Vibe Workflow: A Prompt-Based Pipeline for Planning, Building, and Debugging AI-Generated Code
Templates and workflow for generating PRDs, Tech Designs, and MVP and more using LLMs for AI IDEs
At a glance
- What is it?
- KhazP/vibe-coding-prompt-template offers a structured set of prompts and a CLI to guide LLMs through research, PRD, tech design, and incremental builds. It is a practical system for solo developers and small teams who want more control over vibe coding sessions.
- Who is it for?
- Adopt Vibe Workflow if you are a solo developer or small team using Claude Code, Cursor, or Gemini CLI and you want a repeatable process that separates product thinking from code execution. Skip it if you prefer ad hoc prompting or if your project is too large for a single AI agent to handle in one context window.
- Can I use it commercially?
- Yes. MIT is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 5 days ago.
- What is it written in?
- Mainly TypeScript, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The Problem: Unstructured Vibe Coding Sessions
When you let an AI coding agent work without a plan, the session often wanders. The agent may start coding before the requirements are clear, or it may fix one bug and break another. Vibe Workflow addresses this by providing a sequence of prompts that force a product definition and a technical design before any code is written. The target user is a developer who uses tools like Claude Code, Cursor, or Gemini CLI and wants to avoid the chaos of a raw, unguided chat. The README frames it as a way to decide what to build, check what works, and recover when it breaks. This is not a library or a framework. It is a set of markdown files and a CLI that wraps them into a guided conversation.
How the Workflow Is Structured: Five Steps in Two Phases
The workflow splits into two phases. Phase 1 is about thinking: you do deep research, write a PRD, and create a tech design. These steps happen in any chat tool, and you do not need a repository yet. Phase 2 is execution inside your IDE: you generate agent files like AGENTS.md and agent_docs/, then build in small, verified passes. The README shows a mermaid diagram that starts with an idea and flows through research, PRD, tech design, AGENTS.md, and finally an MVP or AI app. The key mechanism is that each step has its own prompt file. For example, part1-deepresearch.md contains a prompt you copy and paste into a chat tool. The AI then asks clarifying questions and produces a research document. You save that output and feed it into the next prompt. This sequential handoff is what keeps the context grounded. The workflow also offers three planning levels: Quick, Guided, and Deep, which adjust the number of questions the AI asks.
Getting Started: The npx Command and Manual Prompt Pasting
There are two ways to run the workflow. The first is to open an AI coding agent like Claude Code or Cursor in your project and say: Run npx vibeworkflow and follow its instructions. The CLI inspects what already exists in the folder, then routes you to one of three paths: start something new, continue my project, or something broke. The second way is manual: you copy the contents of each prompt file from the repository and paste them into a chat tool in order. The README includes a table that maps each step to the file and the expected output. For instance, Step 1 is Deep Research, and the file is part1-deepresearch.md. The CLI is installed via npx, which means you do not need a global install. The repository also mentions slash commands like /vibe-change, /vibe-debug, and /vibe-verify for use in an existing app, available since version 0.3.0.
What the Prompts Actually Require: Evidence and Source Grounding
The deep research step is not a free-form brainstorm. The README instructs you to turn on web search, source grounding, or deep research mode if your chat tool supports it, and to require cited claims with access dates. This is a concrete constraint that separates this template from a simple 'tell me about my idea' prompt. The PRD step then asks the AI to define what you are building and for whom, which forces scope decisions early. The tech design step picks the surface, stack, and deployment. The emphasis on evidence and citation is notable because many prompt templates skip this. It means the workflow is designed for projects where you need to verify assumptions, not just generate code. If your chat tool does not support browsing, the prompt still produces a research prompt you can run elsewhere, according to the README.
Limitations: Context Length, Tool Dependency, and Manual Steps
The workflow depends heavily on the capabilities of the underlying AI tool. If your chat tool lacks web search, the deep research step degrades to a prompt that you must run elsewhere, which adds friction. The manual path also requires you to copy and paste long markdown files and save outputs between steps. That is a real cost if you switch tools mid-workflow. Another limitation is that the workflow assumes a single agent can handle the whole process. For a large codebase, the generated AGENTS.md file and the build instructions may exceed the context window of the model, causing the agent to lose earlier context. The README does not address scaling to multi-agent or multi-repo setups. The 'continue my project' path exists, but the material does not explain how it handles a long-lived codebase with many files. This is a tool for greenfield MVPs or small incremental changes, not for large enterprise refactors.
Alternatives: Other Prompt Engineering Approaches
A common alternative is to use a single, monolithic system prompt that you paste at the start of every session. That approach has no phased handoff and no separate research step, so the AI often starts coding immediately. Another alternative is to use a dedicated prompt manager or a library like LangChain, which programmatically chains prompts and manages memory. The difference is that Vibe Workflow is static markdown plus a thin CLI, so there is no runtime dependency on a specific framework. It works with any chat tool, whereas LangChain requires you to write code and integrate with a model provider. A third alternative is to rely on the built-in planning features of tools like Claude Code's subagents, but those are tied to a specific editor. Vibe Workflow is tool-agnostic at the prompt level, which is its main advantage over editor-specific solutions.
Maintenance, Licensing, and Upgrade Considerations
The repository is under the MIT License, which means you can use, modify, and redistribute the prompt files freely, including in commercial projects. The project has an active release history: v2.4.0 was an audit and hardening release, v3.0.0 was called the Contracts Release, and v3.1.0 is the Agent-First Release. This suggests the prompts and CLI are being refined, but it also means you need to track changes if you rely on the templates. The last push was in September 2026, so the project is not abandoned. The README mentions that the workflow was used on several public projects, including the project's own website, which gives some evidence of real-world use, but you should not treat that as a guarantee of stability. Before adopting, check the changelog for v3.1.0 to see what changed, especially since the agent-first focus may alter how the CLI behaves.
Editorial conclusion
Adopt Vibe Workflow if you are a solo developer or small team using Claude Code, Cursor, or Gemini CLI and you want a repeatable process that separates product thinking from code execution. Skip it if you prefer ad hoc prompting or if your project is too large for a single AI agent to handle in one context window. Before adopting, verify that the prompt files in your cloned repo match the latest release (v3.1.0), test the npx command in a clean folder, and confirm that the generated AGENTS.md file fits your specific codebase constraints.
Community notes