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prompt-improver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

3,269

周安装

139

GitHub Stars

公开资料未说明

下载量

1,145
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-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

简介

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。

SKILL.md

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

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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。

来源信息

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