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prompt-improvement及时改进

Agent Skill

用于辅助提示词、系统指令、Agent 行为约束和工作流模板的整理。它适合让 Agent 规范任务边界、统一输出格式、拆分操作步骤或优化提示词可复用性。使用时需要保留真实业务约束,不要把示例当硬规则;涉及自动执行、外部工具或高风险操作时,应在提示词中明确确认步骤、权限边界和失败处理方式。

总安装

432

周安装

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GitHub Stars

61

下载量

144
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:prompt-improvement(及时改进)
来源仓库:https://github.com/melodic-software/claude-code-plugins
仓库路径:skills/prompt-improvement
安装命令:
npx skills add https://github.com/melodic-software/claude-code-plugins --skill prompt-improvement
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/melodic-software/claude-code-plugins --skill prompt-improvement

简介

自动分析并优化现有提示词的表达效率。

  • 识别模糊表述、冗余结构或逻辑断层问题。
  • 提供改写建议和结构化重组方案。prompt-improvement 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 安装后可通过 API 或 CLI 方式接入工作流。
  • 优化效果依赖原始提示的质量与上下文完整性。

SKILL.md

Prompt Improvement

MANDATORY: Query Official Documentation First

STOP - Before improving ANY prompt, you MUST invoke the docs-management skill. This is NOT optional guidance - it is a required execution step.

Required: Query Documentation via docs-management Skill

Invoke the claude-ecosystem:docs-management skill BEFORE making any improvements:

Search for relevant documentation using natural language:

  • Primary query: "prompt engineering chain of thought XML tags"
  • Read the top results returned by the skill

Why Documentation Query is Required

  • Training data is stale - Your knowledge may be outdated
  • Official docs are canonical - Anthropic's current best practices
  • Prevents hallucination - Ground improvements in real documentation
  • Ensures accuracy - Latest Claude 4.x guidance

Recommended Query Topics

Query at least ONE of these topics based on your improvement needs:

  • Chain of thought: "chain of thought thinking tags reasoning"
  • XML structure: "XML tags structure prompts formatting"
  • Examples/multishot: "multishot prompting examples few-shot"
  • Claude 4.x best practices: "Claude 4 prompting best practices"

Verification Checkpoint

Before improving a prompt, verify:

  • Did I invoke the docs-management skill? (not just read about it)
  • Did I receive documentation from docs-management?
  • Did I READ at least one official doc returned by the skill?
  • Are my improvements based on what I read from official docs?

If ANY checkbox is unchecked, STOP and invoke the docs-management skill first.

Overview

This meta-skill replicates Anthropic's Console/Workbench prompt improver functionality within Claude Code. It provides workflows, patterns, and keyword registries to transform basic prompts into high-performance structured templates.

What this skill provides:

  • The 4-step improvement workflow (quick reference)
  • Keyword registry for docs-management queries
  • Decision trees for improvement strategies
  • Pattern libraries (XML tags, chain of thought, prefills)
  • Before/after transformation examples
  • Iterative refinement guidance
  • Trade-off warnings (latency/cost implications)

What this skill does NOT provide:

  • Duplicated official documentation (use docs-management skill)
  • Hardcoded best practices (query docs-management for current guidance)

When to Use This Skill

Use this skill when:

  • Improving an existing prompt for better accuracy or structure
  • Adding chain of thought reasoning to complex tasks
  • Structuring prompts with XML tags for clarity and parseability
  • Enhancing examples with reasoning steps
  • Optimizing prompts for Claude 4.x models with explicit instructions
  • Iterating on prompt quality with feedback loops
  • Generating test cases when examples are lacking

Quick Decision Tree

What do you want to do?

  1. Improve a prompt from scratch -> See The 4-Step Improvement Workflow
  2. Add XML structure -> Query docs-management: "Find documentation about XML tags for structuring prompts"
  3. Add chain of thought -> Query docs-management: "Find documentation about chain of thought prompting"
  4. Add/improve examples -> See references/patterns/example-enrichment-patterns.md
  5. Iterate with feedback -> See references/workflows/iterative-refinement.md
  6. Generate test cases -> See references/workflows/test-case-generation.md
  7. Understand trade-offs -> See references/troubleshooting/tradeoffs-guide.md

The 4-Step Improvement Workflow

The prompt improver enhances prompts through a structured 4-step process:

Step 1: Example Identification

  • Locate and extract any existing examples from the prompt
  • Note the format and structure of current examples
  • Identify if examples are missing (if so, consider generating them)

