars
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Added 5/30/2026
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A100/100

Pro scans all 2 files and shows the line behind each finding

Scanned 5/30/2026

$npx -y skills add lingxling/awesome-skills-cn --skill llm-application-dev-prompt-optimize --agent claude-code

Installs into .claude/skills of the current project.

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Files
SKILL.md
---
name: llm-application-dev-prompt-optimize
description: "You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimizati"
risk: unknown
source: community
date_added: "2026-02-27"
---

# Prompt Optimization

You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimization.

## Use this skill when

- Working on prompt optimization tasks or workflows
- Needing guidance, best practices, or checklists for prompt optimization

## Do not use this skill when

- The task is unrelated to prompt optimization
- You need a different domain or tool outside this scope

## Context

Transform basic instructions into production-ready prompts. Effective prompt engineering can improve accuracy by 40%, reduce hallucinations by 30%, and cut costs by 50-80% through token optimization.

## Requirements

$ARGUMENTS

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Resources

- `resources/implementation-playbook.md` for detailed patterns and examples.

## Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Attribution

lingxlinglingxling
View sourceSee grades on GitHubMore from lingxling →
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