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Agent Skill · 研究检索

prompt-improver(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-improver 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,权限为接触敏感数据;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 28,131。

总安装 3,269周安装 139GitHub Stars 公开资料未说明下载 1,145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-improver(及时改进)
来源仓库:https://github.com/dotneet/claude-code-marketplace
仓库路径:skills/prompt-improver
安装命令:
npx skills add https://github.com/dotneet/claude-code-marketplace --skill prompt-improver
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/dotneet/claude-code-marketplace --skill prompt-improver

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。

聚焦于提升提示词清晰度与执行成功率。支持多轮反馈循环和渐进式精炼。prompt-improver 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适用于需要高精度输出的专业领域任务。需配合具体任务类型进行针对性调优。不建议直接应用于关键决策类提示。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/prompt-improver

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/dotneet/claude-code-marketplace --skill prompt…;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

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

prompt-improver 怎么安装?

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

prompt-improver 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-improver 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

prompt-improver 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-improver 用的人多吗?

公开数据是累计安装 3,269 次,近一周安装 139 次,下载 1,145 次。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-improver 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

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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-improver

及时改进 · 本页

3,269—接触敏感数据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 Improver

Overview

A skill that analyzes and improves prompts based on general LLM/agent best practices. It focuses on verifiability, clear scope, explicit constraints, and context economy so the agent can execute with minimal back-and-forth.

If you are running in Claude Code, also read references/claude.md and apply the additional Claude-specific techniques. If you are running in Codex CLI, also read references/codex.md and apply the additional Codex-specific techniques.

When the input is a document that instructs an agent (e.g., plan files, AGENTS.md, system instruction docs), treat the document as the improvement target; identify issues and propose concrete improvements, and include a revised draft when helpful.

Workflow

Step 0: Classify Task and Complexity

Classify the task and decide whether an explicit exploration/planning phase should be recommended:

  • Task type: bugfix, feature, refactor, research, UI/visual, docs, ops
  • Complexity: single-file/small change vs multi-file/uncertain impact
  • Risk: data safety, security, compatibility, performance
  • Input type: prompt vs agent-instruction document (plan files, AGENTS.md, system instruction docs)

If the task is complex or ambiguous, the improved prompt should explicitly request an exploration/planning phase before implementation.

Step 1: Analyze the Prompt

Analyze the user-provided prompt from the following perspectives:

  1. Verifiability: Does it include means for Claude to verify its own work?
  2. Specificity: Are files, scenarios, and constraints clearly specified?
  3. Context: Is necessary background information provided?
  4. Scope: Is the task scope appropriately defined?
  5. Expected Outcome: Are success criteria clear?
  6. Constraints: Are language/runtime versions, dependencies, security, or compatibility requirements specified?
  7. Context Economy: Is the prompt concise and focused, without unnecessary information?
  8. Execution Preference: Is it clear whether the model should implement, propose, or just analyze?

Step 2: Identify Issues

Check for the following anti-patterns:

Anti-patternDescription
Vague instructionsLacks specificity like "make it better" or "improve it"
No verification methodMissing tests, screenshots, or expected output
No verification commandsMissing how to run tests or check outputs
Overly broad scopeAsking for too many things at once
Insufficient contextMissing file paths, error messages, or references to existing patterns
Symptom-only descriptionNot requesting investigation of root cause
Missing constraintsNo environment, dependency, or compatibility requirements
No exploration/planning cueComplex tasks not asking for exploration/planning first
Context bloatUnnecessary details that increase token usage
Ambiguous deliverableUnclear whether to plan, implement, or only analyze
Unclear response formatMissing brevity/structure expectations

Step 3: Create Improved Prompt

Apply best practices to create an improved version:

Add Verifiability
Before: "implement a function that validates email addresses"
After: "write a validateEmail function. test cases: user@example.com is true, invalid is false, user@.com is false. run the tests after implementing"
Add Specific Context
Before: "fix the login bug"
After: "users report that login fails after session timeout. check the auth flow in src/auth/, especially token refresh. write a failing test that reproduces the issue, then fix it"
Add Reference to Existing Patterns
Before: "add a calendar widget"
After: "look at how existing widgets are implemented on the home page to understand the patterns. HotDogWidget.php is a good example. follow the pattern to implement a new calendar widget with month selection and pagination"
Add Context Economy
Before: "here is a long unrelated history ... fix the dropdown"
After: "fix the dropdown in src/ui/Dropdown.tsx. issue: keyboard navigation skips items. keep the prompt focused; omit unrelated history"
Add Rich Context Inputs
Before: "build fails"
After: "build fails with this error: [paste error]. run the smallest relevant test command. if needed, read the build script and package config"
Add Explicit Exploration/Planning When Needed
Before: "refactor auth to support OAuth"
After: "first explore src/auth and summarize current flow, then propose a plan. after approval, implement with tests"

When information is missing, include explicit questions inside the improved prompt and do not assume defaults.

