Sender (untrusted metadata):
```json
{
  "label": "gateway-client",
  "id": "gateway-client"
}
```

[Mon 2026-03-09 09:52 EDT] ping


pong


Sender (untrusted metadata):
```json
{
  "label": "gateway-client",
  "id": "gateway-client"
}
```

[Mon 2026-03-09 09:52 EDT] What are you working on?


Based on the kernel state, I'm the **LifeOS** session (Slot 1).

**Current status:** Phase 1 + 1.5 live — meal tracking and intent layer are operational.

**Next up:** Supplement module.

Want to pick up where we left off, or is there something specific you need?


Sender (untrusted metadata):
```json
{
  "label": "gateway-client",
  "id": "gateway-client"
}
```

[Mon 2026-03-09 09:53 EDT] test


[[reply_to_current]] Got it. ✅


Sender (untrusted metadata):
```json
{
  "label": "gateway-client",
  "id": "gateway-client"
}
```

[Mon 2026-03-09 09:56 EDT] [Slot 3 — Project: QBO Bookkeeping Catch-Up]

What project are you working on?


[[reply_to_current]] I'm working on the **QBO Bookkeeping Catch-Up** (Slot 3).

**Current status:** 383 rows posted ($80,648 total).

**Next up:** Amazon 2FA login via Camoufox → scrape 2025 orders → classify 116 Amazon rows (~$8,979). Then 17 medium rows ($3,506), then 320 NULL rows ($29,212).

Want to pick up on the Amazon step?


Sender (untrusted metadata):
```json
{
  "label": "gateway-client",
  "id": "gateway-client"
}
```

[Mon 2026-03-09 10:02 EDT] [Slot 3 — Project: QBO Bookkeeping Catch-Up]

How much context do you have left?


[[reply_to_current]] Let me check.


🦞 OpenClaw 2026.3.7 (42a1394)
🕒 Time: Monday, March 9th, 2026 — 10:02 AM (America/New_York)
🧠 Model: anthropic/claude-sonnet-4-6 · 🔑 api-key (anthropic)
🧮 Tokens: 3 in / 119 out
🗄 Cache: 0% hit · 0 cached, 27k new
📚 Context: 27k/150k (18%) · 🧹 Compactions: 0
🧵 Session: agent:markbot:mc:web:primary:slot:3:g3 • updated just now
⚙ Runtime: direct · Think: off · elevated
🪢 Queue: collect (depth 0)


**18% used** — 27k of 150k tokens. Plenty of runway. We're good to work for a while before needing to compact.

Ready to dive into the Amazon step whenever you are.


Sender (untrusted metadata):
```json
{
  "label": "gateway-client",
  "id": "gateway-client"
}
```

[Mon 2026-03-09 10:03 EDT] [Slot 3 — Project: QBO Bookkeeping Catch-Up]

Can you find the DB where we had alrady gotten all of our Amazon receipts going back to 2016. You have all of teh Amazon stuff. We did the 2FA already


Let me hunt for that DB.

exec

find /Users/bigdaddy/.openclaw-markbot/workspace -name "*.db" -o -name "*.sqlite" -o -name "*.sqlite3" 2>/dev/null | head -30


# Kasdorf Household Bookkeeping — Master Project State
**Last updated:** 2026-03-06 16:00 ET
**Read this file first at the start of every bookkeeping session.**
**Then read SKILL.md for all rules.**

---

## 🗺 THE BIG PICTURE

We are reconciling the Kasdorf Household QBO company (Realm: `193514692091119`) to be fully clean, categorized, classed, and reconciled through 12/31/2025. The end goal is an accurate P&L and Balance Sheet that can be handed to an accountant.

### The three main workstreams:
1. **CC Transaction Entry & Classification** — Getting all CC statement rows into QBO with correct categories/classes
2. **Property Tax Reconciliation** — 8 Bennett + 10 Bennett taxes properly amortized through Prepaid
3. **Bank Account Reconciliation** — Matching bank statements to QBO (Phase 2, not yet started)

---

## ✅ WHAT'S BEEN COMPLETED

- [x] Full DB import: all 2025 CC statements for all cards
- [x] Classifier run: all rows categorized/classed with confidence levels
- [x] Fixed Delta card `match_status` bug (222 rows corrected)
- [x] **Batch posted 383 high-confidence unmatched rows → QBO** ($80,648 total)
  - First pass: 322 posted, 61 errored (wrong account IDs)
  - Fixed account ID map with live QBO IDs
  - Retry: 61/61 posted, zero errors
- [x] Full audit log → `BATCH-POST-LOG.md`
- [x] JetBlue row 2561 ($218.49, Amex 1009, 2025-08-20): Confirmed BOS→ORD flight for Krista. Decision: class=K, category=Travel:Flights
- [x] **SKILL.md updated** with full verified Chart of Accounts (live pull 2026-03-06), correct card account IDs, token management section, audit section
- [x] **audit_categorization.py written** — full QBO categorization audit script, ready to run after all posting complete. Mark explicitly requested this 2026-03-06.
- [x] batch_post_high_confidence.py card account map corrected (Delta=440, Apple=234, CapOne=85, JetBlue=426)
- [x] All changes committed to git: `5c22df79`

---

## 🔄 IN PROGRESS / NEXT SESSION PICK UP HERE

### IMMEDIATE: Amazon 2FA Login
Mark just regained text access (2026-03-06 ~16:00 ET) and is ready to provide the Amazon 2FA SMS code (sent to phone ending in **94**).

**The plan:**
1. Read `skills/headless-browser/SKILL.md`
2. Launch headless browser session using Camoufox
3. Navigate to amazon.com, log in with Doppler creds (`AMAZON_EMAIL` / `AMAZON_PASSWORD`)
4. When 2FA prompt appears → ask Mark for the code immediately
5. Once logged in → scrape order history for 2025, match to the 116 low-confidence unmatched Amazon rows ($8,978.88)
6. Re-classify those rows using actual order item data
7. Post classified Amazon rows to QBO

**Prior Amazon scripts** (all failed at 2FA): `amazon_orders.py` through `amazon_orders4.py` (workspace/skills/qbo/)

### After Amazon login:
- [ ] Post 17 medium-confidence rows ($3,506.20) — need review first
- [ ] Post 320 unmatched rows with NULL confidence ($29,211.65) — need classification
- [ ] Review 27 proximity-matched Amazon rows already classifiable from receipt DB
- [ ] Run full categorization audit (`audit_categorization.py`) after all posting complete

---

## ⏸ BLOCKED / TABLED

| Item | Status | Blocker |
|------|--------|---------|
| Green Highland $1,146.56 (Apple Card, 2025-02-19, DB row 1931) | TABLED | Mark to check Apple Wallet for merchant detail |
| Southwest $376.72 (Amex 1009, 2025-07-11, DB row 2696) | TABLED | No Gmail match, no SW credentials |
| Southwest $149.00 (Amex 1009, 2025-07-24, DB row 2645) | TABLED | No Gmail match, no SW credentials |
| Property Tax JE blocks (A–G) | PAUSED | See PROPERTY-TAX-SESSION-STATE.md |

---

## 📊 CURRENT DB STATUS (as of 2026-03-06 16:00 ET)

| Status | Confidence | Rows | Amount |
|--------|-----------|------|--------|
| posted | high | 383 | $80,648 |
| exact match | various | 957+ | ~$290K+ |
| **unmatched** | **low** | **116** | **$8,979** — ALL AMAZON |
| **unmatched** | **medium** | **17** | **$3,506** |
| **unmatched** | NULL | **320** | **$29,212** |
| unmatched | KRISTA_PENDING | 1 | $35 |

**Key insight:** All 116 low-confidence unmatched rows are Amazon. If we can log in and scrape, we classify them and clear the entire low-confidence bucket.

---

## 🛠 KEY FILES & PATHS

| File | Purpose |
|------|---------|
| `SKILL.md` | Authoritative rulebook — read first, always |
| `MASTER-PROJECT-STATE.md` | This file — project continuity |
| `bookkeeping.db` | SQLite DB — all statement rows, Amazon receipts |
| `batch_post_high_confidence.py` | Batch posting script (DRY_RUN=False for live) |
| `audit_categorization.py` | Full QBO audit — run when posting complete |
| `BATCH-POST-LOG.md` | Log of all 383 posted rows |
| `BATCH-CONFIRMED-LOG.md` | Confirmed posting log (246 rows / $54,149.31 as of 11:07) |
| `AMAZON-AGENT-STATUS.md` | Amazon classification status |
| `PROPERTY-TAX-SESSION-STATE.md` | Property tax workstream state |
| `TAX-RECONCILIATION-MASTER.md` | All Wayland tax payments + JE blocks A–G |

---

## 🔑 CREDENTIALS & SECRETS

- **QBO Doppler**: project=`markbot_personal`, config=`dev`
- **QBO Realm**: `193514692091119`
- **QBO Token**: Read from `~/skills/qb-cli/config/tokens.json` — NEVER use Doppler token alone (stale)
- **Amazon**: `AMAZON_EMAIL` / `AMAZON_PASSWORD` in Doppler (markbot_personal/dev)
- **2FA phone**: ends in **94** (SMS)

---

## 💳 CARD → QBO ACCOUNT ID MAP (verified 2026-03-06)

| card_last4 | QBO Account ID | Account Name |
|-----------|---------------|--------------|
| 1009 | 84 | Amex Platinum -1009 |
| Delta | 440 | Delta SkyMiles (Khuthala) — note QBO typo "Dleta" |
| AC | 234 | Apple Card |
| 7461 | 85 | Capital One -7461 |
| 4927 | 426 | JetBlue *4927 |

---

## 🏷 KEY CLASS IDs

| Class | QBO ID |
|-------|--------|
| Bennett | 7000000000000738699 |
| 10Bennett | 7000000000001078300 |
| Me | 7000000000001053198 |
| K | 7000000000000777075 |
| KandM | 1000000001 |
| Forge | 7000000000001123473 |
| Dad | 7000000000001047150 |
| Family | 1000000004 |
| Kids | 7000000000000088970 |
| Sawyer | 7000000000001047155 |
| Cassandra | 7000000000000909306 |
| Marky | 7000000000001050225 |

---

## 📋 CLI COMMANDS

```bash
# QBO CLI
cd /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo
doppler run --project markbot_personal --config dev -- ~/skills/qb-cli/run.sh [command]

# Run Python scripts
cd /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo
doppler run --project markbot_personal --config dev -- .venv/bin/python3 script.py

# DB queries
sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "SELECT ..."
```

---

## 🔮 PHASE SEQUENCE (do not skip ahead)

1. **[IN PROGRESS]** CC entry & classification
   - Amazon login → scrape → classify 116 rows ← NEXT
   - Review + post 17 medium rows
   - Classify + post 320 NULL rows
   - Run full audit
2. **[NOT STARTED]** Bank reconciliation (310 rows staged in `bank_statement_rows`)
3. **[PAUSED]** Property tax JEs (Blocks A–G in TAX-RECONCILIATION-MASTER.md)
4. **[NOT STARTED]** Balance sheet cleanup
5. **[NOT STARTED]** Prepaids reconciliation
6. **[NOT STARTED]** Crypto

---

## 📌 KEY DECISIONS (permanent log)

| Date | Decision |
|------|---------|
| 2026-03-06 | JetBlue $218.49 (row 2561): class=K, category=Travel:Flights — trip was for Krista |
| 2026-03-06 | Green Highland $1,146.56: TABLED — Mark to check Apple Wallet |
| 2026-03-06 | Southwest Jul 11 + Jul 24: TABLED — no Gmail match |
| 2026-03-06 | High-confidence only for main batch (383 rows) |
| 2026-03-06 | Bank reconciliation = Phase 2 (after CC phase complete) |
| 2026-03-06 | Full QBO categorization audit requested by Mark — script ready, run after all posting |
| 2026-03-06 | Amazon low-confidence: use headless browser to log in and scrape order history |
| 2026-03-04 | Amazon 2FA blocks all prior scripts (amazon_orders.py through amazon_orders4.py) |


Found it. Let me check what Amazon data is actually in the DB:


amazon_orders_raw         deletion_queue            wayland_bills
amazon_receipts           pending_reclassification  wayland_bills_new
bank_statement_rows       prepaid_balances          wayland_parcels
cards                     prepaid_entries           wayland_payments
categories                prepaids                  wayland_portal_bills
classes                   qbo_transactions
deleted_transactions      statement_rows


sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
SELECT COUNT(*) as total_orders FROM amazon_orders_raw;
SELECT COUNT(*) as total_receipts FROM amazon_receipts;
SELECT MIN(order_date) as earliest, MAX(order_date) as latest FROM amazon_orders_raw;
"


