trace_data_flowlisted

# trace_data_flow Data flows through systems in predictable patterns. Once you know how to read a codebase's architecture, you can follow any piece of data from its origin to its destination. This skill gives you a systematic method for doing that. ## Why Data Tracing Matters Many bugs aren't caused by a single broken function — they're caused by data being wrong by the time it reaches a function. To fix them, you need to know where the data becomes wrong, not just where the error surfaces. Data tracing is also the fastest way to understand an unfamiliar codebase: follow one request or one piece of data, and you'll understand the architecture. ## The Three Questions For any piece of data, ask: 1. **Where does it originate?** (user input, database, external API, config file, hardcoded value, computed from other data) 2. **How is it transformed?** (what functions/methods touch it, what validations apply, what conversions happen) 3. **Where does it land?** (database, external service, response body, log, cache, memory) ## Tracing a Web Request (Django / typical MVC) Follow this path for an incoming HTTP request: ``` HTTP Request ↓ URL Router (urls.py) → matches URL pattern → selects View ↓ View / Controller (views.py) → parses request body / query params → validates input (serializer, form, Pydantic model) → calls Use Case or Service ↓ Use Case / Service (services/, use_cases/) → applies business logic → calls Repository ↓ Repository (repositor