Get Shit Done (GSD)
GSD is a meta-prompting and context engineering system for AI coding assistants. It solves context rot — the quality degradation that occurs as AI fills its context window during long development sessions.
Install: npx get-shit-done-cc@latest
Repo: https://github.com/gsd-build/get-shit-done
Compatible with: Claude Code, OpenCode, Gemini CLI, Cursor, Windsurf
Core Problem It Solves
Long-running AI coding sessions degrade in quality as context fills up. GSD combats this by:
- Externalising project state to structured files (
PROJECT.md,REQUIREMENTS.md,ROADMAP.md,STATE.md) - Running heavy work in fresh subagent contexts, keeping the main window at 30–40% capacity
- Enforcing structured phases so AI never does more than one thing at a time
Six-Stage Workflow
| Stage | Command | Purpose |
|---|---|---|
| Initialize | /gsd-new-project | Gather requirements, generate roadmap |
| Discuss | /gsd-discuss-phase | Capture implementation decisions pre-plan |
| Plan | /gsd-plan-phase | Create verifiable task plans |
| Execute | /gsd-execute-phase | Parallel execution with atomic commits |
| Verify | /gsd-verify-work | Manual acceptance testing and diagnosis |
| Ship | /gsd-ship | PRs, milestones, release management |
Key Concepts
- Context engineering: Structured files survive session boundaries; AI reads them on wake-up
- Parallel execution waves: Independent tasks run simultaneously in fresh subagent contexts
- Multi-agent orchestration: Distributes work across agents to prevent single-window overload
- Atomic commits: Each plan step is committed independently for safe rollback
- Profile-based installation: Core / Standard / Full feature sets; quality/balanced/budget model profiles
Notable Features
- Package legitimacy checks during research/planning phases (supply chain awareness)
- Optional “fallow structural review” pre-pass for code quality
- Interactive and autonomous (
--dangerously-skip-permissions) modes - Skills install to
~/.claude/skills/gsd-*/for Claude Code
Assessment
Strong approach for solo devs and small teams doing multi-session AI-assisted development. The context engineering discipline (externalising state, capping window usage) is the key insight — it’s a systematic answer to a real and widely felt problem. The six-stage structure forces deliberate planning before execution, which reduces AI hallucination and drift.