Reverse-engineers any codebase into a complete Product Requirements Document (PRD) by analyzing routes, components, state, APIs, and user interactions. Use when: 'generate PRD from code', 'reverse-engineer requirements', 'code to documentation', 'extract product specs', 'document API endpoints', 'write requirements from existing codebase'.
Instrucciones de origen · Vista previa de solo lectura
Name
code-to-prd
Tier
STANDARD
Category
product
Dependencies
none
Author
Alireza Rezvani
Version
2.1.2
name
code-to-prd
description
Reverse-engineers any codebase into a complete Product Requirements Document (PRD) by analyzing routes, components, state, APIs, and user interactions. Use when: 'generate PRD from code', 'reverse-engineer requirements', 'code to documentation', 'extract product specs', 'document API endpoints', 'write requirements from existing codebase'.
license
MIT
metadata
{"updated":"2026-03-17T00:00:00.000Z"}
Name
Code → PRD
Description
Reverse-engineer any frontend, backend, or fullstack codebase into a complete Product Requirements Document (PRD). Analyzes routes, components, models, APIs, and user interactions to produce business-readable documentation detailed enough for engineers or AI agents to fully reconstruct every page and endpoint.
Code → PRD: Reverse-Engineer Any Codebase into Product Requirements
Mock detection: Automatically distinguishes real API integrations from mock/fixture data
Enum extraction: Exhaustively lists all status codes, type mappings, and constants
Model extraction: Parses Django models, NestJS entities, Pydantic schemas
Automation scripts: codebase_analyzer.py for scanning, prd_scaffolder.py for directory generation
Quality checklist: Validation checklist for completeness, accuracy, readability
Usage
# Analyze a project and generate PRD skeleton
python3 scripts/codebase_analyzer.py /path/to/project -o analysis.json
python3 scripts/prd_scaffolder.py analysis.json -o prd/ -n "My App"# Or use the slash command
/code-to-prd /path/to/project
Examples
Frontend (React)
/code-to-prd ./src
# → Scans components, routes, API calls, state management# → Generates prd/ with per-page docs, enum dictionary, API inventory
Backend (Django)
/code-to-prd ./myproject
# → Detects Django via manage.py, scans urls.py, views.py, models.py# → Documents endpoints, model schemas, admin config, permissions
Fullstack (Next.js)
/code-to-prd .
# → Analyzes both app/ pages and api/ routes
# → Generates unified PRD covering UI pages and API endpoints
Role
You are a senior product analyst and technical architect. Your job is to read a frontend codebase, understand every page's business purpose, and produce a complete PRD in product-manager-friendly language.
Dual Audience
Product managers / business stakeholders — need to understand what the system does, not how
Engineers / AI agents — need enough detail to fully reconstruct every page's fields, interactions, and relationships
Your document must describe functionality in non-technical language while omitting zero business details.
For backend-only projects, the "page" concept maps to API resource groups or admin views. The same 3-phase workflow applies — routes become endpoints, components become controllers/views, and interactions become request/response flows.
Workflow
Phase 1 — Project Global Scan
Build global context before diving into pages.
1. Identify Project Structure
Scan the root directory and understand organization:
Identify framework from package.json (Node.js frameworks) or project files (manage.py for Django, requirements.txt/pyproject.toml for Python). Routing, component patterns, and state management differ significantly across frameworks — identification enables accurate parsing.
2. Build Route & Page Inventory
Extract all pages from route config into a complete page inventory:
Field
Description
Route path
e.g. /user/list, /order/:id
Page title
From route config, breadcrumbs, or page component
Module / menu level
Where it sits in navigation
Component file path
Source file(s) implementing this page
For file-system routing (Next.js, Nuxt), infer from directory structure.
For backend projects, the page inventory becomes an endpoint/resource inventory:
Field
Description
Endpoint path
e.g. /api/users, /api/orders/:id
HTTP method
GET, POST, PUT, DELETE, PATCH
Controller/view
Source file handling this route
Module/app
Which NestJS module or Django app owns it
Auth required
Whether authentication/permissions are needed
For NestJS: extract from @Controller + @Get/@Post/@Put/@Delete decorators.
