Vibe Coding MVP Workflow
Turn a product idea into structured MVP specifications and agent-ready development context.
The source repeatedly requires planning, approval before execution, incremental changes, diff review, and testing, which fully supports user confirmation. The two GitHub workflows declare permissions broadly proportionate to commenting and labeling, but they still create external writes, so least privilege and external-effects handling are not exhaustive. The README says material is copied into third-party AI/IDE services and recommends pre-deployment checks for secrets, authentication, and dependencies, but supplies no complete data-flow map, retention policy, sensitive-data procedure, or dependency-audit mechanism; Actions are referenced by mutable version tags rather than commit SHAs. No explicit rollback procedure is provided beyond reviewable increments. Authorship, maintenance, licensing, and example projects are attributed, although identity and outcome claims are not independently verified by the supplied files.
The five-stage workflow, filenames, iteration loop, and proposed project structure are internally coherent, fully supporting self-consistency. Multiple tools and adapters offer alternatives, and variable availability is acknowledged, but there is no compatibility matrix, offline path, or dependency-availability guarantee. The troubleshooting table gives actionable responses for ignored documentation, scope inflation, PRD mismatch, and deployment failure, but it does not define structured error messages, diagnostic outputs, or comprehensive failure classes.
The README explicitly addresses non-developers and scenarios including rapid prototypes, production frontends, complex logic, terminal agents, and budget-constrained work; it also clearly excludes native hardware, heavily regulated, and safety-critical use as-is. Each phase specifies a concrete invocation, inputs, outputs, and filenames, providing precise triggers. Distinct adapters are described for Cursor, VS Code, Claude Code, Codex, and Antigravity. These criteria therefore have thorough static evidence and receive full marks.
The stages, templates, artifacts, directories, and tool adapters have clear organization, while prerequisites, setup steps, and naming conventions are detailed and stable. The README points to a worked example, FAQ, support, governance, changelog, and releases, but those referenced contents were not supplied, so examples/FAQ and version history can only be partially confirmed. Limitations are concrete, and the complete MIT text agrees with the metadata, justifying full license credit. A maintainer, monthly cadence, and feedback channels are named, but the publisher remains unverified and neither actual monthly execution nor an update history is demonstrated here.
The prescribed artifacts, filenames, locations, approval loop, review checklist, and pre-deployment checks are directly actionable, fully supporting output usability. PRDs, technical designs, agent contracts, and memory artifacts plausibly add value over an unstructured chat, but claims about shipped projects lack outcome measurements or comparisons. Time estimates, prerequisites, and budget-oriented tool suggestions give basic cost guidance, while model charges, rework, data risk, and total effort across project sizes remain unaddressed.
Most operational claims trace to named stages, files, and repository paths; the LICENSE independently corroborates the license claim, and the workflow files expose their actual permissions and behavior. However, the core templates, example, checklist, validation script, policies, and changelog are only referenced rather than supplied, while shipped-project and monthly-maintenance claims lack cross-source corroboration. Advice, limitations, and instructions are generally separated, and the example is labeled reconstructed, but several effectiveness and tool-capability statements are not explicitly identified as experiential judgments or unverified claims.
- Research, PRDs, technical designs, or code may be pasted into third-party AI services. Remove credentials, personal data, client material, and other secrets first, and verify each service's retention policy.
- The workflow expressly should not be used as-is for safety-critical, heavily regulated, or native-hardware projects; those settings require independent safety, compliance, and engineering controls.
- Deployment safety, dependency security, rollback, and generated-code correctness largely depend on user review. The supplied evidence shows no automated dependency audit, explicit recovery procedure, or executed test results.
- The README references examples, an FAQ, checklists, validation scripts, changelogs, and policy files that were not supplied, so their contents and the stated maintenance cadence are not verified here.
What does this agent do, and when should you use it?
This repository is a collection of prompts, document templates, and agent workflows for MVP development, not a standalone executable agent. Its five-stage process covers market research, product requirements, technical design, agent workspace setup, and incremental coding. Concrete assets include the part1 through part4 prompt files, AGENTS.md-related templates, tool adapters, review checklists, and a worked reddit-to-ai example. The first three stages run through chat products such as ChatGPT, Claude.ai, or Gemini, while execution moves into Cursor, VS Code, Claude Code, Codex, or another coding environment. The workflow produces research, a PRD, a technical design, agent instructions, and eventually application code, but security review and deployment remain the adopter's responsibility.
