bedead / bedead/Eibox

Mail-triggered Notion Auto-Importer (Job / Payment / Rule-based Mail To Table

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agents enhancement fun hard hardcore medium
Dominant language
Python
Stars
1
Forks
0
PR merge metrics
No merged PRs in 30d

Description

Add a mail-triggered module to Eibox backend that detects certain incoming emails (e.g., job application confirmations, payment receipts), extracts structured fields, and inserts/updates rows in a user-configurable Notion table (or another target). Users must be able to define “rules” (which mails to watch), mapping of extracted fields → table columns, and default status/labels.

Goals / Acceptance Criteria
- [ ] 1. When a new email arrives and matches a user rule, the system:
- [ ] 2. extracts relevant fields (see examples below),
- [ ] 3. creates or updates a row in the user-selected Notion table (or chosen target),
- [ ] 4. sets initial metadata (status, source, timestamp) and
- [ ] 5. logs the operation (success/failure).
- [ ] 6. Users can configure multiple rules with priority order and test them against sample emails.
- [ ] 7. The extractor supports both template-based parsing (HTML/text + regex/DOM) and an LLM-assisted fallback for ad-hoc formats.
- [ ] 8. All operations respect user privacy — only authorized Notion workspaces are used, tokens stored encrypted, and user can opt-out per rule.
- [ ] 9. Provide audit trail and UI list where user can see rule run history and manually re-run extraction.
- [ ] 10. Ability to add new rules from app itself.

Contributor guide

No contributing guide indexed for this repository

Research direction

No files, tests, or entry points are named. Start by reading the Eibox backend architecture and existing email-processing and integration paths, then map the acceptance criteria to the current rule, storage, authentication, and UI capabilities. Done means a scoped design and implementation plan exists for ingestion, extraction, Notion updates, privacy, audit history, and rule management.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
ai, backend, security
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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