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Get Shit Done - Context Engineering for AI Coding Agents

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#claude-code#context-engineering#meta-prompting#ai-coding#developer-tools#spec-driven#cursor#copilot#open-source#agent-tools
Inputs: textOutputs: text
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About Get Shit Done

Get Shit Done (GSD) is an open-source system designed to address context rot in AI coding agents—the progressive degradation of output quality as the context window fills during extended development sessions. Originally created for Claude Code by TÂCHES, the tool provides meta-prompting, context engineering, and spec-driven development capabilities to maintain consistent, high-quality interactions. It has since evolved into GSD Core, hosted under the Open GSD organization. GSD integrates with a wide range of AI coding agents, including Claude Code, OpenCode, Gemini CLI, Codex, Copilot, Cursor, Windsurf, and over 15 others, making it a versatile addition to developer toolchains.

By focusing on context engineering, GSD helps developers structure and manage the information fed to AI agents, preventing the common issue where early context is lost or diluted. Its spec-driven development approach allows for formalizing requirements and instructions, ensuring that agents stay aligned with project goals across long conversations or complex tasks. The tool is lightweight and leverages standard CLI workflows, making it accessible for teams and individual developers who rely on AI-assisted coding.

GSD is distributed under the MIT license and is available via GitHub. While the original repository at gsd-build/get-shit-done is archived, active development continues in the open-gsd/gsd-core repository. As an open-source project, users can inspect, modify, and contribute to the codebase, though they should verify compatibility with their specific AI coding agent and workflow requirements.

Key Features

Meta-prompting system maintains agent quality across long sessions
Context engineering prevents context rot degradation in AI agents
Spec-driven development with structured templates
Supports 15+ AI coding agents including Claude Code, Cursor, and Copilot
Lightweight npm package install with zero config required
Built-in spec templates for common development patterns
Active community with Discord support channel

Pros & Cons

Pros
  • Open-source and free to use under the MIT license
  • Lightweight design integrates easily into existing CLI and Git workflows
  • Addresses a real pain point—context rot—that affects many AI coding agents
  • Compatible with a broad range of popular AI coding tools
  • Active development and community support (via the new repository)
  • No usage limits or paywalls, as it is self-hosted open-source software
Cons
  • Primarily designed for developers comfortable with CLI and Git workflows
  • Requires some setup and configuration to tailor to specific agents and projects
  • Effectiveness depends on the quality of user-defined specs and prompts
  • Active development may introduce breaking changes; users should track updates
  • Does not work as a standalone tool; requires an AI coding agent to function

Best For

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