The user pastes part1-deepresearch.md into a chat product, answers questions about the idea, and saves research-[YourAppName].md. They then use part2-prd-mvp.md to produce PRD-[YourAppName]-MVP.md and part3-tech-design-mvp.md to produce TechDesign-[YourAppName]-MVP.md. In the coding environment, those documents go into docs/, and the coding agent is instructed to read part4-notes-for-agent.md and set up the workspace. That stage instantiates and fills AGENTS.md, MEMORY.md, REVIEW-CHECKLIST.md, and agent_docs/ from templates/, with optional adapters such as CLAUDE.md, .cursor/rules/vibe.mdc, and .agent/rules/vibe.md. The coding agent then builds the MVP through a recurring plan, single-feature execution, and verification loop, while REVIEW-CHECKLIST.md supplies checks for dependencies, secrets, authentication paths, and rate limits.
- A nontechnical or early-stage founder who wants to validate demand and narrow an idea into a buildable MVP before coding.
- A beginner using chat models and an AI IDE who needs an explicit path from discovery through PRD creation to implementation.
- A small product team that wants shared AGENTS.md instructions, project context, testing expectations, and a definition of done for coding agents.
- A developer switching among Claude Code, Cursor, Antigravity, or Codex who wants thin tool-specific adapters backed by one master instruction file.
- A team working across long or multiple agent sessions that needs MEMORY.md, specs/, and recap artifacts for handoffs.
What are this agent's strengths and limitations?
- Provides a complete handoff chain from market research through PRD, technical design, agent configuration, and incremental MVP implementation.
- Uses AGENTS.md as a shared source of truth, with thin adapters for Claude Code, Cursor, and Antigravity; Codex is documented as reading AGENTS.md directly.
- Includes artifact-first continuity practices through MEMORY.md, specs/, and handoff documents instead of relying on one large chat history.
- Supplies a worked reddit-to-ai example, quality and security checklists, and documentation for automated contract validation.
- It is primarily a supervised, copy-and-paste workflow rather than an autonomous agent, executable service, library, or API.
- The research, PRD, and technical-design stages require the user to answer questions and manually save and move generated documents.
- Output quality depends on the selected chat model and coding environment; no deterministic generation or cross-platform equivalence is documented.
- The workflow is explicitly not recommended as-is for native hardware, heavily regulated products, or safety-critical systems.
- Deployment guidance names security checks, Vercel, and Cloudflare but does not provide a one-command deployment or a common application runtime.
How do you install or deploy this agent?
There is no runtime package to install, and the repository does not document package-manager or container commands. Select “Use this template” on GitHub, or clone the repository locally, then open the directory in Cursor, VS Code, or another AI-capable coding environment. Create docs/ and place PRD-[YourAppName]-MVP.md, TechDesign-[YourAppName]-MVP.md, and optionally research-[YourAppName].md inside it. The documented prerequisites are a modern browser, access to an AI chat service, and a coding environment that can work with project files. No required API key, fixed language runtime, or MCP service is specified.
How do you use this agent?
First, paste all of part1-deepresearch.md into ChatGPT, Claude.ai, Gemini, or another chat tool, answer its questions, and save the result as research-[YourAppName].md. Second, paste part2-prd-mvp.md into the same conversation, or supply both the research and prompt in a new chat, then save PRD-[YourAppName]-MVP.md. Third, provide the PRD and use part3-tech-design-mvp.md to discuss budget, schedule, and complexity tradeoffs, saving TechDesign-[YourAppName]-MVP.md. Fourth, move the documents into docs/ and tell the IDE agent: “Read part4-notes-for-agent.md, follow its instructions, and set up my workspace.” After it creates AGENTS.md and the supporting files, give the first build instruction: “Read AGENTS.md, propose a Phase 1 plan, wait for my approval, and then build it step by step.” Review the diff and run available tests after each major feature, then complete the security section of REVIEW-CHECKLIST.md before deployment.
How does this agent compare with similar options?
Compared with beginning implementation directly in a builder or AI IDE, this workflow inserts research, PRD, and technical-design stages before agent setup and coding. Its platform guide positions Lovable for fast full-stack prototypes, v0 for production-oriented frontend work, Cursor or VS Code for learning and sandbox coding, Claude Code for complex logic and multi-agent work, and Codex for terminal and agent workflows. These are documented usage recommendations, not benchmark results.