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Agent Skill · 开发

generate-custom-instructions-from-codebase(从代码库生成自定义指令)

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。

generate-custom-instructions-from-codebase 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 211。

总安装 197,880周安装 8,461GitHub Stars 31,664下载 69,360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
unknown

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:generate-custom-instructions-from-codebase(从代码库生成自定义指令)
来源仓库:https://github.com/github/awesome-copilot
仓库路径:skills/generate-custom-instructions-from-codebase
安装命令:
npx skills add https://github.com/github/awesome-copilot --skill generate-custom-instructions-from-codebase
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/github/awesome-copilot --skill generate-custom-instructions-from-codebase

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

通过分析项目版本之间的代码差异生成 GitHub Copilot 迁移说明。

比较两个项目状态(分支、提交或标签)以提取转换模式、API 更改和架构转变 创建 .github/copilot-migration-instructions.md 包含强制性规则、经过验证的转换、API 对应以及要避免的过时模式的文件 支持多种迁移类型:框架版本升级、架构重构、技术替换、依赖更新、模式变更 可配置的自动化级别(保守、平衡、激进)和可选的前后代码示例,以指导 Copilot 未来的转型 包括验证检查点、错误报告机制和监控指标,以跟踪迁移的准确性和一致性

来源仓库内路径:skills/generate-custom-instructions-from-codebase

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/github/awesome-copilot --skill generate-custom…;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

    它需要需要联网,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

generate-custom-instructions-from-codebase 怎么安装?

在终端执行 npx skills add https://github.com/github/awesome-copilot --skill generate-custom-instructi…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

generate-custom-instructions-from-codebase 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。

generate-custom-instructions-from-codebase 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

generate-custom-instructions-from-codebase 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

generate-custom-instructions-from-codebase 用的人多吗?

公开数据是累计安装 197,880 次,近一周安装 8,461 次,下载 69,360 次,GitHub 31,664 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。

generate-custom-instructions-from-codebase 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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 31,664 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 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。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
generate-custom-instructions-from-codebase

从代码库生成自定义指令 · 本页

197,88031,664需要联网未明确通过

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

Migration and Code Evolution Instructions Generator

Configuration Variables

${MIGRATION_TYPE="Framework Version|Architecture Refactoring|Technology Migration|Dependencies Update|Pattern Changes"}
<!-- Type of migration or evolution -->

${SOURCE_REFERENCE="branch|commit|tag"}
<!-- Source reference point (before state) -->

${TARGET_REFERENCE="branch|commit|tag"}
<!-- Target reference point (after state) -->

${ANALYSIS_SCOPE="Entire project|Specific folder|Modified files only"}
<!-- Scope of analysis -->

${CHANGE_FOCUS="Breaking Changes|New Conventions|Obsolete Patterns|API Changes|Configuration"}
<!-- Main aspect of changes -->

${AUTOMATION_LEVEL="Conservative|Balanced|Aggressive"}
<!-- Level of automation for Copilot suggestions -->

${GENERATE_EXAMPLES="true|false"}
<!-- Include transformation examples -->

${VALIDATION_REQUIRED="true|false"}
<!-- Require validation before application -->

Generated Prompt

"Analyze code evolution between two project states to generate precise migration instructions for GitHub Copilot. These instructions will guide Copilot to automatically apply the same transformation patterns during future modifications. Follow this methodology:

### Phase 1: Comparative State Analysis

#### Structural Changes Detection
- Compare folder structure between ${SOURCE_REFERENCE} and ${TARGET_REFERENCE}
- Identify moved, renamed, or deleted files
- Analyze changes in configuration files
- Document new dependencies and removed ones

#### Code Transformation Analysis
${MIGRATION_TYPE == "Framework Version" ?
  "- Identify API changes between framework versions
   - Analyze new features being used
   - Document obsolete methods/properties
   - Note syntax or convention changes" : ""}

${MIGRATION_TYPE == "Architecture Refactoring" ?
  "- Analyze architectural pattern changes
   - Identify new abstractions introduced
   - Document responsibility reorganization
   - Note changes in data flows" : ""}

${MIGRATION_TYPE == "Technology Migration" ?
  "- Analyze replacement of one technology with another
   - Identify functional equivalences
   - Document API and syntax changes
   - Note new dependencies and configurations" : ""}

#### Transformation Pattern Extraction
- Identify repetitive transformations applied
- Analyze conversion rules from old to new format
- Document exceptions and special cases
- Create before/after correspondence matrix

### Phase 2: Migration Instructions Generation

Create a `.github/copilot-migration-instructions.md` file with this structure:

\`\`\`markdown
# GitHub Copilot Migration Instructions

## Migration Context
- **Type**: ${MIGRATION_TYPE}
- **From**: ${SOURCE_REFERENCE}
- **To**: ${TARGET_REFERENCE}
- **Date**: [GENERATION_DATE]
- **Scope**: ${ANALYSIS_SCOPE}

## Automatic Transformation Rules

### 1. Mandatory Transformations
${AUTOMATION_LEVEL != "Conservative" ?
  "[AUTOMATIC_TRANSFORMATION_RULES]
   - **Old Pattern**: [OLD_CODE]
   - **New Pattern**: [NEW_CODE]
   - **Trigger**: When to detect this pattern
   - **Action**: Transformation to apply automatically" : ""}

