# GSD, a Meta-Prompting System That Solves Context Window Degradation

> GSD solves context window degradation by breaking work into atomic plans executed in fresh subagent contexts with parallel wave execution.

Published: 2026-03-25
URL: https://daniliants.com/insights/gsd-get-shit-done-meta-prompting-context-engineering-spec-driven-dev/
Tags: claude-code, context-engineering, spec-driven-development, meta-prompting, ai-coding, subagent-orchestration, vibecoding

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## Summary

GSD is a meta-prompting system for Claude Code that solves context window degradation by breaking work into atomic plans executed in fresh subagent contexts. It provides a full lifecycle workflow (discuss, plan, execute, verify, ship) with parallel wave execution, keeping the main context at 30-40% usage while orchestrating thousands of lines across parallel executors.

## Key Insight

- **Context rot is the core problem being solved** - as Claude fills its context window, output quality degrades. GSD keeps the orchestrator thin (30-40% context usage) while spawning fresh 200k-token subagent contexts for actual implementation work.
- **Wave-based parallel execution** - plans are grouped into dependency waves. Independent plans run in parallel within a wave, dependent plans wait for earlier waves. Vertical slices (end-to-end features) parallelize better than horizontal layers (all models, then all APIs).
- **Atomic commits per task** - each task gets its own git commit, making bisect possible and individual tasks revertable. Commit format: `feat(phase-plan): description`.
- **Multi-runtime support** - works with Claude Code, OpenCode, Gemini CLI, Codex, Copilot, Cursor, and Antigravity via a single npx installer. Not locked to one AI coding tool.
- **Config-driven quality/cost tradeoff** - model profiles let you assign Opus for planning and Sonnet for execution (balanced), or go full Opus (quality) or Sonnet+Haiku (budget).
- **Built-in security** - path traversal prevention, prompt injection detection, and shell argument sanitization since v1.27. Important because planning artifacts (markdown) become LLM system prompts, creating indirect injection vectors.
- **The discuss phase is underrated** - captures implementation preferences before planning. Planner reads locked decisions ("user wants card layout") so it doesn't guess. Skipping it gives reasonable defaults; using it gives your specific vision.