For anyone using AI coding tools
Is your vibe-coded project stuck?_
Vibe coding tools made it easy to start - but they skip the planning, and you pay for it at 80%.
They made it easy to start. Nobody made it easy to finish.
VibeScaffold generates the spec your AI tools need to get all the way to shipped - clear requirements, persistent context, and defined acceptance criteria.
How It Works
Three steps to a complete spec
1
Describe your idea
Tell us what you're building in plain English
2
Chat to refine
AI asks the right questions to fill in the gaps
3
Download 4 docs
Get spec files ready to paste into any AI coding tool
The 80% Problem
AI gets you 80% there. Then you hit the wall.
No Clear Spec
- • Vague user stories, missing edge cases
- • AI fills gaps with hallucinated defaults
Solution
ONE_PAGER.md + DEV_SPEC.md
Forces decisions about audience, MVP scope, auth, data model upfront
Fragmented Context
- • Ideas scattered across chat history
- • Agent can't hold the project in memory
Solution
All 4 docs feed into each other
Single source of truth AI agents can reference every session
'Done' is Undefined
- • No test cases, no acceptance criteria
- • Every fix breaks something else
Solution
PROMPT_PLAN.md + AGENTS.md
Step-by-step prompts with TDD checkboxes and acceptance criteria
What You Get
Four documents. See exactly what's inside.
Real output from VibeScaffold for a Photo Captioner app
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# Photo Captioner - One-Pager Purpose ------- A lightweight mobile app that generates engaging captions for photos to help casual social sharers craft on‑point, shareable text quickly. Problem ------- Social media users often struggle to find short, engaging captions for photos. They want quick, creative text without spending time thinking of the right tone or wording. Target audience --------------- - Primary: Casual social sharers - everyday users who post photos on social platforms (friends, family, lifestyle posts) - Goals: Post frequently with minimal friction, make photos more engaging, sound natural/fun without effort Core user flow (MVP) -------------------- 1. Open app (no account required for MVP) 2. Upload photo / take new photo 3. Select tone from presets: Funny, Heartfelt, Witty 4. Tap "Generate" → show progress 5. Display three caption suggestions 6. User taps "Copy" on chosen caption
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# Photo Captioner - Developer Specification (MVP)
1) Summary & goals
------------------
- Mobile: React Native (Expo) + TypeScript
- Backend: Node.js (18+) + TypeScript + Fastify
- Inference: OpenAI gpt-5-nano (multimodal)
- Auth: Firebase Authentication - passwordless email
- Storage: Google Cloud Storage (6-hour TTL)
2) High-level architecture
--------------------------
Sequence:
1. Client requests /presign-upload with Firebase ID token
2. Backend returns presigned PUT URL and objectPath
3. Client uploads image directly to GCS
4. Client calls /generate with objectPath + tone
5. Backend validates, runs Vision SafeSearch
6. Call OpenAI gpt-5-nano → returns 3 captions
7. Run moderation, filter unsafe content
8. Return up to 3 safe captions
6) API contract
---------------
POST /generate
Request: { objectPath, tone: "funny"|"heartfelt"|"witty" }
Response: { suggestions: [{ id, text, safety_flags }] }1
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# Prompt Plan - Photo Captioner (MVP) Overall stage breakdown ----------------------- Stage A - Project scaffolding Stage B - Core backend & auth Stage C - Media processing & safety Stage D - Model orchestration Stage E - /generate endpoint with rate limiting Stage F - Admin flows Stage G - Client + CI/CD + deployment Prompt 0 - Repo scaffolding --------------------------- Todo checklist: - [ ] Create package.json with scripts - [ ] Add tsconfig.json, jest.config.js - [ ] Implement src/index.ts with /health route - [ ] Add test/health.test.ts - [ ] Add Dockerfile skeleton Prompt 2 - Firebase Auth verification ------------------------------------- Todo checklist: - [ ] Create src/services/authService.ts - [ ] Add verifyFirebaseToken middleware - [ ] Write unit tests with mocked Firebase Admin - [ ] Protect /presign-upload and /generate routes
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# AGENTS.md Purpose ------- This file orients automated agents (Codex, Claude Code, CI bots) to the repository workflow and responsibilities. Agent responsibility -------------------- - After completing any step, immediately update the TODO checklist in prompt_plan.md - Do not consider work "done" until tests are green - Always commit prompt_plan.md alongside code changes Testing policy (non‑negotiable) ------------------------------- - Tests MUST cover functionality being implemented - NEVER ignore test output - logs contain CRITICAL info - Write tests BEFORE implementation (TDD) Guardrails ---------- - Make the smallest change that passes tests - Do not duplicate files to work around issues - If a file cannot be opened, say so and stop
This is a small excerpt. Full documents are 100-800 lines of detailed specifications.
Download Sample Pack (ZIP)4 complete documents for a Photo Captioner app
or
Generate Your First SpecFree. No account required.
2,200+
Active Users
1,300+
Chats Sent
300+
Documents Generated
70+
Complete Specs Created
Works with the tools you already use
Claude Code • Cursor • Codex CLI • Windsurf • Copilot
Also works with: Lovable, Bolt, v0, Replit, and any tool that accepts markdown context
demo.mov
Stop debugging AI-generated spaghetti.
Generate the spec first. Ship the whole thing.
Generate Your First SpecFree. No account required.