Step 2: Initial Draft

  • Create a structured template with clear sections
  • Add XML tags to separate components:

- <instructions> - Task definition and behavioral guidelines - <context> - Background information and relevant details - <examples> - Demonstration cases - <formatting> - Desired output format specification

  • Query docs-management: "Find documentation about XML tags for structuring prompts"

Step 3: Chain of Thought Refinement

  • Add step-by-step reasoning instructions
  • Include thinking tags: <thinking>, <analysis>, <answer>
  • Guide Claude through the problem-solving process
  • Query docs-management: "Find documentation about chain of thought prompting"

Step 4: Example Enhancement

  • Update examples to demonstrate the new reasoning process
  • Add <thinking> steps within examples showing intermediate reasoning
  • Ensure examples match the output format specification
  • This teaches Claude HOW to reason, not just WHAT to output

Detailed workflow: references/workflows/improvement-workflow.md

What You Get After Improvement

An improved prompt typically includes:

  • Detailed chain-of-thought instructions guiding Claude's reasoning
  • Clear XML tag organization separating components
  • Standardized example formatting with step-by-step reasoning
  • Strategic prefills guiding initial responses

Typical Improved Prompt Structure

<instructions>
Your task definition and behavioral guidelines
</instructions>

<context>
Background information and relevant details
</context>

<examples>
  <example>
    <input>Sample input</input>
    <thinking>Step-by-step reasoning</thinking>
    <output>Expected output</output>
  </example>
</examples>

<formatting>
Specify desired output format and structure
</formatting>

Keyword Registry for docs-management

Use these keywords to query the docs-management skill for official documentation:

Core Techniques

TopicQuery Keywords
Prompt Improverprompt improver, prompt improvement, optimize prompts
Chain of Thoughtchain of thought, CoT, thinking, step-by-step, reasoning
XML TagsXML tags, structure prompts, tagging, XML structure
Examplesmultishot, few-shot, example formatting, multishot prompting
System Promptssystem prompt, role, persona, Claude role
Clarityclear, direct, explicit, specific instructions, be clear and direct
Claude 4.xClaude 4, Claude 4.5, Sonnet 4.5, Opus 4.5, best practices

Advanced Topics

TopicQuery Keywords
Prefillingprefill, response prefill, assistant prefill, output format
Long Contextlong context, long context tips, document placement
Extended Thinkingextended thinking, thinking budget, deep reasoning
Prompt Chainingchain prompts, prompt chaining, multi-step prompts

Example Queries

Find documentation about chain of thought prompting and thinking tags
Find documentation about XML tags for structuring prompts
Find documentation about multishot prompting and example formatting
Find documentation about Claude 4 and Claude 4.5 prompting best practices
Find documentation about prefilling Claude's response for output control

Performance Expectations

Empirical Guidance: The metrics below are illustrative examples from Anthropic's testing at time of publication. Actual results vary by task, domain, and model version. These are NOT guarantees - use them as rough benchmarks for improvement potential.

Based on Anthropic's testing, prompt improvement typically yields:

  • 30% accuracy increase on multi-label classification tasks
  • 100% word count adherence on summarization tasks
  • ~40% reduction in prompt iteration cycles

Important: Improved prompts produce longer, more thorough responses. Consider trade-offs for latency-sensitive or cost-sensitive applications.

References Guide

Load these files based on your specific needs:

Workflows

Examples

Patterns

Troubleshooting

Metadata

Related Components

Companion Agent

  • prompt-improver - Subagent that executes the 4-step improvement workflow

- Auto-loads this skill for keyword registries and workflow guidance - Use when you need automated prompt improvement execution - Invoked by the /improve-prompt command

Slash Command

  • /improve-prompt - User-facing command for prompt improvement

- Input modes: direct text, file path, context, iterate - Supports --feedback for iterative refinement - Supports --generate-examples for prompts lacking examples

Related Skills

  • claude-ecosystem:docs-management - Official documentation access (all documentation queries delegate here)
  • claude-ecosystem:current-date - Get current UTC date for version history
  • claude-ecosystem:skill-development - Creating and validating skills

Version History

  • v1.0.0 (2025-12-03): Initial release - Meta-skill for prompt improvement with 4-step workflow, delegation to docs-management, comprehensive pattern library

Last Updated

Date: 2025-12-03 Model: claude-opus-4-5-20251101

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需要参考平台分布和安装热度时

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能力 5

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只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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