gsd-build/get-shit-done
Get Shit Done
This project is an autonomous software development assistant and project management tool that utilizes a multi-agent orchestrator to automate complex workflows. It functions as an agentic framework designed to research, plan, execute, and verify software development tasks by coordinating specialized agents that manage context windows and system performance.
The system distinguishes itself through a structured, interview-based requirement engineering phase that clarifies project objectives before initiating automated work. It employs atomic task decomposition to break goals into independent units, executing them in parallel waves with individual version control commits to ensure traceability. A dedicated file mutation safety layer coordinates modifications across planning and installation modules to prevent conflicts and maintain system stability during automated updates.
The platform integrates with external issue trackers to drive development lifecycles from initialization to release. It provides comprehensive project automation, including milestone management, implementation validation, and the ability to customize pull request documentation. Users can configure agent skill sets, model profiles, and workflow toggles through schema-based settings to adapt the system to specific project requirements.
Features
- AI Development Assistants - Integrates AI-driven agents to automate software development workflows, including requirement engineering, task execution, and implementation validation.
- AI Agent Orchestrators - Orchestrates specialized agents to research, plan, and execute complex software development tasks while managing context windows.
- Agentic Workflow Orchestrators - Orchestrates specialized agents to plan, execute, and verify complex project tasks through structured, context-aware workflows.
- Project Management Tools - Provides autonomous project management by decomposing goals into atomic tasks and maintaining project state throughout the development lifecycle.
- Multi-Agent Orchestrators - Orchestrates teams of specialized AI agents to research, plan, execute, and verify complex software development tasks.
- Multi-Agent Systems - Deploys specialized agents to perform research, coding, and testing while managing context windows and system performance.
- Lifecycle Automation - Integrates issue trackers and automated pull request generation to streamline the transition from requirements to production-ready code.
- Project Management - Organizes projects into structured workflows that track progress, milestones, and task execution from initialization to release.
- Multi-Agent Coordination Systems - Coordinates specialized agents to perform specific roles and complete project objectives through defined toolsets and spawning patterns.
- Atomic Transactional Commits - Executes complex goals in small, independent plans with individual version control commits for traceability and safety.
- Agentic Task Orchestration - Orchestrates execution through structured phases by utilizing specialized agents to manage tasks and monitor active context windows.
- Automated Task Execution Engines - Breaks complex goals into small, independent plans executed in parallel waves with individual version control commits for traceability.
- Requirement Clarification Tools - Facilitates a structured interview-based phase to clarify project assumptions and objectives before initiating automated workflows.
- Conversational Project Generators - Facilitates a structured interview phase to clarify project assumptions and requirements before initiating automated task execution.
- Context Engineering - Maintains project-specific documentation and state files to provide high-quality context for automated operations.
- Agentic Development Frameworks - Workflow-focused toolkit for rapid task completion and agentic productivity.
- Individual Skill Modules - System for meta-prompting and spec-driven development.
- Safety and Validation Layers - Provides a safety layer that coordinates file modification policies to prevent conflicts and maintain system stability.
- Context-Aware State Engines - Maintains project-specific documentation and state files to ensure consistent output during automated development processes.
- Issue Tracking Integrations - Links external project management trackers to the local environment to drive execution workflows directly from existing issue tickets.
- Agent Skill Configurations - Configures agent capabilities and skill sets at install time or via runtime commands to enable specific functionality.
- File System Operations - Coordinates file modification policies across planning and installation modules to prevent conflicting changes.
- Configuration Workflows - Customizes project behavior through schema-based settings, including model profiles and workflow toggles.
- Declarative Configuration Schemas - Uses structured schema-based settings to define project behavior, workflow toggles, and inheritance patterns.
- Development Milestone Reviews - Structures development into defined milestones and phases to ensure consistent progress from initialization to release.
- Implementation Validation Frameworks - Performs automated checks against project requirements and facilitates user acceptance testing to ensure delivered features match expectations.
- Command Execution - Translates installer-owned command text into runtime-aware projections for execution within the current environment.
- Command Standardizers - Translates installer-owned command text into environment-specific instructions to ensure correct execution within the local context.
- Pull Request Templates - Appends project-specific documentation or requirement sections to automated pull request bodies during the shipping process.
- Configuration Migration Utilities - Preserves user data while safely moving or rewriting configuration files during software installation and update processes.
- Workflow Automation - Executes structured task management through defined phases, automated command sequences, and configurable branching strategies.
- LLM Performance Monitoring - Tracks context window usage and system architecture metrics to ensure efficient resource management during automated project workflows.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
