What Is the GSD Framework for Claude Code? How to Break Complex Tasks Into Clean Context Phases

Why Your Claude Code Sessions Keep Going Sideways

You give Claude a complex task. It starts strong, then drifts. The code it writes contradicts the plan it just made. The review it offers misses obvious bugs. By the end of a long session, you’re re-reading a wall of context trying to figure out where things went wrong.

This is a context problem — and the GSD framework for Claude Code is a direct fix.

GSD (Get Stuff Done) is a structured prompt engineering approach that splits complex coding tasks into three distinct phases: plan, execute, and review. Each phase gets its own clean context window, its own clear objective, and Claude’s full attention. The result is consistently better outputs with fewer correction cycles.

This guide explains how the framework works, why it’s effective, and how to actually implement it in your Claude Code workflow.

The Real Problem: Context Pollution

Before getting into the framework itself, it’s worth understanding what you’re solving.

Claude’s context window isn’t infinite. More importantly, even when there’s room, performance degrades when a single session tries to do too many different things at once. When you ask Claude to plan, implement, and review code inside a single conversation, you’re stacking three cognitively distinct tasks on top of each other.

Here’s what that looks like in practice:

The planning discussion gets buried under lines of generated code
Implementation decisions get tangled with review commentary
Earlier requirements drift toward the edge of the model’s effective attention
Errors that should be caught at Phase 2 slip through because they were introduced in Phase 1

The model isn’t broken. The context is polluted. Mixing planning artifacts, code, and debugging feedback into one session creates noise that degrades each subsequent output.

The GSD framework’s core insight is simple: each cognitive task deserves its own clean workspace.

What the GSD Framework Actually Is

The GSD framework structures any complex Claude Code task into three sequential, isolated phases:

Plan — Define scope, break down the problem, produce a structured spec
Execute — Write code against that spec, nothing else
Review — Evaluate the output against original requirements, identify issues

Each phase starts fresh. The output of Phase 1 becomes the input to Phase 2. The output of Phase 2, combined with the original requirements, becomes the input to Phase 3.

Nothing carries over except what you explicitly pass forward. No accumulated conversation history. No half-finished thoughts from three messages ago. Just the structured artifact from the previous step.

This isn’t a new idea in software engineering — it mirrors how senior engineers think about breaking down work. But applying it explicitly to Claude Code sessions turns an ad-hoc conversation pattern into a repeatable system that produces better results at scale.

Phase 1: The Plan Phase

The goal of the Plan phase is to produce a written specification before a single line of code gets written.

Start a new Claude Code session with only the problem statement and relevant constraints. Your prompt should explicitly tell Claude its job in this phase: produce a plan, not code.

What to include in your Plan phase prompt

A clear description of what you’re building or changing
The files, modules, or systems that will be affected
Any constraints (performance requirements, existing patterns to follow, libraries in use)
An explicit instruction: “Do not write any code yet. Your output should be a structured plan.”

What a good Plan phase output looks like

Claude should return something like:

A numbered list of implementation steps
Which files need to be created or modified
Function signatures or interface definitions (pseudocode is fine)
Edge cases and error conditions to handle
Any ambiguities or open questions that need resolving before coding

This output becomes your spec document. Save it — literally copy it to a plan.md file in your project directory. You’ll use it in both Phase 2 and Phase 3.

Why a fresh context matters here

Planning requires broad, exploratory thinking. Claude needs to consider the problem from multiple angles before converging on an approach. If there’s already code or implementation discussion in the context, the model’s “attention” gets anchored to that rather than exploring alternatives. A clean context lets the Plan phase be genuinely generative.

Phase 2: The Execute Phase

Now you write code. But you don’t ask Claude to think about the problem — you ask it to implement the plan.

Start a completely new session. Provide two things: the plan from Phase 1 and a focused implementation prompt.

Structure your Execute phase prompt like this

Here is the implementation plan:
[paste plan.md contents]

Your task: implement Step 1 through Step 4 exactly as described. 
Follow the file structure and function signatures specified. 
Do not deviate from the plan without flagging it first.

The “do not deviate” instruction matters. It keeps Claude from improvising mid-implementation in ways that might conflict with later steps or the overall architecture.