### 2. Transformations with Validation
${VALIDATION_REQUIRED == "true" ?
  "[TRANSFORMATIONS_WITH_VALIDATION]
   - **Detected Pattern**: [DESCRIPTION]
   - **Suggested Transformation**: [NEW_APPROACH]
   - **Required Validation**: [VALIDATION_CRITERIA]
   - **Alternatives**: [ALTERNATIVE_OPTIONS]" : ""}

### 3. API Correspondences
${CHANGE_FOCUS == "API Changes" || MIGRATION_TYPE == "Framework Version" ?
  "[API_CORRESPONDENCE_TABLE]
   | Old API   | New API   | Notes     | Example        |
   | --------- | --------- | --------- | -------------- |
   | [OLD_API] | [NEW_API] | [CHANGES] | [CODE_EXAMPLE] | " : ""} |

### 4. New Patterns to Adopt
[DETECTED_EMERGING_PATTERNS]
- **Pattern**: [PATTERN_NAME]
- **Usage**: [WHEN_TO_USE]
- **Implementation**: [HOW_TO_IMPLEMENT]
- **Benefits**: [ADVANTAGES]

### 5. Obsolete Patterns to Avoid
[DETECTED_OBSOLETE_PATTERNS]
- **Obsolete Pattern**: [OLD_PATTERN]
- **Why Avoid**: [REASONS]
- **Alternative**: [NEW_PATTERN]
- **Migration**: [CONVERSION_STEPS]

## File Type Specific Instructions

${GENERATE_EXAMPLES == "true" ?
  "### Configuration Files
   [CONFIG_TRANSFORMATION_EXAMPLES]

   ### Main Source Files
   [SOURCE_TRANSFORMATION_EXAMPLES]

   ### Test Files
   [TEST_TRANSFORMATION_EXAMPLES]" : ""}

## Validation and Security

### Automatic Control Points
- Verifications to perform after each transformation
- Tests to run to validate changes
- Performance metrics to monitor
- Compatibility checks to perform

### Manual Escalation
Situations requiring human intervention:
- [COMPLEX_CASES_LIST]
- [ARCHITECTURAL_DECISIONS]
- [BUSINESS_IMPACTS]

## Migration Monitoring

### Tracking Metrics
- Percentage of code automatically migrated
- Number of manual validations required
- Error rate of automatic transformations
- Average migration time per file

### Error Reporting
How to report incorrect transformations to Copilot:
- Feedback patterns to improve rules
- Exceptions to document
- Adjustments to make to instructions

\`\`\`

### Phase 3: Contextual Examples Generation

${GENERATE_EXAMPLES == "true" ?
  "#### Transformation Examples
   For each identified pattern, generate:

   \`\`\`
   // BEFORE (${SOURCE_REFERENCE})
   [OLD_CODE_EXAMPLE]

   // AFTER (${TARGET_REFERENCE})
   [NEW_CODE_EXAMPLE]

   // COPILOT INSTRUCTIONS
   When you see this pattern [TRIGGER], transform it to [NEW_PATTERN] following these steps: [STEPS]
   \`\`\`" : ""}

### Phase 4: Validation and Optimization

#### Instructions Testing
- Apply instructions on test code
- Verify transformation consistency
- Adjust rules based on results
- Document exceptions and edge cases

#### Iterative Optimization
${AUTOMATION_LEVEL == "Aggressive" ?
  "- Refine rules to maximize automation
   - Reduce false positives in detection
   - Improve transformation accuracy
   - Document lessons learned" : ""}

### Final Result

Migration instructions that enable GitHub Copilot to:
1. **Automatically apply** the same transformations during future modifications
2. **Maintain consistency** with newly adopted conventions
3. **Avoid obsolete patterns** by automatically proposing alternatives
4. **Accelerate future migrations** by capitalizing on acquired experience
5. **Reduce errors** by automating repetitive transformations

These instructions transform Copilot into an intelligent migration assistant, capable of reproducing your technology evolution decisions consistently and reliably.
"

Typical Use Cases

Framework Version Migration

Perfect for documenting the transition from Angular 14 to Angular 17, React Class Components to Hooks, or.NET Framework to.NET Core. Automatically identifies breaking changes and generates corresponding transformation rules.

Technology Stack Evolution

Essential when replacing a technology entirely: jQuery to React, REST to GraphQL, SQL to NoSQL. Creates a comprehensive migration guide with pattern mappings.

Architecture Refactoring

Ideal for large refactorings like Monolith to Microservices, MVC to Clean Architecture, or Component to Composable architecture. Preserves architectural knowledge for future similar transformations.

Design Pattern Modernization

Useful for adopting new patterns: Repository Pattern, Dependency Injection, Observer to Reactive Programming. Documents the rationale and implementation differences.

Unique Benefits

🧠 Artificial Intelligence Enhancement

Unlike traditional migration documentation, these instructions "train" GitHub Copilot to reproduce your technology evolution decisions automatically during future code modifications.

🔄 Knowledge Capitalization

Transforms specific project experience into reusable rules, avoiding the loss of migration expertise and accelerating future similar transformations.

🎯 Context-Aware Precision

Instead of generic advice, generates instructions tailored to your specific codebase, with real before/after examples from your project evolution.

⚡ Automated Consistency

Ensures that new code additions automatically follow the new conventions, preventing architectural regression and maintaining code evolution coherence.

平台分布

Codex

39.66%
按下载量换算27,508

Claude

28.45%
按下载量换算19,733

Cursor

18.25%
按下载量换算12,658

Gemini CLI

10.22%
按下载量换算7,089

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

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