For Django: extract from urls.py → urlpatterns and viewsets.py → router registrations.
3. Map Global Context
Before analyzing individual pages, capture:
Global state — user info, permissions, feature flags, config
Table columns (name, format, sortable, filterable)
Row action buttons (what each one does)
Field name extraction priority:
Hardcoded display text in code
i18n translation values
Component placeholder / label / title props
Variable names (last resort — provide reasonable display name)
D. Interaction Logic
Describe as "user action → system response":
[Action] User clicks "Create"
[Response] Modal opens with form fields: ...
[Validation] Name required, phone format check
[API] POST /api/user/create with form data
[Success] Toast "Created successfully", close modal, refresh list
[Failure] Show API error message
Status transitions (e.g. approval flows: pending → approved → rejected)
Import / export
Field interdependencies (selecting value A changes options in field B)
Permission controls (buttons/fields visible only to certain roles)
Polling / auto-refresh / real-time updates
E. API Dependencies
Case 1: API is integrated (real HTTP calls in code)
API Name
Method
Path
Trigger
Key Params
Notes
Get users
GET
/api/user/list
Load, search
page, size, keyword
Paginated
Case 2: API not integrated (mock/hardcoded data)
When the page uses mock data, hardcoded fixtures, setTimeout simulations, or Promise.resolve() stubs — the API isn't real yet. Reverse-engineer the required API spec from page functionality and data shape.
For each needed API, document:
Method, suggested path, trigger
Input params (name, type, required, description)
Output fields (name, type, description)
Core business logic description
Detection signals:
setTimeout / Promise.resolve() returning data → mock
Data defined in component or *.mock.* files → mock
Real HTTP calls (axios, fetch, service layer) with real paths → integrated
__mocks__ directory → mock
F. Page Relationships
Inbound: Which pages link here? What parameters do they pass?
Outbound: Where can users navigate from here? What parameters?
Data coupling: Which pages share data or trigger refreshes in each other?
Phase 3 — Generate Documentation
Output Structure
Create prd/ in project root (or user-specified directory):
prd/
├── README.md # System overview
├── pages/
│ ├── 01-user-mgmt-list.md # One file per page
│ ├── 02-user-mgmt-detail.md
│ ├── 03-order-mgmt-list.md
│ └── ...
└── appendix/
├── enum-dictionary.md # All enums, status codes, type mappings
├── page-relationships.md # Navigation map between pages
└── api-inventory.md # Complete API reference
README.md Template
# [System Name] — Product Requirements Document## System Overview
[2-3 paragraphs: what the system does, business context, primary users]
## Module Overview
| Module | Pages | Core Functionality |
|--------|-------|--------------------|
| User Management | User list, User detail, Role mgmt | CRUD users, assign roles and permissions |
## Page Inventory
| # | Page Name | Route | Module | Doc Link |
|---|-----------|-------|--------|----------|
| 1 | User List | /user/list | User Mgmt | [→](./pages/01-user-mgmt-list.md) |
## Global Notes### Permission Model
[Summarize auth/role system if present in code]
### Common Interaction Patterns
[Global rules: all deletes require confirmation, lists default to created_at desc, etc.]
Per-Page Document Template
# [Page Name]> **Route:** `/xxx/xxx`> **Module:** [Module name]> **Generated:** [Date]## Overview
[2-3 sentences: core function and use case]
## Layout
[Region breakdown — text description or ASCII diagram]
## Fields### [Region: e.g. "Search Filters"]
| Field | Type | Required | Options / Enum | Default | Notes |
|-------|------|----------|---------------|---------|-------|
### [Region: e.g. "Data Table"]
| Column | Format | Sortable | Filterable | Notes |
|--------|--------|----------|-----------|-------|
### [Region: e.g. "Actions"]
| Button | Visibility Condition | Behavior |
|--------|---------------------|----------|
## Interactions### Page Load
[What happens on mount]
### [Scenario: e.g. "Search"]-**Trigger:** [User action]
-**Behavior:** [System response]
-**Special rules:** [If any]
### [Scenario: e.g. "Create"]-**Trigger:** ...
-**Modal/drawer content:** [Fields and logic inside]
-**Validation:** ...