1684
540
April 1, 2014|September 9, 2019


sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
SELECT MIN(order_date) as earliest, MAX(order_date) as latest, COUNT(*) as total FROM amazon_receipts;
PRAGMA table_info(amazon_orders_raw);
PRAGMA table_info(amazon_receipts);
"


2019-03-13|2026-03-01|540
0|id|INTEGER|0||1
1|order_id|TEXT|0||0
2|order_date|TEXT|0||0
3|order_year|INTEGER|0||0
4|total_amount|REAL|0||0
5|items_json|TEXT|0||0
6|shipping_address|TEXT|0||0
7|payment_method|TEXT|0||0
8|order_status|TEXT|0||0
9|order_url|TEXT|0||0
10|scraped_at|TEXT|0|datetime('now')|0
11|source|TEXT|0|'browser_scrape'|0
12|recipient|TEXT|0||0
0|id|INTEGER|0||1
1|order_id|TEXT|0||0
2|order_date|TEXT|0||0
3|ship_date|TEXT|0||0
4|total_amount|REAL|0||0
5|items_json|TEXT|0||0
6|recipient|TEXT|0||0
7|raw_email_subject|TEXT|0||0
8|raw_email_date|TEXT|0||0
9|email_source|TEXT|0||0
10|matched_statement_ids|TEXT|0||0
11|suggested_category|TEXT|0||0
12|suggested_class|TEXT|0||0
13|confidence|TEXT|0||0
14|notes|TEXT|0||0
15|created_at|TEXT|0|datetime('now')|0


sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
-- Coverage by year in amazon_orders_raw
SELECT order_year, COUNT(*) as orders FROM amazon_orders_raw GROUP BY order_year ORDER BY order_year;
"


2010|13
2011|42
2012|55
2013|55
2014|103
2015|96
2016|85
2017|86
2018|173
2019|208
2020|176
2021|117
2022|116
2023|96
2024|144
2025|92
2026|27


We're in good shape. Here's what we have:

**`bookkeeping.db`** — `/Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db`

- **`amazon_orders_raw`**: 1,684 orders going back to **2010** (scraped via browser)
- **`amazon_receipts`**: 540 receipts from email parsing (2019–2026)

**2025 coverage:** 92 orders in `amazon_orders_raw` + receipts in `amazon_receipts`.

The 2FA blocker is already cleared — you said you've done the 2FA already. So the next question is: **do we have good 2025 coverage already in the DB, or do we need to re-scrape to fill gaps?**

Let me check how many of the 116 unmatched Amazon CC rows we can already match from what's in the DB:

sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
-- How many of the 116 unmatched low-confidence rows are Amazon, and what years?
SELECT
  strftime('%Y', date) as year,
  COUNT(*) as rows,
  SUM(amount) as total
FROM statement_rows
WHERE match_status = 'unmatched'
  AND confidence = 'low'
  AND (description LIKE '%AMAZON%' OR description LIKE '%AMZ%' OR vendor LIKE '%Amazon%')
GROUP BY year
ORDER BY year;
"


Error: in prepare, no such column: date
   Amazon, and what years? SELECT    strftime('%Y', date) as year,   COUNT(*) as
                                      error here ---^

(Command exited with code 1)


sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "PRAGMA table_info(statement_rows);"


0|id|INTEGER|0||1
1|source_file|TEXT|0||0
2|card_issuer|TEXT|0||0
3|card_last4|TEXT|0||0
4|txn_date|TEXT|0||0
5|posted_date|TEXT|0||0
6|description|TEXT|0||0
7|amount|REAL|0||0
8|credit|REAL|0||0
9|category_raw|TEXT|0||0
10|matched_qbo_id|TEXT|0||0
11|match_status|TEXT|0||0
12|suggested_vendor|TEXT|0||0
13|suggested_category|TEXT|0||0
14|suggested_class|TEXT|0||0
15|notes|TEXT|0||0
16|imported_at|TEXT|0||0
17|confidence|TEXT|0|NULL|0
18|krista_pending|INTEGER|0|0|0
19|month_close_period|TEXT|0|NULL|0
20|email_evidence|INTEGER|0|0|0


sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
SELECT
  strftime('%Y', txn_date) as year,
  COUNT(*) as rows,
  ROUND(SUM(amount),2) as total
FROM statement_rows
WHERE match_status = 'unmatched'
  AND confidence = 'low'
  AND (description LIKE '%AMAZON%' OR description LIKE '%AMZ%' OR suggested_vendor LIKE '%Amazon%')
GROUP BY year
ORDER BY year;
"


sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
-- What does our 2025 amazon_orders_raw coverage look like month by month?
SELECT
  substr(order_date, 1, 7) as month,
  COUNT(*) as orders,
  ROUND(SUM(total_amount),2) as total
FROM amazon_orders_raw
WHERE order_year = 2025
GROUP BY month
ORDER BY month;
"


April 2|3|575.54
April 6|2|69.01
August |4|162.16
Decembe|19|1109.64
Februar|7|367.11
January|11|407.71
July 16|2|216.63
July 17|1|17.85
July 7,|1|121.6
June 16|1|28.68
June 17|1|34.53
June 20|1|47.73
June 22|1|45.99
June 28|1|38.14
June 5,|1|32.99
June 8,|2|163.57
March 1|2|39.61
March 2|2|70.13
March 3|3|95.49
March 6|1|27.61
March 7|1|7.65
March 9|1|20.03
May 1, |1|32.87
May 14,|1|36.06
May 18,|3|214.94
May 23,|1|8.48
May 7, |1|38.15
May 8, |1|21.2
Novembe|5|746.56
October|7|407.71
Septemb|4|132.16


The `order_date` format is messy (not ISO) — looks like it was scraped as display text. Let me check if we can actually match these to the 116 CC rows:

sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
-- Sample the 116 unmatched Amazon rows
SELECT id, txn_date, description, amount, suggested_category, suggested_class, confidence
FROM statement_rows
WHERE match_status = 'unmatched' AND confidence = 'low'
  AND (description LIKE '%AMAZON%' OR description LIKE '%AMZ%' OR suggested_vendor LIKE '%Amazon%')
ORDER BY txn_date
LIMIT 20;
"


3254|2025-02-20|AMAZON.COM          AMZN.COM/BILL       WA|12.72|Home Spending:Household Supplies||low
3255|2025-02-20|AMAZON.COM          AMZN.COM/BILL       WA|24.65|Home Spending:Household Supplies||low
3245|2025-02-24|AMAZON MARKEPLACE NA PA|25.79|Home Spending:Household Supplies||low
3229|2025-02-28|AMAZON.COM          AMZN.COM/BILL       WA|127.43|Home Spending:Household Supplies||low
3224|2025-03-01|AMAZON MARKEPLACE NA PA|15.88|Home Spending:Household Supplies||low
3215|2025-03-02|AMAZON MARKEPLACE NA PA|165.72|Home Spending:Household Supplies||low
3216|2025-03-02|AMAZON.COM          AMZN.COM/BILL       WA|637.47|Home Spending:Household Supplies||low
3194|2025-03-06|AMAZON MARKEPLACE NA PA|27.61|Home Spending:Household Supplies||low
3181|2025-03-09|AMAZON MARKEPLACE NA PA|20.03|Home Spending:Household Supplies||low
3154|2025-03-17|AMAZON MARKEPLACE NA PA|23.73|Home Spending:Household Supplies||low
3143|2025-03-21|AMAZON.COM          AMZN.COM/BILL       WA|11.58|Home Spending:Household Supplies||low
3118|2025-03-28|AMAZON MARKEPLACE NA PA|25.18|Home Spending:Household Supplies||low
3119|2025-03-28|AMAZON MARKEPLACE NA PA|44.95|Home Spending:Household Supplies||low
3112|2025-03-30|AMAZON MARKEPLACE NA PA|24.37|Home Spending:Household Supplies||low
3113|2025-03-30|AMAZON MARKEPLACE NA PA|60.51|Home Spending:Household Supplies||low
3108|2025-04-01|AMAZON MARKEPLACE NA PA|10.61|Home Spending:Household Supplies||low
3097|2025-04-06|AMAZON MARKEPLACE NA PA|52.02|Home Spending:Household Supplies||low
3095|2025-04-07|AMAZON MARKEPLACE NA PA|16.99|Home Spending:Household Supplies||low
3041|2025-04-19|AMAZON.COM          AMZN.COM/BILL       WA|24.65|Home Spending:Household Supplies||low
3037|2025-04-20|AMAZON.COM          AMZN.COM/BILL       WA|20.12|Home Spending:Household Supplies||low


Good news — **we already have the Amazon data in the DB** (92 orders for 2025 in `amazon_orders_raw`). The issue is the `order_date` format is messy text ("April 2", "Februar", etc.) instead of ISO dates, which makes amount-based matching tricky.

Here's the situation:
- **116 unmatched CC rows** — all 2025 Amazon, $8,979
- **92 orders in `amazon_orders_raw` for 2025** — scraped already, but dates are display-text format
- **540 receipts in `amazon_receipts`** — email-parsed, these have proper ISO dates (2019–2026)

**The plan:** We don't need to re-scrape Amazon. We need to write a matching script that:
1. Fuzzy-matches the 116 CC rows to `amazon_orders_raw` (by amount + approximate date)
2. Pulls item descriptions from `items_json` to assign proper categories/classes
3. Updates `statement_rows` with category, class, and bumps confidence to `high`
4. Then we can batch post them to QBO

Want me to build that matching + classification script now?


Sender (untrusted metadata):
```json
{
  "label": "gateway-client",
  "id": "gateway-client"
}
```

[Mon 2026-03-09 10:05 EDT] [Slot 3 — Project: QBO Bookkeeping Catch-Up]

Yes. For orders under $100, review the order and use your best guess to categorize. Ask me if you have any questions even under $100.,

For orders over $100, we shoudl actually split the transactions and categorize by item.

Take a first crack and let me know how high your confdience is and bring any to me that you find confusing.


Let me get the full picture first — look at all the data we have to work with.

sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
-- Sample amazon_orders_raw items_json to understand structure
SELECT order_id, order_date, total_amount, items_json, recipient
FROM amazon_orders_raw
WHERE order_year = 2025
ORDER BY order_date
LIMIT 10;
"


112-1282166-4654622|April 24, 2025|10.61|["Rollo Direct Thermal 2x1 Barcode Labels - Roll of 1,000 Thermal Labels (Commercial Grade)"]|Mark Kasdorf
112-9220528-7494651|April 27, 2025|387.92|["Summit AccuCold FS24L 19\" Upright Freezer with 1.4 cu. ft. Capacity, Factory Installed Lock, Manual Defrost, Removable Shelf Removable Shelf and Adjustable Thermostat, in White"]|Mark Kasdorf
112-0585366-3241027|April 27, 2025|177.01|["EISCO Beaker, 50ml - Tall Form with Spout - White, 5ml Graduations - Borosilicate 3.3 Glass", "Vitamatic Magnesium Glycinate 500mg per Tablet - 180 Vegetarian Tablets (Uncoated) - Added B6 for Maximum Absorption - Supports Muscle, Joint, and Heart Health* 2 Packs", "Amazon Basics 48-Pack AA Alkaline High-Performance Batteries, 1.5 Volt, 10-Year Shelf Life", "Maldon Salt, Sea Salt Flakes, 3.1 lb, Bulk Tub, Kosher, Natural, Handcrafted, Gourmet, Pyramid Crystals", "Amazon Basics AAA Alkaline High-Performance Batteries, 1.5 Volt, 10-Year Shelf Life, 36 Count (Pack of 1)"]|Mark Kasdorf
112-3663758-9321813|April 6, 2025|16.99|["MR.SIGA Microfiber Cleaning Cloth, All-Purpose Cleaning Towels, Pack of 6, Size 13.8 x 15.7 in"]|Mark Kasdorf
112-3258454-3882662|April 6, 2025|52.02|["Skura Style Smart Kitchen Dish Sponges (Bulk 8 Pack) | Odor Free Non-Scratch Scrub Pad | Household Cleaning Scrubber for Sink, Counters, Surfaces, Pots, Pans, Dishwashing | Color-Fade Change Indicator", "Scrub Daddy 6 Kitchen Sponges & 1 Daddy Caddy Dish Sponge Holder - BPA Free & Made with Polymer Foam - Stain & Odor Resistant, Scratch-Free Multipurpose Scrubbers"]|Mark Kasdorf
111-1822973-5601825|August 17, 2025|20.03|["Organic Lions Mane Mushroom Capsules (120 Count), for Brain Support and Immune Health, Third Party Tested and Grown in The USA - by Double Wood"]|Mark Kasdorf
113-3233030-3246613|August 19, 2025|15.93|["EUSOAR 1-4 Cup Disposable Coffee Filters, 100 count Filter Replacement, Natural Brown Unbleached, fit small Basket Style Single Serve Coffee Makers, for Home Office Use Pour Over and Drip Coffee Maker"]|Mark Kasdorf
113-2623675-6820224|August 26, 2025|62.45|["Orgain Hydrolyzed Collagen Peptides Powder For Women & Men, 20g Grass Fed Collagen, Unflavored - Hair, Skin, Nail, & Joint Support Supplement, Paleo & Keto, Non-GMO, Type I and III, 1lb", "Nutricost Calcium with Vitamin D, 240 Tablets - Calcium (1200mg) Vitamin D (50mcg) Per Serving - Non-GMO, Gluten Free", "Nutricost Acetyl L-Carnitine (ALCAR) 500mg, 180 Capsules, 180 Servings - Non-GMO and Gluten Free, 1 Capsule per Serving"]|Mark Kasdorf
113-0294871-3128205|August 26, 2025|63.75|["Dr. Squatch Natural Bar Soap for Men Variety Gift Set - Cold Process No Harsh Chemicals - Birchwood Breeze, Fresh Falls, Wood Barrel Bourbon, Coconut Castaway, Cedar Citrus & more (5 oz, 10-Pack)"]|Mark Kasdorf
111-6431953-8188239|December 14, 2025|53.14|["Heilala Premium Vanilla Bean Paste, 13.52 oz | Bulk Size for Baking | Gourmet Bourbon Variety with Real Specks | Chef\u2019s Choice | Ethically Sourced | Certified B Corp"]|Mark Kasdorf


sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
-- Get ALL 116 unmatched Amazon CC rows
SELECT id, txn_date, amount, description, card_last4
FROM statement_rows
WHERE match_status = 'unmatched' AND confidence = 'low'
  AND (description LIKE '%AMAZON%' OR description LIKE '%AMZ%' OR suggested_vendor LIKE '%Amazon%')
ORDER BY txn_date;
" 2>&1


3254|2025-02-20|12.72|AMAZON.COM          AMZN.COM/BILL       WA|1009
3255|2025-02-20|24.65|AMAZON.COM          AMZN.COM/BILL       WA|1009
3245|2025-02-24|25.79|AMAZON MARKEPLACE NA PA|1009
3229|2025-02-28|127.43|AMAZON.COM          AMZN.COM/BILL       WA|1009
3224|2025-03-01|15.88|AMAZON MARKEPLACE NA PA|1009
3215|2025-03-02|165.72|AMAZON MARKEPLACE NA PA|1009
3216|2025-03-02|637.47|AMAZON.COM          AMZN.COM/BILL       WA|1009
3194|2025-03-06|27.61|AMAZON MARKEPLACE NA PA|1009
3181|2025-03-09|20.03|AMAZON MARKEPLACE NA PA|1009
3154|2025-03-17|23.73|AMAZON MARKEPLACE NA PA|1009
3143|2025-03-21|11.58|AMAZON.COM          AMZN.COM/BILL       WA|1009
3118|2025-03-28|25.18|AMAZON MARKEPLACE NA PA|1009
3119|2025-03-28|44.95|AMAZON MARKEPLACE NA PA|1009
3112|2025-03-30|24.37|AMAZON MARKEPLACE NA PA|1009
3113|2025-03-30|60.51|AMAZON MARKEPLACE NA PA|1009
3108|2025-04-01|10.61|AMAZON MARKEPLACE NA PA|1009
3097|2025-04-06|52.02|AMAZON MARKEPLACE NA PA|1009
3095|2025-04-07|16.99|AMAZON MARKEPLACE NA PA|1009
3041|2025-04-19|24.65|AMAZON.COM          AMZN.COM/BILL       WA|1009
3037|2025-04-20|20.12|AMAZON.COM          AMZN.COM/BILL       WA|1009
2999|2025-04-24|10.61|AMAZON MARKEPLACE NA PA|1009
2990|2025-04-27|177.01|AMAZON MARKEPLACE NA PA|1009
2991|2025-04-27|387.92|AMAZON MARKEPLACE NA PA|1009
2962|2025-05-07|38.15|AMAZON MARKEPLACE NA PA|1009
2954|2025-05-08|21.2|AMAZON MARKEPLACE NA PA|1009
2930|2025-05-14|36.06|AMAZON MARKEPLACE NA PA|1009
2915|2025-05-18|44.61|AMAZON MARKEPLACE NA PA|1009
2859|2025-06-02|133.85|AMZ*THE HOUSE OF STAPAY.AMAZON.CO       WA|1009
2848|2025-06-04|9.17|AMAZON MARKEPLACE NA PA|1009
2850|2025-06-04|94.01|AMAZON MARKEPLACE NA PA|1009
2845|2025-06-05|32.99|AMAZON MARKEPLACE NA PA|1009
2829|2025-06-08|49.91|AMAZON MARKEPLACE NA PA|1009
2830|2025-06-08|113.66|AMAZON MARKEPLACE NA PA|1009
2783|2025-06-20|2.56|AMAZON.COM          AMZN.COM/BILL       WA|1009
2784|2025-06-20|3.33|AMAZON.COM          AMZN.COM/BILL       WA|1009
2785|2025-06-20|24.65|AMAZON.COM          AMZN.COM/BILL       WA|1009
2746|2025-06-28|28.68|AMAZON MARKEPLACE NA PA|1009
2707|2025-07-07|121.6|AMAZON MARKEPLACE NA PA|1009
2670|2025-07-16|78.09|AMAZON MARKEPLACE NA PA|1009
2671|2025-07-16|138.54|AMAZON MARKEPLACE NA PA|1009
2563|2025-08-19|24.65|AMAZON.COM          AMZN.COM/BILL       WA|1009
2540|2025-08-23|89.02|AMAZON.COM          AMZN.COM/BILL       WA|1009
2532|2025-08-24|31.82|AMAZON MARKEPLACE NA PA|1009
2533|2025-08-24|74.36|AMAZON.COM          AMZN.COM/BILL       WA|1009
2534|2025-08-24|128.77|AMAZON.COM*X85VS5QH3AMZN.COM/BILL       WA|1009
2527|2025-08-25|135.99|AMAZON MARKEPLACE NA PA|1009
2519|2025-08-26|62.45|AMAZON MARKEPLACE NA PA|1009
2520|2025-08-26|63.75|AMAZON MARKEPLACE NA PA|1009
2522|2025-08-26|101.16|AMAZON.COM*5J6TG2WL3AMZN.COM/BILL       WA|1009
2514|2025-08-28|79.87|AMAZON.COM*HJ1FX4YV3AMZN.COM/BILL       WA|1009
2503|2025-08-30|68.53|AMAZON.COM*HK8CM33W3AMZN.COM/BILL       WA|1009
2505|2025-08-30|129.37|AMAZON.COM*V10VM7WN3AMZN.COM/BILL       WA|1009
2500|2025-08-31|20.03|AMAZON MARKEPLACE NA PA|1009
2479|2025-09-06|127.12|AMAZON.COM*SM8M43PD3AMZN.COM/BILL       WA|1009
2431|2025-09-17|2.31|AMAZON.COM          AMZN.COM/BILL       WA|1009
2432|2025-09-17|3.42|AMAZON.COM          AMZN.COM/BILL       WA|1009
2433|2025-09-17|39.47|AMAZON.COM          AMZN.COM/BILL       WA|1009
2425|2025-09-19|15.93|AMAZON MARKEPLACE NA PA|1009
2426|2025-09-19|427.52|AMAZON.COM          AMZN.COM/BILL       WA|1009
2422|2025-09-20|31.86|AMAZON MARKEPLACE NA PA|1009
2423|2025-09-20|246.71|AMAZON MARKEPLACE NA PA|1009
2409|2025-09-23|19.64|AMAZON.COM          AMZN.COM/BILL       WA|1009
2410|2025-09-23|32.89|AMAZON.COM          AMZN.COM/BILL       WA|1009
2385|2025-09-28|28.68|AMAZON MARKEPLACE NA PA|1009
2386|2025-09-28|71.36|AMAZON MARKEPLACE NA PA|1009
2387|2025-09-28|146.4|AMAZON.COM*NJ4Q581A1AMZN.COM/BILL       WA|1009
2340|2025-10-11|33.99|AMAZON MARKEPLACE NA PA|1009
2341|2025-10-11|42.49|AMAZON MARKEPLACE NA PA|1009
2342|2025-10-11|223.07|AMAZON MARKEPLACE NA PA|1009
2343|2025-10-11|41.43|AMAZON.COM          AMZN.COM/BILL       WA|1009
2334|2025-10-12|20.07|AMAZON MARKEPLACE NA PA|1009
2335|2025-10-12|119.95|AMAZON MARKEPLACE NA PA|1009
2296|2025-10-22|53.11|AMAZON MARKEPLACE NA PA|1009
2272|2025-10-29|20.03|AMAZON MARKEPLACE NA PA|1009
2273|2025-10-29|22.93|AMAZON MARKEPLACE NA PA|1009
2230|2025-11-09|30.79|AMAZON MARKEPLACE NA PA|1009
2231|2025-11-09|106.24|AMAZON MARKEPLACE NA PA|1009
2232|2025-11-09|6.19|AMAZON.COM          AMZN.COM/BILL       WA|1009
2233|2025-11-09|10.98|AMAZON.COM          AMZN.COM/BILL       WA|1009
2234|2025-11-09|13.06|AMAZON.COM          AMZN.COM/BILL       WA|1009
2235|2025-11-09|35.95|AMAZON.COM          AMZN.COM/BILL       WA|1009
2217|2025-11-12|457.82|AMAZON MARKEPLACE NA PA|1009
2209|2025-11-14|96.26|AMAZON MARKEPLACE NA PA|1009
2203|2025-11-15|91.98|AMAZON MARKEPLACE NA PA|1009
2193|2025-11-16|27.76|AMAZON.COM          AMZN.COM/BILL       WA|1009
2194|2025-11-16|514.24|AMAZON.COM          AMZN.COM/BILL       WA|1009
2195|2025-11-16|119.82|AMAZON.COM*B896O4QA2AMZN.COM/BILL       WA|1009
2186|2025-11-17|318.7|AMAZON MARKEPLACE NA PA|1009
2188|2025-11-17|29.31|AMAZON.COM          AMZN.COM/BILL       WA|1009
2182|2025-11-18|22.99|AMAZON MARKEPLACE NA PA|1009
2183|2025-11-18|27.99|AMAZON MARKEPLACE NA PA|1009
2175|2025-11-19|35.32|AMAZON MARKEPLACE NA PA|1009
2079|2025-12-11|183.93|AMAZON MARKEPLACE NA PA|1009
2072|2025-12-12|63.07|AMAZON MARKEPLACE NA PA|1009
2035|2025-12-17|23.36|AMAZON MARKEPLACE NA PA|1009
2036|2025-12-17|2.06|AMAZON.COM          AMZN.COM/BILL       WA|1009
2037|2025-12-17|3.06|AMAZON.COM          AMZN.COM/BILL       WA|1009
2041|2025-12-17|50.0|AMAZON.COM          AMZN.COM/BILL       WA|1009
2011|2025-12-21|7.99|AMAZON MARKEPLACE NA PA|1009
2015|2025-12-21|80.99|AMAZON MARKEPLACE NA PA|1009
2016|2025-12-21|142.26|AMAZON.COM*CR4T27GT3AMZN.COM/BILL       WA|1009
2005|2025-12-22|17.73|AMAZON MARKEPLACE NA PA|1009
2006|2025-12-22|26.51|AMAZON MARKEPLACE NA PA|1009
1999|2025-12-23|9.94|AMAZON MARKEPLACE NA PA|1009
2000|2025-12-23|15.99|AMAZON MARKEPLACE NA PA|1009
1993|2025-12-24|15.99|AMAZON MARKEPLACE NA PA|1009
1990|2025-12-25|100.22|AMAZON.COM*EJ6JK4443AMZN.COM/BILL       WA|1009
1986|2025-12-26|46.06|AMAZON.COM          AMZN.COM/BILL       WA|1009
1976|2025-12-27|34.72|AMAZON.COM          AMZN.COM/BILL       WA|1009
1965|2025-12-28|9.55|AMAZON MARKEPLACE NA PA|1009
1966|2025-12-28|13.6|AMAZON MARKEPLACE NA PA|1009
1967|2025-12-28|14.86|AMAZON MARKEPLACE NA PA|1009
1968|2025-12-28|11.68|AMAZON.COM          AMZN.COM/BILL       WA|1009
1969|2025-12-28|25.49|AMAZON.COM          AMZN.COM/BILL       WA|1009
1970|2025-12-28|56.46|AMAZON.COM          AMZN.COM/BILL       WA|1009
1971|2025-12-28|379.2|AMAZON.COM          AMZN.COM/BILL       WA|1009


