Add email-to-expense skill; expand log_expense.py to accept other/professional-development/software types; update skill index

email-to-expense/SKILL.md

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+---
+name: email-to-expense
+description: |
+  Process emails from a Gmail label into work expenses. Opens Gmail via browser automation,
+  iterates through emails in the "expense" label, saves each as PDF, extracts metadata,
+  and logs them to MongoDB via the log-work-expense skill.
+
+  Triggers when user mentions:
+  - "process my expense emails"
+  - "email to expense"
+  - "triage my expense label"
+  - "process expense label"
+---
+
+# Email to Expense
+
+Pulls emails from Gmail's "expense" label, saves them as PDFs, extracts metadata, and inserts them into `wip.work_expenses`.
+
+## Prerequisites
+
+- Browser tool available (`openclaw browser`)
+- User's Gmail must be logged in (use `profile="user"`)
+- `log-work-expense` skill scripts accessible at `~/notes/skills/log-work-expense/scripts/`
+
+## Flow
+
+### 1. Open Gmail with expense label
+
+Open Gmail filtered to the "expense" label using the user's browser profile:
+
+```
+URL: https://mail.google.com/mail/u/0/#label/expense
+```
+
+If the label name has spaces or special characters, URL-encode it. The label may also be accessed as "2 expense" — adjust accordingly.
+
+Take a snapshot to see how many emails are in the list. Note the email count.
+
+### 2. Iterate through each email
+
+For each email in the label (oldest first to maintain order):
+
+1. **Click the email** to open it
+2. **Snapshot the open email** to extract metadata:
+   - **Date** — from the email header (parse the displayed date string)
+   - **Sender** — from the "From" field
+   - **Subject** — from the subject line
+3. **Save as PDF** — Use the browser's print-to-PDF functionality:
+   - Use `action="pdf"` if available, or trigger `Ctrl+P` and save to a temp directory
+   - Save to `/tmp/email_expense_<index>.pdf`
+4. **Upload PDF to S2** — Use the S2 upload endpoint from TOOLS.md:
+   ```bash
+   curl -X POST https://api.connorrhodes.com/agent/s2_upload \
+     -H "x-api-key: LT6CXiLT5cEApfqtThz17bENr6OLp804FepOMqa1tZkfTGXiiCcSFlupl6gaYeX" \
+     -F "file=@/tmp/email_expense_<index>.pdf"
+   ```
+   Collect the returned S2 URL.
+5. **Return to the email list** — Go back to the label view
+
+### 3. Classify each expense
+
+After collecting all emails, classify each one:
+
+**Known merchants (auto-classify):**
+
+| Sender / Keyword | Type | Default Account |
+|-------------------|------|-----------------|
+| DoorDash | meal | _(ask user)_ |
+| Uber Eats | meal | _(ask user)_ |
+| Amazon | other | _(ask user)_ |
+
+For unknown senders, **ask the user** to classify:
+- Type: `meal`, `mileage`, `other`, `professional-development`, `software`, etc.
+- Account: the work account to bill against
+
+### 4. Insert into MongoDB
+
+For each classified email, run the existing log_expense.py script:
+
+```bash
+uv run --with pymongo ~/notes/skills/log-work-expense/scripts/log_expense.py \
+  <type> <account> <date> "<note>" <s2_url>
+```
+
+- **type**: meal, mileage, or other
+- **account**: from classification step
+- **date**: parsed from the email header (YYYY-MM-DD format)
+- **note**: include sender and subject for context, e.g. "DoorDash - order confirmation"
+- **s2_url**: the uploaded PDF URL
+
+### 5. Confirm
+
+Show a summary table of all processed expenses:
+- Sender | Date | Type | Account | Status
+
+### 6. Cleanup
+
+- Remove local PDF files from `/tmp/`
+- Optionally archive/remove the processed label from Gmail (ask user first)
+
+## Notes
+
+- Always ask the user to confirm before inserting expenses into the database
+- If an email doesn't look like an expense (no receipt, no order confirmation, etc.), skip it and note it
+- The Gmail label URL format: `https://mail.google.com/mail/u/0/#label/<labelname>`
+- For labels with spaces, the URL typically uses the raw label name