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moai-foundation-quality摩艾基础质量

Agent Skill

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。

总安装

1,738

周安装

71

GitHub Stars

964

下载量

562
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: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

简介

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。

SKILL.md

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

适合场景

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04

需要参考平台分布和安装热度时

能力概览

能力 1

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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

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

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安装前确认

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