Contacts Unify

Contacts Unify

Turn a messy, multi-source pile of contacts into one deduplicated, trustworthy address book — with a review UI simple enough for a non-technical person to drive. Optionally, mine iMessage history to find the people worth a deeper OSINT dossier.

Why this matters: duplicates and inconsistency make people stop maintaining their contacts at all. The win is a single source of truth they'll actually keep feeding.

Scope of local_only: Phase A (dedupe) is fully local — no network egress. Phase B is opt-in and its dossier step (research-person) does reach the public web (LinkedIn, gov/org sites). Treat Phase B network access as a deliberate, separately-approved step, never an automatic follow-on.

Consent & authorization (read before running): only operate on contacts/messages belonging to the user, on a machine they own or have explicit permission to access (including any SSH host). Phase B researches other people from the user's private data — only build a dossier on someone the user names explicitly. Never infer a research shortlist from message volume and run it unattended, and don't dossier minors or obviously sensitive relationships.

When to use

• "Fix / clean up / merge my contacts", duplicate cards, contacts scattered across accounts.
• Consolidating iCloud + several Google accounts + Zoho + stray VCF/CSV exports.
• "Who do I talk to most?" → rank message contacts, then research the important ones.

Two phases (run either or both)

Phase A — Unify & dedupe (the core)

Full plan: references/unify-plan.md (read it before building). Summary of the workflow:

1. Confirm the target + sources. Whose contacts? On the local Mac, or over SSH to another box (e.g. ssh <host> → sudo -u <user> -i)? Inventory which sources exist:
   • macOS Contacts / iCloud — sqlite ~/Library/Application Support/AddressBook/Sources/*/AddressBook-v22.abcddb (read mode=ro for rich fields + ZCREATIONDATE/ZMODIFICATIONDATE); osascript for the live list and for writes.
   • Google accounts (often several) — Google Contacts export / People API / Takeout .vcf/CSV.
   • Zoho, legacy Gmail, and any loose .vcf/.csv on Desktop/Downloads.
   • Tag every contact with its source account as provenance.
2. Back up first, always. Whole AddressBook DB → ~/Desktop/Contacts-backup-<ts>/; every source → timestamped snapshot in the working dir. This is non-negotiable.
3. Group & classify. Group by normalized name; cross-link groups sharing a normalized phone (last 10 digits) or email even under different names. Classify each: identical (auto-merge), combine (safe union), review (disjoint → maybe different people → default keep-separate). Name-only cards (businesses, placeholders with no phone/email) can't be matched on identifiers — group them by normalized name only and always route them to review, never silent auto-merge.
4. Review UI. Launch the self-contained web page (stock macOS python, http.server, no pip). Each duplicate group shows every competing field value with its label (home/work/cell), source account, and age ("edited yesterday", "added 3mo ago"). The user picks the winning value per field; losers become secondary labelled fields or are dropped if marked outdated. Search/filter for large books. Save records decisions; Apply performs merges/deletes. Without dates + labels, "Keep/Delete" is meaningless — dates and labels are mandatory.
5. Apply — dry-run by default. Nothing mutates without --live. Before any delete, export the removed cards to deleted-contacts-<ts>.vcf (drag-to-restore undo). Auto-merge only truly identical cards (same normalized phone/email set — no info lost).
6. Never sync back to any cloud account until the user names an explicit target and approves.

Starter code: scripts/contacts_cleanup.py is the proven single-source (macOS) merger with the full web review UI, backup, undo, and classification already working. Read it, then extend it to multi-source ingest + per-field winner selection. Do not rebuild from scratch.

Phase B — iMessage mining → dossiers (optional deep dive)

Full plan: references/imessage-dossier-plan.md.

1. Ask the user who's worth a deep dive. Before mining, offer to focus: high-value people, specific groups, or traits (e.g. "Utah legislators", "arts-org board members", "clients", "family"). Let them steer the shortlist — don't research everyone. The ranking informs the conversation; it does not authorize research. A dossier runs only for a person the user names.
2. Mine the message graph (read-only). Run scripts/imessage_signal.py — it copies chat.db to a temp file, opens it read-only, and ranks handles by volume and recency (plus a 1:1-vs-group signal). It reads counts and timestamps only; message content never leaves the machine. Reciprocity is an unreliable column — chat.db nulls the sender on the user's own messages, so rank on volume + recency, not reciprocity. Output: ranked people-signal.csv/json + gap lists ("high-frequency but not in contacts" / "in contacts but never messaged"). Full Disk Access required for the running user.
3. Resolve identities against the unified address book from Phase A (collapse phone+email splits).
4. Fan out dossiers. For the approved shortlist, invoke the research-person skill to build confidence-marked Obsidian People/ notes, enriched from public sources appropriate to the person's world — LinkedIn, and (Utah-centric example) the state legislature site (le.utah.gov), arts-group/nonprofit sites, and the lobbyist registry (disclosures.utah.gov). Private message content stays internal context, never published.

Safety rules (both phases)

• Dry-run by default; --live only after the user sees what would change.
• Back up before any mutation; every delete is reversible via exported .vcf.
• Read-only on chat.db (work on a copy); never write the live Messages or Contacts DB directly except through the reviewed, --live-gated apply step.
• No outbound sync and no message content in any shared vault without explicit approval.

Deliverables

• Working dir (e.g. ~/dev/agent-notes/me/contacts-merge/<person>/) with source snapshots, contacts-data.json, contacts-decisions.(csv|json), and the review HTML.
• Unify My Contacts.command launcher + a friend-readable README.md.
• (Phase B) people-signal.(csv|json) + research-person dossiers for the approved shortlist.