User Scanner
User Scanner Future and Project: Build a Privacy-Preserving OSINT Evidence Lab
A capstone for versioned modules, consent receipts, pivot graphs, confidence labels, redacted reports, offline fixtures, and governed MCP operations.
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
- Consent and evidence receipts can make broad OSINT workflows reviewable.
- Offline fixtures, status semantics, pivot-cycle tests, and redaction enable safer evolution.
- Accessible text/SVG remains canonical when interactive graph views are added.
Make evidence the product
User Scanner already combines modules, pivots, reports, proxies, and MCP. The next useful layer is a case package that records why a target was scanned, which modules ran, what each response means, how pivots were accepted, and what a reviewer verified.
New vectors, breach integrations, and agent features should remain proposals until their consent, data handling, parser tests, and failure modes are documented.
Define the case manifest
Store package/commit, target type and normalized hash, authorization reference, purpose, module snapshot, proxy/network policy, depth/concurrency, response status, evidence URLs/identifiers, pivot edges, report hashes, reviewer, retention, and deletion receipt. Keep raw personal data and credentials outside the manifest behind access controls.
Generate accessible Markdown/JSON plus local SVG graphs; an optional Three.js explorer can help navigate large pivot graphs but must not replace text evidence or expose hidden data by default.
Ship bounded milestones
Milestone one adds offline module fixtures and distinct found/not-found/blocked/timeout states. Milestone two validates cross-scan depth, cycle prevention, redaction, and report parity. Milestone three adds MCP capability manifests, approvals, and spend/rate budgets. Milestone four canaries new modules and regional changes with human review.
Start report-only, then advisory, then enforce consent, evidence, and retention gates after operators can reproduce them.
Success criteria
A reviewer should replay a safe case, understand every pivot, verify representative results, produce a minimal report, and delete the data on schedule. That is more durable than simply adding more platform modules.
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
Define consent/case/module/pivot/report manifest fields.
- 2
Add offline fixtures for statuses, cycles, redaction, and export parity.
- 3
Govern MCP capabilities, approvals, budgets, and regional changes.
- 4
Move from report-only to advisory/enforced evidence and retention gates.
Copy-ready example
case:
package_commit: <sha>
target_sha256: <hash>
authorization_ref: self-audit-001
modules: [github, etsy]
cross_depth: 1
evidence: [results.json]
report_sha256: <hash>
retention_until: 2026-10-01Frequently asked questions
Should the future lab store full profile data in Git?
No. Keep controlled raw evidence outside Git and commit only sanitized fixtures, hashes, and manifests.
Is a 3D pivot graph required?
No. Use accessible text and local SVG first; add Three.js only as an optional exploration layer.
Sources
- User Scanner README (captured 2026-08-31)Source checked 2026-08-31
- User Scanner repositorySource checked 2026-08-31