Good data. Now let me get all the 2025 Amazon orders with their items so I can do the matching:

sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
SELECT order_id, order_date, total_amount, items_json, recipient
FROM amazon_orders_raw
WHERE order_year = 2025
ORDER BY order_date;
" 2>&1


112-1282166-4654622|April 24, 2025|10.61|["Rollo Direct Thermal 2x1 Barcode Labels - Roll of 1,000 Thermal Labels (Commercial Grade)"]|Mark Kasdorf
112-9220528-7494651|April 27, 2025|387.92|["Summit AccuCold FS24L 19\" Upright Freezer with 1.4 cu. ft. Capacity, Factory Installed Lock, Manual Defrost, Removable Shelf Removable Shelf and Adjustable Thermostat, in White"]|Mark Kasdorf
112-0585366-3241027|April 27, 2025|177.01|["EISCO Beaker, 50ml - Tall Form with Spout - White, 5ml Graduations - Borosilicate 3.3 Glass", "Vitamatic Magnesium Glycinate 500mg per Tablet - 180 Vegetarian Tablets (Uncoated) - Added B6 for Maximum Absorption - Supports Muscle, Joint, and Heart Health* 2 Packs", "Amazon Basics 48-Pack AA Alkaline High-Performance Batteries, 1.5 Volt, 10-Year Shelf Life", "Maldon Salt, Sea Salt Flakes, 3.1 lb, Bulk Tub, Kosher, Natural, Handcrafted, Gourmet, Pyramid Crystals", "Amazon Basics AAA Alkaline High-Performance Batteries, 1.5 Volt, 10-Year Shelf Life, 36 Count (Pack of 1)"]|Mark Kasdorf
112-3663758-9321813|April 6, 2025|16.99|["MR.SIGA Microfiber Cleaning Cloth, All-Purpose Cleaning Towels, Pack of 6, Size 13.8 x 15.7 in"]|Mark Kasdorf
112-3258454-3882662|April 6, 2025|52.02|["Skura Style Smart Kitchen Dish Sponges (Bulk 8 Pack) | Odor Free Non-Scratch Scrub Pad | Household Cleaning Scrubber for Sink, Counters, Surfaces, Pots, Pans, Dishwashing | Color-Fade Change Indicator", "Scrub Daddy 6 Kitchen Sponges & 1 Daddy Caddy Dish Sponge Holder - BPA Free & Made with Polymer Foam - Stain & Odor Resistant, Scratch-Free Multipurpose Scrubbers"]|Mark Kasdorf
111-1822973-5601825|August 17, 2025|20.03|["Organic Lions Mane Mushroom Capsules (120 Count), for Brain Support and Immune Health, Third Party Tested and Grown in The USA - by Double Wood"]|Mark Kasdorf
113-3233030-3246613|August 19, 2025|15.93|["EUSOAR 1-4 Cup Disposable Coffee Filters, 100 count Filter Replacement, Natural Brown Unbleached, fit small Basket Style Single Serve Coffee Makers, for Home Office Use Pour Over and Drip Coffee Maker"]|Mark Kasdorf
113-2623675-6820224|August 26, 2025|62.45|["Orgain Hydrolyzed Collagen Peptides Powder For Women & Men, 20g Grass Fed Collagen, Unflavored - Hair, Skin, Nail, & Joint Support Supplement, Paleo & Keto, Non-GMO, Type I and III, 1lb", "Nutricost Calcium with Vitamin D, 240 Tablets - Calcium (1200mg) Vitamin D (50mcg) Per Serving - Non-GMO, Gluten Free", "Nutricost Acetyl L-Carnitine (ALCAR) 500mg, 180 Capsules, 180 Servings - Non-GMO and Gluten Free, 1 Capsule per Serving"]|Mark Kasdorf
113-0294871-3128205|August 26, 2025|63.75|["Dr. Squatch Natural Bar Soap for Men Variety Gift Set - Cold Process No Harsh Chemicals - Birchwood Breeze, Fresh Falls, Wood Barrel Bourbon, Coconut Castaway, Cedar Citrus & more (5 oz, 10-Pack)"]|Mark Kasdorf
111-6431953-8188239|December 14, 2025|53.14|["Heilala Premium Vanilla Bean Paste, 13.52 oz | Bulk Size for Baking | Gourmet Bourbon Variety with Real Specks | Chef\u2019s Choice | Ethically Sourced | Certified B Corp"]|Mark Kasdorf
111-5538934-7003401|December 14, 2025|275.19|["Sega Toys Homestar Flux (Satin Black) Home Planetarium Star Projector"]|Mark Kasdorf
112-7374981-7114601|December 15, 2025|55.48|["wet n wild MegaSlicks Lip Gloss, Ultra-Glossy, Vitamin-E Enriched, Ultra-Gloss High Shine Moisturizing, Cruelty-Free & Vegan - Crystal Clear", "Ritach Initial Bracelets for Girls - Gold Bracelets for Women Letter K Initial Bracelets for Girls Gifts for 7 8 9 10 11 12 13 14 Year Old Girl Birthday Gifts for Teen Girl Gift Trendy Stuff", "Hoojoot 4PCS Fashion non-slip Headband for Women,Women's Hairband with Teeth,Woman's elastic headbands,Hair Accessories With polka dot Headband for Girls Daily Dating Decorations", "e.l.f. Cream Glide Lip Liner, Highly-Pigmented Pencil For Shaping & Sculpting Lips, Semi-Matte Finish, Vegan & Cruelty-Free, Truth or Bare", "Anteel 2 Pack Silicone Suction Phone Case Mount, Non Slip Sticky Grip for Cellphone, Silicon Adhesive Suction Cup Mount, Strong Grip Holder for Selfies and Videos (LightPink&White)"]|Cassandra Kasdorf
112-5094021-3219431|December 16, 2025|37.7|["MANVEN Travel, Travel Gifts for Women, Travel Essentials Accessories Jewelry Stuff for Women Men, Jewelry Gifts for People Who Travel A Lot Travelers Turtle", "100 Vintage Football Cards in Old Sealed Wax Packs - Perfect for New Collectors"]|Mark Kasdorf
112-7302932-6116269|December 17, 2025|23.36|["ENJBRICK Typewriter Building Set Compatible with Lego for Adults,Ideas Cute Building Toys for Girls 8-14 Years,Christmas Toys for Kids"]|Mark Kasdorf
112-9694974-4762613|December 17, 2025|50.0|["AMC Theatres Gift Card $50"]|Mark Kasdorf
111-4756929-2372219|December 17, 2025|0.0|["SOLARAY Tongkat Ali 400 mg - Longjack Tongkat Ali for Men - Herbal Support for Men's Health and Vitality - Vegan, Non-GMO, Lab Verified - 180 Servings, 180 VegCaps"]|
112-4865117-5741838|December 17, 2025|0.0|["Organic Lions Mane Mushroom Capsules (120 Count), for Focus Support and Immune Health, Third Party Tested and Grown in The USA - by Double Wood"]|
113-7840065-2222626|December 18, 2025|15.51|["NFL Buffalo Bills Dog T-Shirt for Pets, Size Small. Officially Licensed Pet Shirt with Team Logo, Soft Stretchable Comfort Fit Apparel for Dogs & Cats"]|Mark Kasdorf
113-1913520-9763430|December 19, 2025|80.99|["Squishmallows kellytoy Veggie Squad Plush Doll Toy (8\" Claudia The Beet)", "HUES and CUES - Vibrant Color Guessing Board Game for 3-10 Players Ages 8+, Connect Clues and Guess from 480 Color Squares", "Mermaker Burrito Tortilla Taco Blanket 71 inches for Adult Kids, Realistic Tortilla Throw Blanket Funny Gifts for Women Men Teen Boy Girl, Soft Flannel Food Blanket for Christmas Birthday Gifts-Yellow", "maamgic Womens Feel Scarf Large Shawls and Wraps Light Blanket Scarf for Evening Dress Warm Daily Travel Office Light Red and Scarlet"]|Mark Kasdorf
113-3172274-8674630|December 20, 2025|16.42|["Hello, My Name Is Ice Cream: The Art and Science of the Scoop: A Cookbook"]|Mark Kasdorf
113-3551803-4859455|December 20, 2025|18.05|["Electronic Whistle Rechargeable for Referees Coaches Teachers Electric Whistle Handheld Whistles with USB Charging for Outdoor Sports Games Emergency Whistle with Lanyard 4 Tones High Volume, Blue"]|Mark Kasdorf
111-6634451-9706669|December 23, 2025|15.99|["thorlos mens Xj Max Cushion Crew Running Sock, White/Platinum (1 Pair), Small US"]|Mark Kasdorf
111-2891045-2254612|December 23, 2025|15.99|["thorlos mens Xj Max Cushion Crew Running Sock, White/Platinum (1 Pair), Small US"]|Mark Kasdorf
111-0991643-8781850|December 23, 2025|0.0|["thorlos mens Xj Max Cushion Crew Running Sock, White/Platinum (1 Pair), Small US"]|
111-1641936-5898602|December 23, 2025|9.94|["Awerise Personalized Wooden Cheese Spreader Custom Butter Spreader Butter Knife Jelly Spreader (5, With Engraving)"]|Mark Kasdorf
113-7678926-2357056|December 23, 2025|14.86|["Football Can & Bottle Holder Insulator Beverage Huggie Cooler (Buffalo Blue (Bills) - Collapsible Can 2-Pack)"]|Mark Kasdorf
112-1948722-2636201|December 28, 2025|379.2|["CHAUVET DJ HURRICANE HAZE 1DX (HHAZE (HHAZE 1DX)", "Nintendo Neon Purple/Neon Orange Joy-Con (L-R) - Switch", "Chauvet DJ High Performance Haze Fluid (HFG)"]|Mark Kasdorf
113-2723164-8517844|December 3, 2025|47.82|["blue bottle coffee filters 30 count"]|Mark Kasdorf
111-8723495-4164216|February 10, 2025|94.26|["O'Keeffe's Working Hands Hand Cream; Relieves and Repairs Extremely Dry Hands; 3 oz Tube; (Pack of 2)", "Nutricost L-Tyrosine 500mg, 180 Capsules - Gluten Free & Non-GMO", "Nutricost 5-HTP 100mg, 240 Vegetarian Capsules (5-Hydroxytryptophan) - Non-GMO & Gluten Free", "USB C to Micro USB Cable 2 FT (3-Pack), Flexible Micro USB to USB-C Cord, Supports Fast Charging & Data Sync, Compatible with MacBook Pro, Galaxy S7 S6, Kindle, Pixel 3 XL, 2 XL and Micro USB Devices"]|Mark Kasdorf
114-4843060-6066651|February 19, 2025|12.72|["Vicks VapoInhaler, On-the-Go Portable Nasal Inhaler, Non-Medicated, With Refreshing Vicks Vapors, Menthol Scent , 2 Scented Sticks"]|Mark Kasdorf
112-1395077-0369065|February 22, 2025|25.14|["Nature Made Super B Energy Complex, Dietary Supplement for Brain Cell Function Support, 160 Softgels, 160 Day Supply"]|Mark Kasdorf
113-8914786-5735414|February 22, 2025|127.43|["Filtrete 20x25x4 AC Furnace Air Filter, MPR 1550, MERV 12, Fits Lennox & Honeywell, Allergen, Bacteria & Virus, Electrostatic Air Cleaning Filter, 2-Pack (actual size 19.88 x 24.63 x 4.31)"]|Mark Kasdorf
111-2374100-1261809|February 24, 2025|25.79|["Henkelion Cat, Dog Carrier for Small Medium Cats Puppies up to 15 Lbs, TSA Airline Approved Carrier Soft Sided, Collapsible Travel Puppy Carrier - Grey"]|Gram
114-5661562-4114645|February 26, 2025|72.22|["Replacement Receiver for Logitech G903"]|Mark Kasdorf
111-0705454-4722607|February 28, 2025|9.55|["SLLEA AC Adapter Replacement for Taylor TEADPT3 TE10R Precision Digital Scale Balance Power Cord"]|Mark Kasdorf
112-9485656-2918603|January 2, 2025|73.11|["Icelandic-English/English-Icelandic Practical Dictionary", "Colloquial Icelandic: The Complete Course for Beginners (Colloquial Series (Book Only))"]|Angela
111-2737215-4847425|January 3, 2025|31.82|["Egooz Large Smell Proof Stash Bags with 11 items - Waterproof Smell Proof Containers Case for Travel and Home, Kitchen Smell Proof Box for Storing Your Products, Grey"]|Mark Kasdorf
111-6306753-9817822|January 3, 2025|13.9|["Pilot, Precise V5 RT Refillable & Retractable Rolling Ball Pens, Extra Fine Point 0.5 mm, Black, Pack of 6"]|Mark Kasdorf
111-5732302-5801846|January 5, 2025|31.88|["Dr. Squatch Fresh Falls Shampoo + Conditioner Hair Bundle"]|Mark Kasdorf
111-0120726-6785054|January 5, 2025|37.19|["Dr. Squatch Men's Bar Soap - 5 Pack Pine Tar - All Natural Bar Soap for Men with Heavy Grit - 5 Bars of Soap and a Collectible Magnet"]|Mark Kasdorf
111-5842762-3777803|January 5, 2025|60.5|["Golden Maple Detail Paint Brushes Set 10pcs Miniature Brushes for Fine Detailing & Art Painting - Acrylic, Watercolor, Oil, Models, 40k", "Games Workshop Warhammer 40k - Necron Warriors, Black"]|Mark Kasdorf
111-1856444-2696261|January 6, 2025|42.0|["PIHKAL: A Chemical Love Story", "TIHKAL: The Continuation"]|Mark Kasdorf
111-5739579-3070617|January 6, 2025|37.19|["The Art of Shaving Sandalwood Shaving Cream for Men \u2013 Protects Against Irritation and Razor Burn \u2013 Hydrates and Nourishes Dry Skin \u2013 Clinically Tested for Sensitive Skin \u2013 5 oz"]|Mark Kasdorf
111-1946127-2907411|January 6, 2025|12.74|["Harry's Shaving Cream - Shaving Cream for Men with Eucalyptus - 2 pack (6 oz)"]|Mark Kasdorf
111-2781241-9006608|January 6, 2025|39.3|["viewcare 3-Tier Long Shoe Rack, Bamboo Wood Shoe Rack for Closet, Entryway & Hallway, Stackable | Beautiful | Functional | Sturdy"]|Mark Kasdorf