Breaking execute phases into sub-phases

For large plans, don’t try to execute everything in one shot. Break the Execute phase into multiple sub-sessions, each covering a logical chunk of the plan. This keeps each session focused and prevents the same context drift you’re trying to avoid.

A common pattern is:

Execute 2a: Core logic and data structures
Execute 2b: API layer or interface
Execute 2c: Error handling and edge cases

Each sub-session gets the relevant plan sections, not the entire plan. The less irrelevant context, the better.

What to do when Claude deviates

Claude will sometimes push back on the plan or suggest changes. This is actually useful. When it happens, note the deviation and take it back to a short planning session before proceeding. Don’t let it improvise in the Execute phase without explicit approval — that’s how you end up with a half-implemented original plan and half-implemented improvisation that don’t fit together.

Phase 3: The Review Phase

The Review phase is where most developers skip ahead and regret it.

After Execute, it’s tempting to assume the code is good because it looks right. The Review phase exists to stress-test that assumption with fresh eyes — specifically, Claude’s fresh eyes with a clean context that isn’t carrying the implementation biases of the Execute session.

What to provide in the Review phase

Start yet another new session. Provide:

The original requirements (not the plan — go back to what the user or ticket asked for)
The generated code from Phase 2
A review brief

A good review brief might look like:

Review the following code against the original requirements. 
Check for:
- Correctness: does it actually solve the stated problem?
- Edge cases: what inputs or conditions could break this?
- Security: any obvious vulnerabilities?
- Performance: anything that will cause issues at scale?
- Code quality: readability, naming, unnecessary complexity?

Return a structured review report with a severity rating for each issue.

Why “original requirements” and not “the plan”

The plan was an interpretation of the requirements. Claude might have made a reasonable planning decision that still missed the actual goal. By reviewing against the source requirements, you catch plan-level errors that would otherwise only surface in production.

What to do with Review output

The review report becomes the input for a targeted fix session. Don’t try to fix everything in the Review phase conversation — use the review output as a prioritized bug list, then address each issue in its own focused session or as a new Execute sub-phase.

How to Run GSD in Claude Code: Practical Setup

Use `/clear` between phases

In Claude Code, the /clear command resets the conversation context. This is your primary tool for enforcing phase boundaries. Treat it like a commit checkpoint — you’re signaling a phase transition.

Store phase outputs as files

Make it a habit to save Phase 1 output as plan.md and Phase 2 output as implementation-notes.md (or just the actual code files). This creates a clear artifact trail and makes it easy to pass structured context into the next phase without relying on conversation history.

Use system prompts to reinforce role boundaries

If you’re using the Claude API directly or a tool that supports system prompts, you can use them to enforce the phase role. A Plan phase system prompt might say: “You are a software architect. Your only output is planning documents. Do not write implementation code.”

This is a lightweight but effective way to prevent mode-mixing.

Template your phase prompts

The structure of each phase prompt is largely the same across tasks. Build yourself a small prompt library:

plan-prompt.md — your standard Plan phase opener
execute-prompt.md — your standard Execute phase opener
review-prompt.md — your standard Review phase opener

Fill in the task-specific details each time. This keeps your phase discipline consistent even when you’re moving fast.

Common Mistakes When Using GSD

Skipping the plan and going straight to code

This is the most common one. If a task seems “simple enough,” the temptation is to skip Phase 1. But simple tasks that turn complex mid-execution are exactly where context pollution causes the most damage. The Plan phase only takes a few minutes — use it every time.

Carrying conversation history between phases

Opening a new conversation for each phase is non-negotiable. If you’re summarizing “what happened in the last session” at the start of a new one, you’re reintroducing the context pollution you’re trying to avoid. Only pass structured artifacts — plan docs, code files, review reports — not conversation summaries.

Asking Claude to review its own just-written code in the same session

This is the classic mistake. Asking Claude to review code in the same session it wrote it is like asking a writer to proofread a document the moment they finish it. The model’s context is saturated with its own reasoning from the writing process. A separate Review session with clean context surfaces issues that the Execute session will reliably miss.

