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Agent Skill · 研究检索

prompt-improvement(及时改进)

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。

prompt-improvement 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 57,503。

总安装 432周安装 18GitHub Stars 61下载 144核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/prompt-improvement

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/melodic-software/claude-code-plugins --skill p…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

prompt-improvement 怎么安装?

在终端执行 npx skills add https://github.com/melodic-software/claude-code-plugins --skill prompt-impr…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

prompt-improvement 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

prompt-improvement 需要什么权限?

权限级别是只读。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。

prompt-improvement 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

prompt-improvement 用的人多吗?

公开数据是累计安装 432 次,近一周安装 18 次,下载 144 次,GitHub 61 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

prompt-improvement 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
prompt-improvement

及时改进 · 本页

43261只读MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

平台分布

Antigravity

32.58%
按下载量换算47

trae

22.03%
按下载量换算32

windsurf

17.21%
按下载量换算25

Claude Code

12.73%
按下载量换算18

Codex

7.37%
按下载量换算11

Gemini CLI

3.42%
按下载量换算5

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

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。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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