-**On success:** ...
## API Dependencies
| API | Method | Path | Trigger | Notes |
|-----|--------|------|---------|-------|
| ... | ... | ... | ... | ... |
## Page Relationships-**From:** [Source pages + params]
-**To:** [Target pages + params]
-**Data coupling:** [Cross-page refresh triggers]
## Business Rules
[Anything that doesn't fit above]
Key Principles
1. Business Language First
Don't write "calls useState to manage loading state." Write "search button shows a spinner to prevent duplicate submissions."
Don't write "useEffect fetches on mount." Write "page automatically loads the first page of results on open."
Include technical details only when they directly affect product behavior: API paths (engineers need them), validation rules (affect UX), permission conditions (affect visibility).
2. Don't Miss Hidden Logic
Code contains logic PMs may not realize exists:
Field interdependencies (type A shows field X; type B shows field Y)
Conditional button visibility
Data formatting (currency with 2 decimals, date formats, status label mappings)
Default sort order and page size
Debounce/throttle effects on user input
Polling / auto-refresh intervals
3. Exhaustively List Enums
When code defines enums (status codes, type codes, role types), list every value and its meaning. These are often scattered across constants files, component valueEnum configs, or API response mappers.
4. Mark Uncertainty — Don't Guess
If a field or logic's business meaning can't be determined from code (e.g. abbreviated variable names, overly complex conditionals), mark it [TBC] and explain what you observed and why you're uncertain. Never fabricate business meaning.
5. Keep Page Files Self-Contained
Each page's Markdown should be standalone — reading just that file gives complete understanding. Use relative links when referencing other pages or appendix entries.
Large projects (>15 pages): Work in batches of 3-5 pages per module. Complete system overview + page inventory first. Output each batch for user review before proceeding.
Small projects (≤15 pages): Complete all analysis in one pass.
Common Pitfalls
Pitfall
Fix
Using component names as page names
UserManagementTable → "User Management List"
Skipping modals and drawers
They contain critical business logic — document fully
Missing i18n field names
Check translation files, not just component JSX
Ignoring dynamic route params
/order/:id = page requires an order ID to load
Forgetting permission controls
Document which roles see which buttons/pages
Assuming all APIs are real
Check for mock data patterns before documenting endpoints
Skipping Django admin customization
admin.py often contains critical business rules (list filters, custom actions, inlines)
Missing NestJS guards/pipes
@UseGuards, @UsePipes contain auth and validation logic that affects behavior
Ignoring database constraints
Model field constraints (unique, max_length, choices) are validation rules for the PRD
Overlooking middleware
Auth middleware, rate limiters, and CORS config define system-wide behavior
Generate PRD directory skeleton from analysis JSON
python3 prd_scaffolder.py analysis.json
Recommended workflow:
# 1. Analyze the project (JSON output — works for frontend, backend, or fullstack)
python3 scripts/codebase_analyzer.py /path/to/project -o analysis.json
# 2. Review the analysis (markdown summary)
python3 scripts/codebase_analyzer.py /path/to/project -f markdown
# 3. Scaffold the PRD directory with stubs
python3 scripts/prd_scaffolder.py analysis.json -o prd/ -n "My App"# 4. Fill in TODO sections page-by-page using the SKILL.md workflow
Both scripts are stdlib-only — no pip install needed.
References
File
Contents
references/prd-quality-checklist.md
Validation checklist for completeness, accuracy, readability
references/framework-patterns.md
Framework-specific patterns for routes, state, APIs, forms, permissions
Attribution
This skill was inspired by code-to-prd by @lihanglogan, who proposed the original concept and methodology in PR #368. The core three-phase workflow (global scan → page-by-page analysis → structured document generation) originated from that work. This version was rebuilt from scratch in English with added tooling (analysis scripts, scaffolder, framework reference, quality checklist).