Example: Bugfix
Before: "search is broken"
After: "users report search returns empty results for queries with hyphens. reproduce in src/search/. paste the error log if any. write a failing test for 'foo-bar' returning results, fix the root cause, run: pnpm test --filter search"
Example: UI/Visual
Before: "make the dashboard look better"
After: "implement the attached screenshot for the dashboard header in src/ui/DashboardHeader.tsx. match spacing and typography. take a new screenshot and list any differences. run: pnpm lint"
Example: Refactor
Before: "clean up the auth code"
After: "inspect src/auth and list duplication hotspots. propose a refactor plan scoped to one module. after approval, remove duplication without changing behavior. add a targeted test if coverage is missing. run the smallest relevant test command"
Example: Research
Before: "why is this API slow?"
After: "explore request flow around src/api/. summarize likely bottlenecks with evidence (logs, timings). propose 2-3 hypotheses and what data is needed to confirm. do not implement yet. if needed, ask for access to profiling output"
Example: Ops
Before: "deployment failed"
After: "deploy fails with error: [paste log]. identify the failing step in scripts/deploy.sh and related CI config. suggest a fix and a rollback plan. run: ./scripts/deploy.sh --dry-run (if available)"
Example: Docs
Before: "update the README"
After: "update README.md to include install + dev steps based on existing scripts. keep it concise. confirm commands exist and match actual scripts"

Step 4: Output Format

Output in the following format:

## Prompt Analysis

### Original Prompt
[User-provided prompt]

### Issues
- [Issue 1 and its impact]
- [Issue 2 and its impact]
...

### Missing Information (Questions to Ask)
The following questions should be answered to make the prompt more effective:
- [Question 1] (Why it is needed)
- [Question 2] (Why it is needed)
...

### Verification Plan
- Commands to run (tests, build, lint, screenshots)
- Expected results or acceptance criteria

### Exploration/Planning Recommendation
- Recommend exploration/planning first? [Yes/No] and why

### Execution Preference
- Implement now / propose plan only / analyze only
- Any constraints on response length or format

### Improved Prompt
[The improved prompt]

### Document Improvement Suggestions (if applicable)
- [Issue and concrete improvement for the instruction document]
- [Issue and concrete improvement for the instruction document]

### Revised Document (optional)
[A revised draft of the instruction document when helpful]

### Improved Prompt Template (optional)
Use this as a fill-in template if the user wants a reusable prompt format:

[Task]

  • Goal:
  • Target files/paths (@...):
  • Constraints (runtime/version/deps/security/compat):
  • Context (symptom, logs, repro, links):

[Verification]

  • Commands:
  • Expected results:
  • UI checks (screenshots/visual diffs):

[Exploration/Planning]

  • Do exploration/planning first? (Yes/No) + reason:

[Execution Preference]

  • Implement now / plan only / analyze only
  • Output format (concise report, patch summary, checklist, etc.)
### Short Prompt Template (optional)
Use this when the user wants the shortest effective prompt:

Goal:... Targets: path1/path2 Context: symptom + repro + logs Constraints: runtime/deps/compat Verify: command + expected result Explore/Plan: yes/no (why) Execute: implement / plan only / analyze only Output: concise format

### Improvement Points
1. [Explanation of improvement 1]
2. [Explanation of improvement 2]
...

### Additional Recommendations (optional)
- [Whether to do exploration/planning first]
- [Whether to parallelize or delegate investigation]
- [Whether preliminary research is needed]
- [Ways to reduce context usage]

Reference: Best Practices Checklist

Best practices to reference when improving prompts:

Provide Verification Methods

  • Include test cases
  • Specify expected output
  • For UI changes, request screenshot comparison
  • Add "run the tests" or "verify the build succeeds"
  • Include explicit commands to run

Explore → Plan → Implement Order

  • Use an exploration/planning phase first for complex tasks
  • Create a plan before implementation
  • Small fixes don't need a formal plan
  • When uncertain, ask clarifying questions before coding

Include Specific Context

  • Paste error messages
  • Specify existing patterns
  • Clarify edge cases
  • State environment constraints (runtime, language versions, dependencies)

Use Rich Inputs

  • Paste logs and stack traces
  • Provide URLs for docs or API references
  • Attach or paste large text outputs when needed

Manage Context Window

  • Keep prompts concise and focused
  • Remove unrelated history or speculation
  • Ask for missing info instead of guessing

Leverage Parallelism/Delegation

  • For research tasks: request parallel investigation or summaries
  • For code review: request an independent review pass
  • Keeps the main task focused

Patterns to Avoid

  • Vague instructions like "make it better" or "improve it"
  • Implementation requests without verification methods
  • Multiple unrelated tasks at once
  • Requesting changes to files not yet read
  • Large context dumps with no clear signal

平台分布

Claude Code

27.89%
按下载量换算319

Antigravity

23.76%
按下载量换算272

Gemini CLI

16.33%
按下载量换算187

OpenCode

12.95%
按下载量换算148

windsurf

6.82%
按下载量换算78

trae

3.3%
按下载量换算38

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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