Conversation view coverage: remaining chat artifacts (test data needed)

Same effort as the iLEAPP side (see iLEAPP #1645/#1647/#1648 and ALEAPP #925, #926, #927). Every conversation view we shipped had its direction column verified against real test images first. These are the artifacts still waiting, each blocked on either test data or a small design question:

- [ ] **Teams** (`teams.py`) - The local user appears to be derivable by matching `UserPreferences.tenantId` to the `User.mri` cid, but that format assumption is fragile and our current test data is thin (32 rows, mostly call logs). Needs a Teams extraction with actual back and forth chat and a known account owner.
- [ ] **GroupMe** (`groupMe.py`) - The messages are all there, but the local user id lives in the app's shared_prefs XML which the artifact doesn't currently collect. Needs a paths addition plus a test extraction that includes the prefs file so we can verify.
- [ ] **TextNow** (`textnow.py`) - Direction exists, but there is a mismatch between the SQL (which selects a `thread_id`) and the emitted headers. Needs a careful column audit plus data to validate against; our current image set had no TextNow rows.
- [ ] **RandoChat** (`RandoChat.py`) - The `Sent?` column is emitted raw from the database and the value set is unverified. Needs an extraction with known sent/received messages.
- [ ] **MeetMe** (`meetme.py`) - Has thread and sender name but no direction column. The DB may hold a flag the query doesn't select. Needs a schema dig plus test data.
- [ ] **Bluesky** (`blueskymessages.py`) - No direction or conversation column in the current output. The DM store likely has both. Schema dig plus test data.
- [ ] **Wire** (`wireMessenger.py`) - Same: no direction or thread in the current output. Schema dig plus test data.
- [ ] **Kijiji** (`kijijiConversations.py`) - Sender and recipient names are present but there is no direction and no account owner source identified yet. Needs a dig for where the app stores the logged-in user.

**How to help:** if you have a test extraction for any of these where you know who sent what, comment here. Verification is fast once we have ground truth. And if you want to attempt one yourself, the derivation patterns are documented in the merged PRs above: self flags in the DB, account/service tables, sidecar uid files, path segments, and structural sources. The one rule is that direction must come from data inside the evidence and be verified, never assumed.

The issue lists eight chat artifacts needing test data and schema investigation. Start by examining the corresponding Python modules (e.g., teams.py, groupMe.py) in the repository to understand the current data structures and queries. For each artifact, the goal is to locate the source of the local user ID or direction flag within the evidence files (databases, shared_prefs, etc.), then verify with a known test extraction. The merged PRs (iLEAPP #1645/#1647/#1648, ALEAPP #925, #926, #927) document derivation patterns for direction columns.

索引モデルが issue の本文から書いたものです。