User Scanner
User Scanner Source-Code Analysis: Trace a Target Through Modules and Pivots
A fixture-driven method for reading User Scanner's Python engine, module registry, HTTP adapters, cross-scan graph, exporters, and MCP server.
What you will learn
- Explain module, pivot, report, and MCP layers
- Run a bounded scan and interpret uncertainty
- Apply authorization, rate-limit, and data-retention controls
Before you start
- Basic Git and command-line usage
- Comfort reading a project README
You can explain the project, run its documented first step, and decide what to verify before adopting it.
Key takeaways
- Mocked fixtures expose dispatch, status, pivot, and export behavior safely.
- Trace fields and failure mapping instead of assuming every module is equivalent.
- Small parser, cycle, redaction, and schema tests are safer first contributions.
Use a harmless fixture
Start with a synthetic username, an owned email, or a local mock server. Pin the repository/package, select one module, and capture request/response fixtures with secrets removed. This keeps source analysis reproducible without querying hundreds of services.
Use README anchors such as `user_scanner.core.engine`, email/username module paths, `--cross-scan`, report flags, and `user-scanner-mcp`, then confirm actual symbols and tests in the checkout.
Trace normalization and dispatch
Follow CLI parsing into target validation, category/module selection, concurrency configuration, HTTP adapter, and module response normalization. Record fields read/written, timeout/retry behavior, and whether a blocked response differs from not found.
Then locate the module registry and verify how a new module declares target type, URL, parser, metadata, and error mapping. Keep module-specific quirks out of the shared result contract.
Trace cross-scan and exports
Instrument one mocked response that exposes a handle or email, then follow classification, depth, verification, de-duplication, queueing, and parent-child evidence. Force a duplicate and a cycle to test termination.
Follow the same observation set into JSON, CSV, PDF, and MCP response schemas. Check that photos, URLs, and personal fields are redacted or retained only under an explicit policy.
Contribution-sized findings
Prefer a deterministic parser fixture, timeout/error regression, pivot-cycle guard, report redaction test, or MCP schema test. Run the project tests and document observed behavior versus README claims; do not publish recipes for unauthorized collection.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Pin code and create local mocked target/module fixtures.
- 2
Trace CLI validation, registry, HTTP adapter, parser, and result fields.
- 3
Follow one pivot and duplicate/cycle into report and MCP outputs.
- 4
Add a focused regression and record evidence versus assumptions.
Copy-ready example
fixture -> CLI/engine -> module registry -> HTTP mock
status/metadata -> pivot queue -> JSON/CSV/PDF/MCP
regression -> redacted evidenceFrequently asked questions
Can source analysis query all live platforms?
It should not; local fixtures and owned targets provide safer, repeatable evidence.
What is a useful first module contribution?
A parser fixture that distinguishes found, not-found, blocked, timeout, and malformed responses.
Sources
- User Scanner README (captured 2026-08-31)Source checked 2026-08-31
- User Scanner repositorySource checked 2026-08-31