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Agent Skill · 运维和基础设施

moai-foundation-quality(摩艾基础质量)

moai-foundation-quality 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

moai-foundation-quality 是运维和基础设施类 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;许可证为 MIT,第三方扫描有提醒项,按安装量在运维和基础设施类的 2,449 个 Skill 里排第 760。

总安装 1,738周安装 71GitHub Stars 964下载 562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:moai-foundation-quality(摩艾基础质量)
来源仓库:https://github.com/modu-ai/moai-adk
仓库路径:skills/moai-foundation-quality
安装命令:
npx skills add https://github.com/modu-ai/moai-adk --skill moai-foundation-quality
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/modu-ai/moai-adk --skill moai-foundation-quality

这个 Skill 是做什么的

运维和基础设施类 Skill 处理部署、监控、日志和云资源,多数需要较高的执行权限。

moai-foundation-quality 用于处理 GitHub 仓库、Issue 和 Pull Request 协作信息。适用于围绕代码变更、仓库状态或协作事项进行整理的场景。通过 npx skills add 命令安装,需结合来源仓库和 README 确认具体用法。

安装前建议确认权限范围、维护状态及是否会触发联网或文件读写操作。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/moai-foundation-quality

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/modu-ai/moai-adk --skill moai-foundation-quali…;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

moai-foundation-quality 怎么安装?

在终端执行 npx skills add https://github.com/modu-ai/moai-adk --skill moai-foundation-quality。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

moai-foundation-quality 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。

moai-foundation-quality 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。

moai-foundation-quality 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。

moai-foundation-quality 用的人多吗?

公开数据是累计安装 1,738 次,近一周安装 71 次,下载 562 次,GitHub 964 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。

moai-foundation-quality 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理运维和基础设施类任务、又不想每次都手写提示词的人。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

运维和基础设施类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
brainstorming

头脑风暴 · Codex、Claude

3,148,776173,131操作浏览器MIT通过
copywriting

文案写作 · Codex、Claude

1,996,72425,583需要联网MIT通过
openclaw-secure-linux-cloud

OpenClaw secure linux cloud 命令行 · Codex、Claude

1,853,08840接触敏感数据未明确未通过
xget

xget 命令行 · Codex、Claude

1,799,28040执行命令未明确未通过
requesting-code-review

代码审查请求 · Codex、Claude

1,590,144173,103权限未标注MIT通过
moai-foundation-quality

摩艾基础质量 · 本页

1,738964需要联网MIT有提醒

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

Enterprise Code Quality Orchestrator

Enterprise-grade code quality management system that combines systematic code review, proactive improvement suggestions, and automated best practices enforcement. Provides comprehensive quality assurance through TRUST 5 framework validation with Context7 integration for real-time best practices.

Quick Reference (30 seconds)

Core Capabilities:

  • TRUST 5 Validation: Testable, Readable, Unified, Secured, Trackable quality gates
  • Proactive Analysis: Automated issue detection and improvement suggestions
  • Best Practices Enforcement: Context7-powered real-time standards validation
  • Multi-Language Support: 25+ programming languages with specialized rules
  • Enterprise Integration: CI/CD pipelines, quality metrics, reporting

Key Patterns:

  • Quality Gate Pipeline: Automated validation with configurable thresholds
  • Proactive Scanner: Continuous analysis with improvement recommendations
  • Best Practices Engine: Context7-driven standards enforcement
  • Quality Metrics Dashboard: Comprehensive reporting and trend analysis

When to Use:

  • Code review automation and quality gate enforcement
  • Proactive code quality improvement and technical debt reduction
  • Enterprise coding standards enforcement and compliance validation
  • CI/CD pipeline integration with automated quality checks

Quick Access:

Implementation Guide

Getting Started

Basic Quality Validation: Initialize QualityOrchestrator with trust5_enabled, proactive_analysis, best_practices_enforcement, and context7_integration all set to True. Call analyze_codebase method with path parameter set to source directory, languages list including python, javascript, and typescript, and quality_threshold of 0.85. The method returns comprehensive quality results.

For quality gate validation with TRUST 5, create QualityGate instance and call validate_trust5 with codebase_path, test_coverage_threshold of 0.90, and complexity_threshold of 10.

Proactive Quality Analysis: Initialize ProactiveQualityScanner with context7_client and BestPracticesEngine rule_engine. Call scan_codebase with path and scan_types list including security, performance, maintainability, and testing. Generate recommendations by calling generate_recommendations with issues, priority set to high, and auto_fix enabled.

Core Components

Quality Orchestration Engine

The QualityOrchestrator class provides enterprise quality orchestration with TRUST 5 framework. Initialize with QualityConfig and create instances of TRUST5Validator, ProactiveScanner, BestPracticesEngine, Context7Client, and QualityMetricsCollector.

The analyze_codebase method performs comprehensive analysis in four phases. Phase 1 runs TRUST 5 validation on the codebase with specified thresholds. Phase 2 performs proactive analysis scanning focus areas. Phase 3 checks best practices for specified languages with Context7 docs enabled. Phase 4 collects comprehensive metrics from all analysis results.

The method returns QualityResult containing trust5_validation, proactive_analysis, best_practices, metrics, and overall_score calculated from all results.

Detailed implementations available in modules:

Configuration and Customization

Quality Configuration: Create quality-config.yaml with quality_orchestration section.

Under trust5_framework, set enabled to true with thresholds for overall (0.85), testable (0.90), readable (0.80), unified (0.85), secured (0.90), and trackable (0.80).

Under proactive_analysis, set enabled true, scan_frequency to daily, and focus_areas list including performance, security, maintainability, and technical_debt.

Under auto_fix, set enabled true, severity_threshold to medium, and confirmation_required to true.