111-2026820-3073841|January 7, 2025|28.08|["Dixie To Go Medium Paper Coffee Cups, 12 Oz, 132 Count, Disposable Cups For On-The-Go Hot Beverages"]|Mark Kasdorf
113-7438297-7545823|July 16, 2025|78.09|["BSN Micronized Creatine Monohydrate Powder, Unflavored, 2 Months Supply-60 Servings, 10.9 ounce", "Skura Style Smart 4 Pack Odor Free Non-Scratch Scrub Pads for Sink, Counters, Surfaces, Pots, Pans, Dishwashing with Color-Fade Change Indicator", "Pilot, Precise V5 RT Refillable & Retractable Rolling Ball Pens, Extra Fine Point 0.5 mm, Black, Pack of 6"]|Mark Kasdorf
113-6152746-7283461|July 16, 2025|138.54|["Marblelife InterCare Marble and Travertine, Natural Stone & Terrazzo Liquid Cleaner, Shower & Tile Surface Care, Floor, Walls & Countertop Cleaner and Degreaser, 32 oz", "Best Naturals Alpha Lipoic Acid 300 mg 120 Capsules", "Bronson Rhodiola Rosea Vegetarian Capsules - Adaptogenic Herb - Brain, Stress & Mood Support - Non-GMO, 250 Count", "Organic Lions Mane Mushroom Capsules (120 Count), for Brain Support and Immune Health, Third Party Tested and Grown in The USA - by Double Wood", "88 Acres Granola Bars | Double Dark Chocolate | Gluten Free, Nut-Free Oat and Seed Snack Bar | Vegan & Non GMO | 12 Pack"]|Mark Kasdorf
111-1229238-2116224|July 17, 2025|17.85|["A&A 2 Pack Chess Scorebook, 50 Games Recordable, 100 Moves Per Game, Eye-Protection Chess Scorebook"]|Mark Kasdorf
113-9448701-3053855|July 7, 2025|121.6|["True Thermostat-162F Fixed"]|Mark Kasdorf
112-2296787-4111432|June 16, 2025|28.68|["SOLARAY Tongkat Ali 400 mg - Longjack Tongkat Ali for Men - Herbal Support for Men's Health and Vitality - Vegan, Non-GMO, Lab Verified - 180 Servings, 180 VegCaps"]|Mark Kasdorf
113-8830758-1491423|June 17, 2025|34.53|["Trained Flag Football Set,10 Man Set,Premium Football Gear, Massive 36 Piece Set, Flags, Belts, Stylish Carry Bag & Flag Football Playbook (eBook)"]|Mark Kasdorf
113-1536607-2767404|June 20, 2025|47.73|["Toniiq Ultra High Purity Alpha GPC Capsules - 600mg Concentrated Formula - 90%+ Highly Purified and Bioavailable Nootropic - 120 Capsules Alpha GPC Supplement", "Organic Lions Mane Mushroom Capsules (120 Count), for Brain Support and Immune Health, Third Party Tested and Grown in The USA - by Double Wood"]|Mark Kasdorf
113-6269118-5492221|June 22, 2025|45.99|["Maldon Salt, Sea Salt Flakes, 3.1 lb, Bulk Tub, Kosher, Natural, Handcrafted, Gourmet, Pyramid Crystals"]|Mark Kasdorf
111-5209489-9137800|June 28, 2025|38.14|["Nutricost Vitamin E 400 IU, 240 Softgel Capsules - Gluten Free, Non-GMO", "Nutricost CoQ10 100mg, 120 Vegetarian Capsules, 120 Servings - High Absorption, Vegetarian, Non-GMO, Coenzyme Q10"]|Mark Kasdorf
111-2146545-2977015|June 5, 2025|32.99|["TruFox, Bree-1, Nude Pat, 7"]|Mark Kasdorf
112-8743535-6299419|June 8, 2025|113.66|["RitFit Weighted Medicine Ball - Non-Slip Rubber Shell & Dual Texture Grip - Workout Exercise Ball for Core Strength, Balance Training, Coordination Fitness - Multiple Weights & Colors"]|Mark Kasdorf
112-4472698-1958603|June 8, 2025|49.91|["FGBNM 25/50/100/200 Pack Disc Cones, Agility Soccer Cones with Carry Bag and Holder, Soccer Cones for Sports Training, Football, Soccer, Basketball, Coaching, Practice Equipment, 5 Color (25 Pack)", "RitFit Weighted Medicine Ball - Non-Slip Rubber Shell & Dual Texture Grip - Workout Exercise Ball for Core Strength, Balance Training, Coordination Fitness - Multiple Weights & Colors"]|Mark Kasdorf
111-3441500-4997845|March 1, 2025|15.88|["Nutricost High Potency Vitamin B Complex 462mg, 120 Capsules - with Vitamin C - Energy Complex"]|Mark Kasdorf
111-8923068-4766647|March 17, 2025|23.73|["Acetyl L-Carnitine 1,000mg Per Serving, 150 Capsules (ALCAR for Brain Function Support, Memory, Attention, and Stamina) Acetyl L Carnitine That is Third Party Tested by Double Wood", "Nature Made Zinc 30 mg, Dietary Supplement for Immune Health and Antioxidant Support, 100 Tablets, 100 Day Supply"]|Mark Kasdorf
111-5283026-2948233|March 28, 2025|44.95|["nutpods Dairy-Free Creamer Unsweetened, 11.2 FL OZ (Original, 12-pack) - Whole30 / Paleo / Keto / Vegan / Sugar Free, White"]|Mark Kasdorf
111-7468436-7058650|March 28, 2025|25.18|["Nutricost Alpha GPC 600mg, 120 Vegetarian Capsules - Non-GMO and Gluten Free, 300mg Per Capsule"]|Mark Kasdorf
112-9553637-4421800|March 30, 2025|10.61|["Bronson Rhodiola Rosea Vegetarian Capsules - Adaptogenic Herb - Brain, Stress & Mood Support - Non-GMO, 60 Count"]|Mark Kasdorf
112-8467169-8142657|March 30, 2025|24.37|["SOLARAY Tongkat Ali 400 mg - Longjack Tongkat Ali for Men - Herbal Support for Men's Health and Vitality - Vegan, Non-GMO, Lab Verified - 180 Servings, 180 VegCaps"]|Mark Kasdorf
112-4464694-4665046|March 30, 2025|60.51|["Omega 3 Fish Oil Supplement for Women and Men - 2500 mg Triple Strength Fish Oil Pills with Re-Esterified Omega 3 Fatty Acids including EPA, DHA DPA - 180 Pescatarian-Friendly Softgels"]|Mark Kasdorf
114-0484296-2473022|March 6, 2025|27.61|["100 Pcs Gift Certificates for Business- 8x6 in Blank Gift Certificates with Envelopes Great for Small Business, Restaurant, Spa, Makeup, Hair Beauty Salon, Wedding, Holiday, Christmas, Birthday.."]|Mark Kasdorf
111-9066591-3936262|March 7, 2025|7.65|["Mouse Skates/Mouse feet for Logitech G903 (2 Sets of Replacement feet)"]|Mark Kasdorf
111-0223892-1536251|March 9, 2025|20.03|["Corsair MM350 CHAMPION Series Premium Anti-Fray Cloth Gaming Mouse Pad \u2013 Medium"]|Mark Kasdorf
113-4618738-4957858|May 1, 2025|32.87|["Garden Hose Filter for Filling Hot Tub, Pool, Spa, Greatly Reduces Sediment, Chlorine, Heavy Metals and Odors, Standard 3/4\" Garden Hose Thread (1 pack)", "JJKMALL 1ml/2ml/3ml Mini Amber Glass Vial Essential Oil Bottles with Orifice Reducer and Cap for Essential Oils, Chemistry Lab Chemicals, (30 pcs) (1ml 10pcs 2ml 10pcs 3ml 10pcs)"]|Mark Kasdorf
112-4029543-4006645|May 14, 2025|36.06|["Best Naturals Alpha Lipoic Acid 300 mg 120 Capsules", "Nutricost Beta-Alanine Capsules 3400mg, 240 Capsules (60 Serv) - Potent Beta Alanine, Gluten Free & Non-GMO, 850mg Per Cap"]|Mark Kasdorf
113-1483227-1314653|May 18, 2025|23.9|["Big Green Egg Gasket Replacement Large/XL/XXL - 2-Pack x 8 FT Smoker Gasket Seal - BGE Gasket 7/8\" x 1/8\" Felt - Compatible with Big Green Egg - Accessories & Parts for Grill by Smoker Chef"]|Mark Kasdorf
113-2806577-3953809|May 18, 2025|146.43|["Wood Smoker Chips Bundle of 3 Flavors - Apple, Mesquite & Hickory Wood Chips for Smokers, Can be Used on Gas, Charcoal Grill, 3 Variety Flavor for Grilling, BBQ, Camping by Mr. Bar-B-Q(3 x 1.6 Lb Bag)", "FOGO Super Premium Hardwood Lump Charcoal, Natural, Large Sized Lump Charcoal for Grilling and Smoking, Restaurant Quality, 35 Pound Bag, 2-Pack"]|Mark Kasdorf
113-1003338-2025002|May 18, 2025|44.61|["LAO XUE Basketball Net Outdoor,(7.16 oz) 12 Loops Professional Heavy Duty Basketball Net Replacement,All Weather Anti Whip, Suitable for Outdoor Standard 12 Loops Basketball Hoop", "Dr. Squatch Natural Hair Bundle for Men - Healthy, Full Looking Hair - Fresh Falls Shampoo (8 fl oz), Fresh Falls Conditioner (11.5 fl oz)"]|Mark Kasdorf
113-6002696-5949028|May 23, 2025|8.48|["Hanatora Micro-AB to USB C 8.14 Inch Remote Controller Cable for DJI Mavic Mini, Mini SE & Mavic 2 Pro/Zoom, Air, Pro Platinum; OTG Nylon Braided Type C Tablet Data Cord (Type-C Connector)"]|Mark Kasdorf
111-3200098-3543450|May 7, 2025|38.15|["Nutricost Magnesium+ Extra Strength 420mg, 240 Capsules - 120 Servings. Magnesium Oxide and Glycinate - Non-GMO, Gluten Free, Vegan Friendly", "Nutricost Magnesium Glycinate Powder (250 Grams) (Unflavored)"]|Mark Kasdorf
111-5902225-8913816|May 8, 2025|21.2|["Nutricost L-Theanine 200mg, 240 Capsules - Double Strength"]|Mark Kasdorf
113-7935409-7736246|November 10, 2025|315.57|["Stelo Glucose Biosensor & App by Dexcom - A Leader in Continuous Glucose Monitoring (CGM). 24/7 Tracking & Personalized Insights to Reveal Patterns. 2-Pack (Up to 15 Days Each). iOS & Android."]|Mark Kasdorf
113-7191706-5537014|November 11, 2025|145.76|["Skura Style Smart 4 Pack Odor Free Non-Scratch Scrub Pads for Sink, Counters, Surfaces, Pots, Pans, Dishwashing with Color-Fade Change Indicator", "BSN Micronized Creatine Monohydrate Powder, Unflavored Creatine Powder, Micronized, Supports Muscle Building, Supports Muscle Strength, Gym Supplement, 5g per Serving, 5000mg per Servings, 60 Servings", "88 Acres Granola Bars | Dark Chocolate Sea Salt | Gluten Free, Nut-Free Oat and Seed Snack Bar | Vegan & Non GMO | 12 Pack"]|Mark Kasdorf
113-7090200-5021856|November 16, 2025|27.76|["What We Can Know: A Novel"]|Mark Kasdorf
111-9899279-2833844|November 7, 2025|212.49|["The Chimney Shop Big Stick Kwik-Shot Soot Stopper and Creosote Remover - 36 Pack"]|Mark Kasdorf
111-7531000-9128221|November 7, 2025|44.98|["Life Extension Zinc Caps, zinc 50 mg, zinc Citrate, Support The Body's Immune defenses, Ultra-absorbable, Vegetarian, Non-GMO, Gluten-Free, 90 Vegetarian Capsules", "Life Extension N-Acetyl-L-Cysteine (NAC), Immune Response, Respiratory Health, Liver Health, NAC 600 mg, antioxidant Defense, Free radicals, Easy to Absorb, 60 Capsules", "Life Extension Vitamins D and K with SeaIodine Bone Brain Thyroid Arterial Support Non GMO Once Daily Gluten Free, 60 Count"]|Mark Kasdorf
111-7517842-3638619|October 11, 2025|42.49|["Lancer Tactical 600ml/20oz 12kg Powerful Green Gas High PSI G-1100 for Airsoft Pistol, Airsoft Rifle, Air Gun (Silicone Oil) -Pack of 4"]|Mark Kasdorf
111-3801575-0307434|October 11, 2025|223.07|["Dye i5 Paintball Goggle (Onyx/Gold 2020)"]|Mark Kasdorf
113-8060418-2653001|October 12, 2025|10.61|["Silicone Muffin Pan Mini 24 Cups Cupcake Pan, Nonstick Food Grade Silicone Baking Pan 1 Pack"]|Mark Kasdorf
112-2131965-6372231|October 16, 2025|20.03|["Organic Lions Mane Mushroom Capsules (120 Count), for Focus Support and Immune Health, Third Party Tested and Grown in The USA - by Double Wood"]|Mark Kasdorf
114-8594563-4646637|October 16, 2025|22.93|["Toniiq Ultra High Purity Alpha GPC Capsules - 600mg Concentrated Formula - 90%+ Highly Purified and Bioavailable Nootropic - 120 Capsules Alpha GPC Supplement"]|Mark Kasdorf
113-9995712-8386623|October 22, 2025|77.97|["ACETAIA DODI Olivewood Balsamic, 8.5 FZ"]|Mark Kasdorf
113-3014937-0500254|October 28, 2025|10.61|["AOSWTLIF 1 Pound White Fine Quartz Sand - 80-120 Mesh, Ideal for Zen Gardens, DIY Painting, Incense Burners, Acrylic Texturing, Interior Decor & Vase Fillers"]|Mark Kasdorf
113-3486438-2835427|September 16, 2025|28.68|["SOLARAY Tongkat Ali 400 mg - Longjack Tongkat Ali for Men - Herbal Support for Men's Health and Vitality - Vegan, Non-GMO, Lab Verified - 180 Servings, 180 VegCaps"]|Mark Kasdorf
111-7508746-2806669|September 17, 2025|50.95|["for Unagi Scooter Charger - Replacement for Unagi One E350 E500 Scooter (33.6V 2A)"]|Mark Kasdorf
111-3303942-3954647|September 23, 2025|19.64|["Amazon Basics 48-Pack AA Alkaline High-Performance Batteries, 1.5 Volt, 10-Year Shelf Life"]|Mark Kasdorf
111-1338127-7164218|September 23, 2025|32.89|["Amazon Basics 12-Pack C Cell Alkaline All-Purpose Batteries, 1.5 Volt, 5-Year Shelf Life", "Amazon Basics 12-Pack D Cell Alkaline All-Purpose Batteries, 1.5 Volt, 5-Year Shelf Life"]|Mark Kasdorf


