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⇱ Software Development Lifecycle Guide and Resources | Glama

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Software Development Lifecycle Guide and Resources

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  • Why this server?

    This server directly supports the early phases of the software development lifecycle by transforming ideas into structured requirements, technical designs, and task planning.

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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.
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    MIT

  • Why this server?

    This server offers a comprehensive suite of tools covering multiple aspects of the software development lifecycle, including code analysis, debugging, refactoring, documentation, testing, and project automation.

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    Provides unified development tools including code analysis, debugging, refactoring, documentation, testing, and project automation through multiple LLM providers (KIMI, GLM, OpenRouter). Features agentic audit capabilities with multi-model consensus for finding issues and generating direct fixes.
    Last updated

  • Why this server?

    This server is designed to manage the entire product development lifecycle, including various phases and sprint tracking, making it highly relevant for overseeing the SDLC.

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    Enables AI agents to track and manage product development projects through structured 7-phase lifecycles with sprint tracking, role-based collaboration, and multi-project support. Provides phase management, progress tracking, and team coordination tools for complete product development workflows.
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  • Why this server?

    Critical for the documentation phase of the SDLC, this server generates comprehensive software documentation such as requirements, design specifications, and test strategies.

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    AI-powered document generation suite that creates comprehensive software documentation including requirements, design specs, test strategies, and deployment guides through LangGraph workflows. Integrates with Cursor IDE via MCP to transform project ideas into professional documentation suites.
    Last updated
    1
    MIT

  • Why this server?

    This server provides integration with CI/CD processes, enabling automated builds, monitoring of build status, and analysis of test failures, which are crucial for the build and testing phases of the SDLC.

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    Enables AI coding assistants to interact with JetBrains TeamCity CI/CD server through natural language commands. Supports triggering builds, monitoring status, analyzing test failures, and managing build configurations directly from development environments.
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    MIT

  • Why this server?

    As a central component for version control and collaboration, this server enables repository management, file operations, issue tracking, and pull request management, supporting the coding and collaboration phases.

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    Enables comprehensive GitHub operations through natural language including file management, repository administration, issue tracking, and advanced code searching.
    Last updated
    47
    6
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    MIT

  • Why this server?

    Essential for the deployment and operations phases, this server provides tools for cluster observability, debugging, and managing Kubernetes resources.

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    Enables real-time Kubernetes cluster observability and debugging through standardized MCP interface. Provides access to pods, services, nodes, events, and includes built-in tools for cluster health analysis and issue identification.
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  • Why this server?

    Focusing on the coding phase, this server analyzes frontend project code to provide insights into its structure and relationships, which helps in maintaining code quality and understanding.

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    Analyzes frontend project code (React, Vue, Angular) and converts it into AI-understandable flow diagrams and object structures. Provides tools for code analysis, variable/function/component inspection, and project structure insights.
    Last updated
    4
    MIT

  • Why this server?

    This server is crucial for the testing phase, offering capabilities to analyze testing setups and evaluate code coverage, ensuring the quality of the software.

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    Enables comprehensive analysis of JavaScript/TypeScript project testing setups by detecting frameworks like Jest, Vitest, and Cypress, analyzing test coverage metrics, and generating actionable recommendations for improving test quality. Provides detailed insights into test structure, dependencies, and coverage thresholds with visual feedback.
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