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⇱ AI Programming with Standards-Driven Development | Glama

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AI Programming with Standards-Driven Development

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  • Why this server?

    Directly addresses 'specification-driven' workflow by transforming ideas into implementation through structured stages like requirement gathering (EARS format) and detailed task planning, aligning perfectly with '规范驱动'.

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    Enables AI-guided spec-driven development workflow that transforms ideas into implementation through structured stages: goal collection, requirements gathering in EARS format, technical design documentation, task planning, and systematic code execution.
    Last updated
    9
    8
    21
    MIT

  • Why this server?

    Provides AI-powered code reviews and static analysis using tools like ESLint and Semgrep, ensuring adherence to coding norms, quality, and security standards ('规范驱动').

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    Enables comprehensive GitHub PR reviews through Cursor's AI by fetching PR diffs, running static analysis tools (ESLint, Prettier, TypeScript, Semgrep), executing tests, and generating detailed code review reports with inline comments.
    Last updated
    9
    2,825
    1

  • Why this server?

    Enables proactive security scanning based on standards like OWASP ASVS, providing necessary guidance for norm-driven programming focusing on security compliance.

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    Provides real-time OWASP ASVS security guidance and vulnerability scanning for AI coding agents. Enables proactive security during code generation by checking security requirements, scanning code for vulnerabilities, and suggesting secure code fixes.
    Last updated
    3
    MIT

  • Why this server?

    Implements a structured specification-driven development workflow with sequential specification creation (requirements, design, tasks), emphasizing process and documentation standards.

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    Provides structured spec-driven development workflow tools for AI-assisted software development with sequential spec creation (Requirements → Design → Tasks). Features a real-time web dashboard for monitoring project progress and managing development workflows.
    Last updated
    5
    889
    4,231
    GPL 3.0

  • Why this server?

    Enables the generation of standardized code, enforcement of architectural patterns, and programmatic validation of outputs, directly supporting '规范驱动' development.

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    Enables AI coding agents to generate standardized code using scaffolding templates, enforce architectural patterns, and validate outputs programmatically. Supports creating projects from boilerplates and adding features to existing codebases while maintaining team conventions.
    Last updated
    159
    AGPL 3.0

  • Why this server?

    Offers a comprehensive suite of quality tools, including linting, security scanning, and testing frameworks, essential for ensuring code meets specific norms and quality standards.

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    Provides comprehensive code quality tools including linting, security scanning, TypeScript checking, and testing through a single MCP server. Integrates multiple quality analysis tools like Biome, ESLint, and Playwright for streamlined development workflows.
    Last updated

  • Why this server?

    Focuses on complex code analysis, providing assessments of code quality, security vulnerabilities, and adherence to best practices ('规范').

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    Enables comprehensive code analysis including quality assessment, security vulnerability detection, refactoring suggestions, complexity calculations, and automatic documentation generation for multiple programming languages.
    Last updated
    5
    4
    MIT

  • Why this server?

    A workflow tool that generates structured specifications and validates documents through a systematic workflow (Requirements → Design → Tasks → Code), fitting the '规范驱动' theme.

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    A Spec-Driven Development toolkit that transforms LLMs into development agents by providing expert-crafted prompts for generating structured specifications and validating documents across the Requirements → Design → Tasks → Code workflow.
    Last updated
    1
    MIT

  • Why this server?

    Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, strongly matching the need for rule-based processes.

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    Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.
    Last updated
    20
    36
    12
    MIT