Now I have everything I need. Let me build the full matching + classification script:

write

"""
Amazon Order Matcher & Classifier
----------------------------------
Matches 116 unmatched Amazon CC rows to scraped order data.
- Under $100: single-line categorization (best guess, flag if unsure)
- Over $100: flag for split by item (or auto-split if items are clear)

Rules:
- Category from QBO chart of accounts
- Class: Me, K, KandM, Kids, Family, Bennett, Forge, etc.
- Mark = Me, Krista = K, both = KandM, gifts/kids = Kids/Family

Run: python3 amazon_classify.py
Outputs: amazon_classification_results.json + amazon_questions.txt
"""

import sqlite3
import json
import re
from datetime import datetime, timedelta

DB_PATH = "/Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db"

# ── QBO Category mappings ─────────────────────────────────────────────────────
CATEGORIES = {
    # Health / supplements
    "supplements": "Health & Wellness:Supplements & Vitamins",
    "personal_care": "Personal Care",
    "health_general": "Health & Wellness",
    # Household
    "household_supplies": "Home Spending:Household Supplies",
    "kitchen": "Home Spending:Household Supplies",
    "cleaning": "Home Spending:Household Supplies",
    # Food / groceries
    "groceries": "Home Spending:Groceries",
    "coffee": "Home Spending:Groceries",
    # Electronics / tech
    "electronics": "Electronics & Tech",
    # Clothing / personal items
    "clothing": "Personal Care",
    # Books / media
    "books": "Personal:Books & Media",
    # Sports / fitness
    "fitness": "Health & Wellness:Fitness",
    # Gifts
    "gifts": "Gifts & Donations",
    # Home / furniture
    "home_improvement": "Home Spending:Home Improvement",
    # Kids
    "kids_toys": "Kids",
    # Office / business
    "office": "Office & Business",
    # HVAC / Bennett property
    "hvac": "Bennett Property:Maintenance & Repair",
    # Entertainment
    "entertainment": "Personal:Entertainment",
    # Pet
    "pets": "Home Spending:Household Supplies",
    # Gift card
    "gift_card": "Gifts & Donations",
    # Outdoor / grill
    "outdoor_grill": "Home Spending:Household Supplies",
    # Firearms / airsoft
    "hobby": "Personal:Entertainment",
}

# ── Classifier ────────────────────────────────────────────────────────────────
def classify_item(item_name: str, recipient: str = "Mark Kasdorf") -> dict:
    """Return best-guess category, class, confidence, and notes for a single item."""
    name = item_name.lower()
    r = recipient.lower() if recipient else "mark kasdorf"

    # Default class
    cls = "Me"
    if "cassandra" in r:
        cls = "Cassandra"
    elif "gram" in r or "angela" in r:
        cls = "Family"  # gift to family member

    cat = None
    confidence = "high"
    notes = ""

    # ── SUPPLEMENTS / HEALTH ──────────────────────────────────────────────────
    supplement_keywords = [
        "magnesium", "vitamin", "omega", "fish oil", "zinc", "b complex",
        "alpha lipoic", "alpha gpc", "l-theanine", "l-tyrosine", "5-htp",
        "acetyl l-carnitine", "alcar", "beta-alanine", "creatine", "collagen",
        "peptide", "rhodiola", "tongkat ali", "lion's mane", "lions mane",
        "coq10", "calcium", "nac", "n-acetyl", "nootropic", "adaptogen",
        "nutricost", "bronson", "life extension", "best naturals", "toniiq",
        "vitamatic", "solaray", "nutpods", "orgain", "double wood",
    ]
    if any(k in name for k in supplement_keywords):
        cat = CATEGORIES["supplements"]
        cls = "Me"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Supplement/vitamin"}

    # ── PERSONAL CARE ─────────────────────────────────────────────────────────
    personal_care_keywords = [
        "shampoo", "conditioner", "shaving", "soap", "dr. squatch", "dr squatch",
        "vicks", "inhaler", "hand cream", "o'keeffe", "moistur", "skin care",
        "deodorant", "toothpaste", "dental",
    ]
    if any(k in name for k in personal_care_keywords):
        cat = CATEGORIES["personal_care"]
        cls = "Me"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Personal care"}

    # ── COFFEE / GROCERIES ────────────────────────────────────────────────────
    coffee_keywords = ["coffee", "blue bottle", "filter", "coffee filter", "nutpods creamer"]
    if any(k in name for k in coffee_keywords):
        cat = CATEGORIES["coffee"]
        cls = "Me"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Coffee/groceries"}

    food_keywords = ["maldon salt", "salt", "vanilla", "balsamic", "granola bar", "snack bar",
                     "ice cream", "cookbook", "bar-b-q", "charcoal", "smoker chip", "bbq"]
    if any(k in name for k in food_keywords):
        cat = CATEGORIES["groceries"]
        cls = "KandM"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Food/grocery item"}

    # ── CLEANING / HOUSEHOLD ──────────────────────────────────────────────────
    cleaning_keywords = [
        "sponge", "scrub", "cleaning cloth", "dish", "microfiber", "mop",
        "broom", "trash bag", "paper towel", "cleaning", "detergent", "laundry",
        "mr.siga", "skura", "scrub daddy",
    ]
    if any(k in name for k in cleaning_keywords):
        cat = CATEGORIES["cleaning"]
        cls = "KandM"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Cleaning supply"}

    # ── BATTERIES / ELECTRONICS ───────────────────────────────────────────────
    battery_keywords = ["battery", "batteries", "alkaline", "amazon basics aa", "amazon basics aaa",
                        "amazon basics c ", "amazon basics d "]
    if any(k in name for k in battery_keywords):
        cat = CATEGORIES["household_supplies"]
        cls = "KandM"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Batteries/household"}

    electronics_keywords = [
        "usb", "cable", "adapter", "charger", "mouse", "keyboard", "headphone",
        "speaker", "monitor", "hard drive", "ssd", "phone", "iphone", "android",
        "logitech", "corsair", "gaming", "nintendo", "switch", "joy-con", "hdmi",
        "micro usb", "usb c", "power bank", "raspberry", "webcam", "printer",
        "rollo", "barcode", "drone", "dji", "scooter charger", "unagi",
    ]
    if any(k in name for k in electronics_keywords):
        cat = CATEGORIES["electronics"]
        cls = "Me"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Electronics/tech"}

    # ── FITNESS / SPORTS ──────────────────────────────────────────────────────
    fitness_keywords = [
        "medicine ball", "basketball", "soccer", "football", "agility cone",
        "sports", "workout", "exercise", "gym", "creatine", "protein",
        "flag football", "net", "hoop", "airsoft", "paintball", "chess",
    ]
    if any(k in name for k in fitness_keywords):
        cat = CATEGORIES["fitness"]
        cls = "Me"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Fitness/sports"}

    # ── BOOKS ─────────────────────────────────────────────────────────────────
    book_keywords = ["book", "novel", "dictionary", "cookbook", "pihkal", "tihkal",
                     "scorebook", "icelandic", "hello my name is"]
    if any(k in name for k in book_keywords):
        cat = CATEGORIES["books"]
        cls = "Me"
        if "icelandic" in name:
            cls = "Family"  # Gift to Angela
            notes = "Gift — Icelandic books to Angela"
        return {"category": cat, "class": cls, "confidence": "high", "notes": notes or "Books/media"}

    # ── HOME IMPROVEMENT ──────────────────────────────────────────────────────
    home_keywords = [
        "furnace filter", "air filter", "filtrete", "hvac", "thermostat",
        "smoke detector", "carbon monoxide", "ladder", "tool", "drill",
        "paint", "brush", "shoe rack", "bamboo", "storage", "organizer",
        "freezer", "appliance", "silicone muffin", "baking",
    ]
    if any(k in name for k in home_keywords):
        cat = CATEGORIES["home_improvement"]
        cls = "KandM"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Home/household item"}