Making the plan too vague

“Implement the authentication module” is not a plan — it’s a task description. A plan specifies the component structure, the files involved, the function signatures, the libraries to use, and the edge cases to handle. If the plan is vague, the Execute phase becomes a planning session in disguise, and you’re back to mode-mixing.

Where MindStudio Fits Into This Pattern

The GSD framework’s core structure — isolated phases with explicit inputs and outputs — is exactly how well-designed AI workflows work at a system level too.

MindStudio is a no-code platform for building AI agents and multi-step workflows. Its visual workflow builder lets you chain AI calls in sequence, where each step gets a focused prompt, defined inputs, and structured outputs that feed directly into the next step.

If you’re running GSD-style workflows regularly — or want to automate them — you can build the entire plan → execute → review pipeline in MindStudio without writing infrastructure code. Each phase becomes a discrete workflow step with its own model, prompt, and output format. Claude handles the reasoning at each step; MindStudio handles the orchestration, context handoff, and state management between them.

This is particularly useful for teams that want to standardize the GSD pattern across projects. Instead of relying on individual developers to remember phase discipline, you codify the structure in a reusable workflow that runs consistently every time.

MindStudio also supports 200+ AI models — so you can mix models across phases if it makes sense. A cheaper, faster model for the Review phase, for example, while using Claude’s extended thinking for the Plan phase.

You can start building for free at mindstudio.ai.

Frequently Asked Questions

What does GSD stand for in the context of Claude Code?

GSD stands for “Get Stuff Done.” It’s a prompt engineering framework for structuring complex AI coding sessions into distinct phases — plan, execute, and review — each with a clean context window. The name reflects its practical orientation: the goal is structured process that produces working outputs, not theoretical purity.

Why does context window size matter for Claude Code performance?

Context window size matters because LLMs don’t give equal attention to all content in a long context. As more content accumulates — especially mixed content like planning discussion, code, and debugging feedback — earlier information becomes less reliably referenced. Separating tasks into clean sessions ensures Claude is focused on the current phase’s objective without irrelevant prior context competing for attention. Anthropic’s research on Claude’s architecture informs much of how practitioners think about context management in production workflows.

Can you use the GSD framework with other AI coding tools, not just Claude?

Yes. The framework’s principles apply to any LLM-based coding tool that uses a context window — Cursor, GitHub Copilot Chat, GPT-4, and others. The specific mechanics (like the /clear command) are Claude Code-specific, but the phase structure and the discipline of not mixing planning, implementation, and review contexts applies universally.

How many phases should a complex task have?

The basic framework is three phases: plan, execute, review. For larger tasks, the Execute phase is typically broken into sub-phases covering logical implementation chunks. There’s no strict limit — the right number is whatever keeps each session’s objective narrow and its context uncluttered. A good rule of thumb: if a session is expected to run more than 30–40 exchanges, it’s probably doing too much.

Does the GSD framework slow you down?

In the short term, it adds a few minutes of structure upfront. In practice, most developers find it faster overall because it reduces the debugging and correction cycles that come from mode-mixed sessions. You spend less time re-reading long conversation histories, less time asking Claude to undo decisions it made mid-session, and less time debugging code that was written without a clear plan. The overhead is front-loaded; the savings are downstream.

What’s the best way to pass context between GSD phases?

Structured artifacts — not conversation summaries. Save Phase 1 output to a plan.md file. Save Phase 2 output as the actual code files. Pass those as explicit inputs to the next phase. Avoid summarizing “what we discussed last session” — summaries introduce interpretation and lose specificity. The raw structured output from each phase is always better than a paraphrase of it.

Key Takeaways

The GSD framework splits Claude Code sessions into three phases — plan, execute, review — each with a clean context window.
Context pollution (mixing planning, coding, and review in one session) is the primary cause of degraded Claude Code output quality.
The Plan phase produces a written spec; the Execute phase implements it; the Review phase evaluates it against the original requirements.
Use /clear between phases, store outputs as structured files, and never carry conversation history between phases.
The framework scales — Execute phases can be broken into sub-phases for larger tasks without compromising the core discipline.
If you want to automate GSD-style workflows across a team, MindStudio’s visual workflow builder lets you codify the pattern as a repeatable, multi-step AI pipeline.