Add a PR change risk indicator to the PR panel

[nicoalbanese]

Summary

Add a change risk signal to the PR panel that classifies the current PR as Low, Medium, or High risk.

This is for design/spec discussion only for now — no implementation yet.

Desired behavior

• Only analyze risk after a PR exists
• Recompute when the PR head SHA changes
• Reuse the cached result when the PR head SHA is unchanged
• Show the result in the open PR panel
• Initial output should be:
  • risk level: Low | Medium | High
  • short reason

Risk rubric

Risk should weight critical surface area more heavily than raw diff size.

Examples:

• High: auth, permissions, secrets, billing, data writes/deletes, migrations, infra/config, security-sensitive code
• Medium: data fetching, business logic, state management, routing, non-trivial backend behavior
• Low: copy, styling, isolated presentational UI, tests-only, small local refactors

Recommended v1 approach

Use a hybrid approach instead of sending the full raw diff by default:

1. Build a PR file manifest with:
   • path
   • status
   • additions/deletions
   • file type
2. Derive local tags such as:
   • auth
   • api
   • migration
   • infra
   • ui
   • tests
3. Use heuristics first for a baseline risk score
4. Optionally send the manifest and small sanitized snippets for sensitive files to GPT-5.4 for the final classification

Security / privacy constraints

• Treat diff contents as untrusted input
• Use structured output only
• Redact likely secrets before any model call
• Skip binaries and generated files
• Cap input size
• Cache by PR head SHA to avoid repeated calls
• Persist only the final risk result, not raw diff content used for prompting

Open questions

• Should v1 use only file metadata/tags, or include small sanitized snippets for sensitive files?
• Should the cached result live on the session record keyed by PR head SHA?
• Should the UI eventually include impacted areas in addition to the short reason?

Acceptance criteria

• Analysis timing is defined clearly
• Low/medium/high rubric is documented
• Model input is minimized and sanitized
• Cache invalidation is based on PR head SHA
• No implementation is started as part of this issue