    # ── HVAC / BENNETT ────────────────────────────────────────────────────────
    hvac_keywords = ["thermostat", "hvac", "chimney", "creosote", "soot",
                     "chimney shop", "big stick kwik"]
    if any(k in name for k in hvac_keywords):
        cat = CATEGORIES["hvac"]
        cls = "Bennett"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "HVAC/Bennett property"}

    # ── GIFTS / KIDS ──────────────────────────────────────────────────────────
    gift_keywords = ["gift card", "amc theatres", "gift certificate", "certificate"]
    if any(k in name for k in gift_keywords):
        cat = CATEGORIES["gift_card"]
        cls = "Family"
        return {"category": cat, "class": cls, "confidence": "medium", "notes": "Gift card — confirm recipient"}

    kids_toy_keywords = [
        "lego", "squishmallow", "toy", "plush", "doll", "blanket", "game",
        "board game", "minecraft", "pokemon", "barbie", "play-doh", "playmobil",
        "typewriter building", "football card", "trading card",
    ]
    if any(k in name for k in kids_toy_keywords):
        cat = CATEGORIES["kids_toys"]
        cls = "Kids"
        return {"category": cat, "class": cls, "confidence": "medium", "notes": "Possible kids item — verify"}

    # ── GRILL / OUTDOOR ───────────────────────────────────────────────────────
    grill_keywords = ["smoker", "charcoal", "grill", "bbq", "gasket", "green egg",
                      "wood chip", "hose filter", "hot tub", "pool", "spa"]
    if any(k in name for k in grill_keywords):
        cat = CATEGORIES["outdoor_grill"]
        cls = "KandM"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Grill/outdoor"}

    # ── PETS ──────────────────────────────────────────────────────────────────
    pet_keywords = ["dog", "cat", "pet", "carrier", "collar", "leash", "paw",
                    "kennel", "kibble", "treat", "henkelion"]
    if any(k in name for k in pet_keywords):
        cat = CATEGORIES["pets"]
        cls = "KandM"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Pet supply"}

    # ── ENTERTAINMENT / HOBBY ─────────────────────────────────────────────────
    entertainment_keywords = [
        "planetarium", "star projector", "warhammer", "paint brush", "miniature",
        "airsoft", "paintball", "chess", "game", "hobby", "incense", "zen garden",
        "quartz sand",
    ]
    if any(k in name for k in entertainment_keywords):
        cat = CATEGORIES["hobby"]
        cls = "Me"
        return {"category": cat, "class": cls, "confidence": "medium", "notes": "Entertainment/hobby"}

    # ── WOMEN'S CLOTHING/ACCESSORIES (likely Krista or Cassandra) ────────────
    womens_keywords = [
        "headband", "lip gloss", "lip liner", "bracelet", "scarf", "shawl",
        "women", "woman", "girls", "teen girl", "nail", "mascara", "blush",
        "e.l.f.", "wet n wild", "anteel", "silicone suction phone",
    ]
    if any(k in name for k in womens_keywords):
        cat = CATEGORIES["personal_care"]
        # Check if delivered to Cassandra
        if "cassandra" in r:
            cls = "Cassandra"
        else:
            cls = "K"
        return {"category": cat, "class": cls, "confidence": "medium",
                "notes": "Women's personal care — likely Krista or Cassandra"}

    # ── OFFICE / BUSINESS ─────────────────────────────────────────────────────
    office_keywords = [
        "pen", "pilot precise", "paper cup", "dixie", "coffee cup", "disposable cup",
        "label", "envelope", "notebook", "binder", "staple",
    ]
    if any(k in name for k in office_keywords):
        cat = CATEGORIES["office"]
        cls = "Me"
        return {"category": cat, "class": cls, "confidence": "high", "notes": "Office supply"}

    # ── RUNNING / SOCKS / CLOTHING ────────────────────────────────────────────
    clothing_keywords = ["sock", "shirt", "pants", "shorts", "shoes", "sneaker",
                         "jacket", "thorlos", "nfl", "jersey"]
    if any(k in name for k in clothing_keywords):
        cat = CATEGORIES["clothing"]
        cls = "Me"
        return {"category": cat, "class": cls, "confidence": "medium", "notes": "Clothing/apparel"}

    # ── UNKNOWN ───────────────────────────────────────────────────────────────
    return {
        "category": CATEGORIES["household_supplies"],
        "class": "Me",
        "confidence": "low",
        "notes": f"UNKNOWN — item: {item_name[:80]}"
    }

def parse_order_date(date_str: str):
    """Convert scraped display date to datetime object."""
    if not date_str:
        return None
    # Try ISO first
    try:
        return datetime.strptime(date_str[:10], "%Y-%m-%d")
    except:
        pass
    # Try display format: "April 2, 2025", "February 10, 2025", etc.
    for fmt in ["%B %d, %Y", "%B %d %Y", "%b %d, %Y"]:
        try:
            return datetime.strptime(date_str.strip(), fmt)
        except:
            pass
    # Truncated month like "Februar" — try first 3 chars
    try:
        short = date_str.strip()
        # Find year
        year_match = re.search(r'(\d{4})', short)
        day_match = re.search(r'\b(\d{1,2})\b', short)
        month_match = re.match(r'([A-Za-z]+)', short)
        if year_match and day_match and month_match:
            month_str = month_match.group(1)[:3]
            return datetime.strptime(f"{month_str} {day_match.group(1)} {year_match.group(1)}", "%b %d %Y")
    except:
        pass
    return None

def amount_match(cc_amount: float, order_amount: float, tolerance: float = 2.0) -> bool:
    """Check if amounts match within tolerance."""
    return abs(cc_amount - order_amount) <= tolerance

def date_match(cc_date_str: str, order_date, window_days: int = 5) -> bool:
    """Check if CC charge date is within window of order date (orders ship 1-3 days before charge)."""
    try:
        cc_date = datetime.strptime(cc_date_str, "%Y-%m-%d")
    except:
        return True  # can't parse, don't filter
    if not order_date:
        return True
    delta = (cc_date - order_date).days
    return -2 <= delta <= window_days  # order can be 2 days after charge (returns), or up to 5 days before

def main():
    conn = sqlite3.connect(DB_PATH)
    conn.row_factory = sqlite3.Row
    cur = conn.cursor()

    # Get all 116 unmatched Amazon CC rows
    cur.execute("""
        SELECT id, txn_date, amount, description, card_last4
        FROM statement_rows
        WHERE match_status = 'unmatched' AND confidence = 'low'
          AND (description LIKE '%AMAZON%' OR description LIKE '%AMZ%' OR suggested_vendor LIKE '%Amazon%')
        ORDER BY txn_date
    """)
    cc_rows = cur.fetchall()

    # Get all 2025 Amazon orders
    cur.execute("""
        SELECT order_id, order_date, total_amount, items_json, recipient
        FROM amazon_orders_raw
        WHERE order_year = 2025
    """)
    orders = cur.fetchall()

    # Parse orders
    parsed_orders = []
    for o in orders:
        items = []
        try:
            items = json.loads(o["items_json"]) if o["items_json"] else []
        except:
            pass
        parsed_orders.append({
            "order_id": o["order_id"],
            "order_date": parse_order_date(o["order_date"]),
            "order_date_raw": o["order_date"],
            "total_amount": o["total_amount"],
            "items": items,
            "recipient": o["recipient"] or "Mark Kasdorf",
            "matched": False,
        })

    results = []
    questions = []
    matched_order_ids = set()

    for row in cc_rows:
        cc_id = row["id"]
        cc_amount = row["amount"]
        cc_date = row["txn_date"]

        # Find candidate orders by amount match
        candidates = [
            o for o in parsed_orders
            if amount_match(cc_amount, o["total_amount"])
            and date_match(cc_date, o["order_date"])
            and o["order_id"] not in matched_order_ids
        ]

        if not candidates:
            # Try looser amount match (within $5)
            candidates = [
                o for o in parsed_orders
                if amount_match(cc_amount, o["total_amount"], tolerance=5.0)
                and date_match(cc_date, o["order_date"], window_days=10)
                and o["order_id"] not in matched_order_ids
            ]

        matched_order = candidates[0] if len(candidates) == 1 else None

        # Multiple candidates — pick best date match
        if len(candidates) > 1:
            def date_score(o):
                if not o["order_date"]:
                    return 99
                try:
                    cc_dt = datetime.strptime(cc_date, "%Y-%m-%d")
                    return abs((cc_dt - o["order_date"]).days)
                except:
                    return 99
            candidates.sort(key=date_score)
            matched_order = candidates[0]

        if matched_order:
            matched_order_ids.add(matched_order["order_id"])
            items = matched_order["items"]
            recipient = matched_order["recipient"]

            if cc_amount < 100:
                # Single-line classification from first/dominant item
                if items:
                    best_item = items[0]
                    # If multiple items, pick the most expensive-looking (longest/most descriptive)
                    if len(items) > 1:
                        best_item = max(items, key=lambda x: len(x))
                    result = classify_item(best_item, recipient)
                    result["multi_item"] = len(items) > 1
                    result["items"] = items
                else:
                    result = {"category": "Home Spending:Household Supplies", "class": "Me",
                              "confidence": "low", "notes": "No items in DB", "items": []}

                entry = {
                    "cc_row_id": cc_id,
                    "cc_date": cc_date,
                    "cc_amount": cc_amount,
                    "order_id": matched_order["order_id"],
                    "order_date": matched_order["order_date_raw"],
                    "order_amount": matched_order["total_amount"],
                    "matched": True,
                    "under_100": True,
                    "split_required": False,
                    "category": result["category"],
                    "class": result["class"],
                    "confidence": result["confidence"],
                    "notes": result["notes"],
                    "items": items,
                    "recipient": recipient,
                }

                if result["confidence"] == "low":
                    questions.append(entry)

            else:
                # Over $100 — flag for item-level split
                item_classifications = []
                needs_question = False
                for item in items:
                    r = classify_item(item, recipient)
                    item_classifications.append({
                        "item": item,
                        "category": r["category"],
                        "class": r["class"],
                        "confidence": r["confidence"],
                        "notes": r["notes"],
                    })
                    if r["confidence"] == "low":
                        needs_question = True

                # Check if all items classify to same category/class — simplifies split
                categories_set = set(i["category"] for i in item_classifications)
                classes_set = set(i["class"] for i in item_classifications)
                all_same = len(categories_set) == 1 and len(classes_set) == 1

                entry = {
                    "cc_row_id": cc_id,
                    "cc_date": cc_date,
                    "cc_amount": cc_amount,
                    "order_id": matched_order["order_id"],
                    "order_date": matched_order["order_date_raw"],
                    "order_amount": matched_order["total_amount"],
                    "matched": True,
                    "under_100": False,
                    "split_required": not all_same,
                    "all_same_category": all_same,
                    "single_category": list(categories_set)[0] if all_same else None,
                    "single_class": list(classes_set)[0] if all_same else None,
                    "item_classifications": item_classifications,
                    "confidence": "medium" if needs_question else "high",
                    "notes": "All items same category — no split needed" if all_same else "Multi-category split required",
                    "items": items,
                    "recipient": recipient,
                }

                if needs_question or not all_same:
                    questions.append(entry)

            results.append(entry)

        else:
            # No match found in DB
            entry = {
                "cc_row_id": cc_id,
                "cc_date": cc_date,
                "cc_amount": cc_amount,
                "order_id": None,
                "matched": False,
                "under_100": cc_amount < 100,
                "split_required": cc_amount >= 100,
                "category": "Home Spending:Household Supplies",
                "class": "Me",
                "confidence": "low",
                "notes": "NO ORDER MATCH IN DB",
                "items": [],
                "recipient": "Unknown",
            }
            results.append(entry)
            questions.append(entry)

    conn.close()

    # Write results
    out_path = "/Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_classification_results.json"
    with open(out_path, "w") as f:
        json.dump(results, f, indent=2, default=str)

    # Summary
    matched = sum(1 for r in results if r["matched"])
    unmatched = sum(1 for r in results if not r["matched"])
    high_conf = sum(1 for r in results if r.get("confidence") == "high" and r["matched"])
    medium_conf = sum(1 for r in results if r.get("confidence") == "medium" and r["matched"])
    needs_split = sum(1 for r in results if r.get("split_required"))
    q_count = len(questions)

    print(f"\n{'='*60}")
    print(f"AMAZON CLASSIFICATION RESULTS")
    print(f"{'='*60}")
    print(f"Total CC rows:        {len(results)}")
    print(f"Matched to orders:    {matched}")
    print(f"No DB match:          {unmatched}")
    print(f"  → High confidence:  {high_conf}")
    print(f"  → Medium conf:      {medium_conf}")
    print(f"  → Needs split:      {needs_split}")
    print(f"  → Questions for Mark: {q_count}")
    print(f"\nResults written to:   {out_path}")

    return results, questions

if __name__ == "__main__":
    results, questions = main()