Under best_practices, set enabled true, context7_integration true, auto_update_standards true, and compliance_target to 0.85.

Under language_rules, configure python with pep8 style_guide, black formatter, ruff linter, and mypy type_checker. Configure javascript with airbnb style_guide, prettier formatter, and eslint linter. Configure typescript with google style_guide, prettier formatter, and eslint linter.

Under reporting, set enabled true, metrics_retention_days to 90, trend_analysis true, and executive_dashboard true.

Under notifications, enable quality_degradation, security_vulnerabilities, and technical_debt_increase.

Integration Examples: See Integration Patterns for CI/CD Pipeline Integration, GitHub Actions Integration, Quality-as-Service REST API, and Cross-Project Benchmarking.

Advanced Patterns

Custom Quality Rules

Create CustomQualityRule class with name, validator callable, and severity defaulting to medium. The validate async method executes the validator on codebase, wrapping in try-except. On success, return RuleResult with rule_name, passed status, severity, details, and recommendations. On exception, return RuleResult with passed false, severity error, error details, and fix recommendation.

See Best Practices - Custom Rules for complete examples.

Machine Learning Quality Prediction

ML-powered quality issue prediction using code feature extraction and predictive models. See Proactive Analysis - ML Prediction for implementation details.

Real-time Quality Monitoring

Continuous quality monitoring with automated alerting for quality degradation and security vulnerabilities. See Proactive Analysis - Real-time Monitoring for implementation details.

Cross-Project Quality Benchmarking

Compare project quality metrics against similar projects in your industry. See Integration Patterns - Benchmarking for implementation details.

Module Reference

Core Modules

Key Components by Module

TRUST 5 Validation: TRUST5Validator for five-pillar quality validation, TestableValidator for test coverage and quality, SecuredValidator for security and OWASP compliance, and quality gate pipeline integration.

Proactive Analysis: ProactiveQualityScanner for automated issue detection, QualityPredictionEngine for ML-powered predictions, RealTimeQualityMonitor for continuous monitoring, and performance and maintainability analysis.

Best Practices: BestPracticesEngine for standards validation, Context7 integration for latest docs, custom quality rules, and language-specific validators.

Integration Patterns: CI/CD pipeline integration, GitHub Actions workflows, Quality-as-Service REST API, and cross-project benchmarking.

Context7 Library Mappings

Essential library mappings for quality analysis tools and frameworks. See Best Practices - Library Mappings for complete list.

Works Well With

Agents:

  • core-planner - Quality requirements planning
  • workflow-ddd - DDD implementation validation
  • security-expert - Security vulnerability analysis
  • code-backend - Backend code quality
  • code-frontend - Frontend code quality

Skills:

  • moai-foundation-core - TRUST 5 framework reference
  • moai-workflow-ddd - DDD workflow validation
  • moai-security-owasp - Security compliance
  • moai-context7-integration - Context7 best practices
  • moai-performance-optimization - Performance analysis

Commands:

  • /moai:2-run - DDD validation integration
  • /moai:3-sync - Documentation quality checks
  • /moai:9-feedback - Quality improvement feedback

Quick Reference Summary

Core Capabilities: TRUST 5 validation, proactive scanning, Context7-powered best practices, multi-language support, enterprise integration

Key Classes: QualityOrchestrator, TRUST5Validator, ProactiveQualityScanner, BestPracticesEngine, QualityMetricsCollector

Essential Methods: analyze_codebase(), validate_trust5(), scan_for_issues(), validate_best_practices(), generate_quality_report()

Integration Ready: CI/CD pipelines, GitHub Actions, REST APIs, real-time monitoring, cross-project benchmarking

Enterprise Features: Custom rules, ML prediction, real-time monitoring, benchmarking, comprehensive reporting

Quality Standards: OWASP compliance, TRUST 5 framework, Context7 integration, automated improvement recommendations

Common Rationalizations

RationalizationReality
"The linter warnings are false positives"False positives should be suppressed with inline comments. Ignoring them trains the team to ignore real issues.
"Security scanning can wait until before release"Security vulnerabilities compound. Late discovery means expensive rework. Scan continuously.
"Coverage is high enough, the remaining 15% is edge cases"Edge cases are where production bugs live. The uncovered code is the riskiest code.
"Code review is subjective, automation is sufficient"Automation catches syntax and patterns. Reviews catch design flaws, naming confusion, and missing abstractions.
"TRUST 5 is too bureaucratic for a hotfix"Hotfixes without quality gates introduce the next hotfix. TRUST 5 on a hotfix is the minimum, not the maximum.

Chesterton's Fence: Before removing a quality check, understand why it was added. Removing a gate without understanding its history repeats the failure it was designed to prevent.

Shift Left: The earlier a defect is found, the cheaper it is to fix. Quality checks belong in the development loop, not at the end of it.

Red Flags

  • Linter or type-checker warnings suppressed globally instead of per-line
  • OWASP checklist not consulted when handling user input or authentication
  • Coverage report not generated for a commit that adds new functionality
  • TRUST 5 dimension skipped with "not applicable" without justification
  • Quality report generated but no action taken on identified issues

Verification

  • Linter runs clean or remaining warnings have inline suppression comments with reasons
  • OWASP checklist reviewed for security-relevant changes (show checklist references)
  • Coverage report generated and threshold met (show tool output)
  • All five TRUST 5 dimensions assessed (show assessment for each)
  • Quality report issues triaged with resolution plan for each finding
  • No global rule disabling in linter configuration

平台分布

Claude Code

29.92%
按下载量换算168

Antigravity

21.89%
按下载量换算123

Codex

17.38%
按下载量换算98

Cursor

10.92%
按下载量换算61

OpenCode

7.53%
按下载量换算42

Gemini CLI

3.05%
按下载量换算17

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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