Successfully wrote 22935 bytes to /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_classify.py


exec

cd /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo && python3 amazon_classify.py 2>&1


============================================================
AMAZON CLASSIFICATION RESULTS
============================================================
Total CC rows:        116
Matched to orders:    52
No DB match:          64
  → High confidence:  41
  → Medium conf:      8
  → Needs split:      22
  → Questions for Mark: 70

Results written to:   /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_classification_results.json


52 matched, 64 didn't find an order in the DB. Let me understand what the unmatched ones look like and review the full output before bringing you anything:

cd /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo && python3 -c "
import json

with open('amazon_classification_results.json') as f:
    results = json.load(f)

# Separate matched vs not
matched = [r for r in results if r['matched']]
unmatched = [r for r in results if not r['matched']]

print(f'=== UNMATCHED ({len(unmatched)} rows — no order in DB) ===')
for r in unmatched:
    print(f'  ID={r[\"cc_row_id\"]} | {r[\"cc_date\"]} | \${r[\"cc_amount\"]:.2f}')

print()
print(f'=== MATCHED HIGH CONFIDENCE UNDER \$100 ({sum(1 for r in matched if r[\"confidence\"]==\"high\" and r[\"under_100\"])} rows) ===')
for r in matched:
    if r['confidence'] == 'high' and r['under_100']:
        items_str = ', '.join(r['items'])[:80]
        print(f'  ID={r[\"cc_row_id\"]} | {r[\"cc_date\"]} | \${r[\"cc_amount\"]:.2f} | {r[\"category\"]} [{r[\"class\"]}] | {items_str}')

print()
print(f'=== MATCHED HIGH CONFIDENCE OVER \$100 — ALL SAME CATEGORY ({sum(1 for r in matched if r[\"confidence\"]==\"high\" and not r[\"under_100\"] and not r.get(\"split_required\"))} rows) ===')
for r in matched:
    if r['confidence'] == 'high' and not r['under_100'] and not r.get('split_required'):
        print(f'  ID={r[\"cc_row_id\"]} | {r[\"cc_date\"]} | \${r[\"cc_amount\"]:.2f} | {r.get(\"single_category\",r.get(\"category\"))} [{r.get(\"single_class\",r.get(\"class\"))}]')
        for item in r['items']:
            print(f'      - {item[:100]}')
" 2>&1


=== UNMATCHED (64 rows — no order in DB) ===
  ID=3215 | 2025-03-02 | $165.72
  ID=3216 | 2025-03-02 | $637.47
  ID=3143 | 2025-03-21 | $11.58
  ID=3041 | 2025-04-19 | $24.65
  ID=3037 | 2025-04-20 | $20.12
  ID=2859 | 2025-06-02 | $133.85
  ID=2848 | 2025-06-04 | $9.17
  ID=2850 | 2025-06-04 | $94.01
  ID=2783 | 2025-06-20 | $2.56
  ID=2784 | 2025-06-20 | $3.33
  ID=2746 | 2025-06-28 | $28.68
  ID=2540 | 2025-08-23 | $89.02
  ID=2532 | 2025-08-24 | $31.82
  ID=2533 | 2025-08-24 | $74.36
  ID=2534 | 2025-08-24 | $128.77
  ID=2527 | 2025-08-25 | $135.99
  ID=2522 | 2025-08-26 | $101.16
  ID=2514 | 2025-08-28 | $79.87
  ID=2503 | 2025-08-30 | $68.53
  ID=2505 | 2025-08-30 | $129.37
  ID=2500 | 2025-08-31 | $20.03
  ID=2479 | 2025-09-06 | $127.12
  ID=2431 | 2025-09-17 | $2.31
  ID=2432 | 2025-09-17 | $3.42
  ID=2433 | 2025-09-17 | $39.47
  ID=2425 | 2025-09-19 | $15.93
  ID=2426 | 2025-09-19 | $427.52
  ID=2423 | 2025-09-20 | $246.71
  ID=2385 | 2025-09-28 | $28.68
  ID=2386 | 2025-09-28 | $71.36
  ID=2387 | 2025-09-28 | $146.40
  ID=2340 | 2025-10-11 | $33.99
  ID=2343 | 2025-10-11 | $41.43
  ID=2334 | 2025-10-12 | $20.07
  ID=2335 | 2025-10-12 | $119.95
  ID=2296 | 2025-10-22 | $53.11
  ID=2272 | 2025-10-29 | $20.03
  ID=2273 | 2025-10-29 | $22.93
  ID=2230 | 2025-11-09 | $30.79
  ID=2231 | 2025-11-09 | $106.24
  ID=2232 | 2025-11-09 | $6.19
  ID=2233 | 2025-11-09 | $10.98
  ID=2234 | 2025-11-09 | $13.06
  ID=2235 | 2025-11-09 | $35.95
  ID=2217 | 2025-11-12 | $457.82
  ID=2209 | 2025-11-14 | $96.26
  ID=2203 | 2025-11-15 | $91.98
  ID=2194 | 2025-11-16 | $514.24
  ID=2195 | 2025-11-16 | $119.82
  ID=2188 | 2025-11-17 | $29.31
  ID=2182 | 2025-11-18 | $22.99
  ID=2183 | 2025-11-18 | $27.99
  ID=2175 | 2025-11-19 | $35.32
  ID=2079 | 2025-12-11 | $183.93
  ID=2072 | 2025-12-12 | $63.07
  ID=2016 | 2025-12-21 | $142.26
  ID=2006 | 2025-12-22 | $26.51
  ID=1990 | 2025-12-25 | $100.22
  ID=1986 | 2025-12-26 | $46.06
  ID=1976 | 2025-12-27 | $34.72
  ID=1965 | 2025-12-28 | $9.55
  ID=1968 | 2025-12-28 | $11.68
  ID=1969 | 2025-12-28 | $25.49
  ID=1970 | 2025-12-28 | $56.46

=== MATCHED HIGH CONFIDENCE UNDER $100 (34 rows) ===
  ID=3254 | 2025-02-20 | $12.72 | Personal Care [Me] | Vicks VapoInhaler, On-the-Go Portable Nasal Inhaler, Non-Medicated, With Refresh
  ID=3245 | 2025-02-24 | $25.79 | Home Spending:Household Supplies [KandM] | Henkelion Cat, Dog Carrier for Small Medium Cats Puppies up to 15 Lbs, TSA Airli
  ID=3224 | 2025-03-01 | $15.88 | Health & Wellness:Supplements & Vitamins [Me] | Nutricost High Potency Vitamin B Complex 462mg, 120 Capsules - with Vitamin C -
  ID=3181 | 2025-03-09 | $20.03 | Electronics & Tech [Me] | Corsair MM350 CHAMPION Series Premium Anti-Fray Cloth Gaming Mouse Pad – Medium
  ID=3154 | 2025-03-17 | $23.73 | Health & Wellness:Supplements & Vitamins [Me] | Acetyl L-Carnitine 1,000mg Per Serving, 150 Capsules (ALCAR for Brain Function S
  ID=3118 | 2025-03-28 | $25.18 | Health & Wellness:Supplements & Vitamins [Me] | Nutricost Alpha GPC 600mg, 120 Vegetarian Capsules - Non-GMO and Gluten Free, 30
  ID=3119 | 2025-03-28 | $44.95 | Health & Wellness:Supplements & Vitamins [Me] | nutpods Dairy-Free Creamer Unsweetened, 11.2 FL OZ (Original, 12-pack) - Whole30
  ID=3112 | 2025-03-30 | $24.37 | Health & Wellness:Supplements & Vitamins [Me] | SOLARAY Tongkat Ali 400 mg - Longjack Tongkat Ali for Men - Herbal Support for M
  ID=3113 | 2025-03-30 | $60.51 | Health & Wellness:Supplements & Vitamins [Me] | Omega 3 Fish Oil Supplement for Women and Men - 2500 mg Triple Strength Fish Oil
  ID=3108 | 2025-04-01 | $10.61 | Health & Wellness:Supplements & Vitamins [Me] | Bronson Rhodiola Rosea Vegetarian Capsules - Adaptogenic Herb - Brain, Stress &
  ID=3097 | 2025-04-06 | $52.02 | Home Spending:Household Supplies [KandM] | Skura Style Smart Kitchen Dish Sponges (Bulk 8 Pack) | Odor Free Non-Scratch Scr
  ID=3095 | 2025-04-07 | $16.99 | Home Spending:Household Supplies [KandM] | MR.SIGA Microfiber Cleaning Cloth, All-Purpose Cleaning Towels, Pack of 6, Size
  ID=2999 | 2025-04-24 | $10.61 | Electronics & Tech [Me] | Rollo Direct Thermal 2x1 Barcode Labels - Roll of 1,000 Thermal Labels (Commerci
  ID=2962 | 2025-05-07 | $38.15 | Health & Wellness:Supplements & Vitamins [Me] | Nutricost Magnesium+ Extra Strength 420mg, 240 Capsules - 120 Servings. Magnesiu
  ID=2954 | 2025-05-08 | $21.20 | Health & Wellness:Supplements & Vitamins [Me] | Nutricost L-Theanine 200mg, 240 Capsules - Double Strength
  ID=2930 | 2025-05-14 | $36.06 | Health & Wellness:Supplements & Vitamins [Me] | Best Naturals Alpha Lipoic Acid 300 mg 120 Capsules, Nutricost Beta-Alanine Caps
  ID=2915 | 2025-05-18 | $44.61 | Health & Wellness:Fitness [Me] | LAO XUE Basketball Net Outdoor,(7.16 oz) 12 Loops Professional Heavy Duty Basket
  ID=2829 | 2025-06-08 | $49.91 | Health & Wellness:Fitness [Me] | FGBNM 25/50/100/200 Pack Disc Cones, Agility Soccer Cones with Carry Bag and Hol
  ID=2785 | 2025-06-20 | $24.65 | Health & Wellness:Supplements & Vitamins [Me] | SOLARAY Tongkat Ali 400 mg - Longjack Tongkat Ali for Men - Herbal Support for M
  ID=2670 | 2025-07-16 | $78.09 | Home Spending:Household Supplies [KandM] | BSN Micronized Creatine Monohydrate Powder, Unflavored, 2 Months Supply-60 Servi
  ID=2563 | 2025-08-19 | $24.65 | Health & Wellness:Supplements & Vitamins [Me] | Organic Lions Mane Mushroom Capsules (120 Count), for Brain Support and Immune H
  ID=2519 | 2025-08-26 | $62.45 | Health & Wellness:Supplements & Vitamins [Me] | Orgain Hydrolyzed Collagen Peptides Powder For Women & Men, 20g Grass Fed Collag
  ID=2520 | 2025-08-26 | $63.75 | Personal Care [Me] | Dr. Squatch Natural Bar Soap for Men Variety Gift Set - Cold Process No Harsh Ch
  ID=2422 | 2025-09-20 | $31.86 | Health & Wellness:Supplements & Vitamins [Me] | SOLARAY Tongkat Ali 400 mg - Longjack Tongkat Ali for Men - Herbal Support for M
  ID=2409 | 2025-09-23 | $19.64 | Home Spending:Household Supplies [KandM] | Amazon Basics 48-Pack AA Alkaline High-Performance Batteries, 1.5 Volt, 10-Year
  ID=2410 | 2025-09-23 | $32.89 | Home Spending:Household Supplies [KandM] | Amazon Basics 12-Pack C Cell Alkaline All-Purpose Batteries, 1.5 Volt, 5-Year Sh
  ID=2341 | 2025-10-11 | $42.49 | Health & Wellness:Fitness [Me] | Lancer Tactical 600ml/20oz 12kg Powerful Green Gas High PSI G-1100 for Airsoft P
  ID=2193 | 2025-11-16 | $27.76 | Personal:Books & Media [Me] | What We Can Know: A Novel
  ID=2036 | 2025-12-17 | $2.06 | Health & Wellness:Supplements & Vitamins [Me] | SOLARAY Tongkat Ali 400 mg - Longjack Tongkat Ali for Men - Herbal Support for M
  ID=2037 | 2025-12-17 | $3.06 | Health & Wellness:Supplements & Vitamins [Me] | Organic Lions Mane Mushroom Capsules (120 Count), for Focus Support and Immune H
  ID=1999 | 2025-12-23 | $9.94 | Health & Wellness:Fitness [Me] | Football Can & Bottle Holder Insulator Beverage Huggie Cooler (Buffalo Blue (Bil
  ID=1993 | 2025-12-24 | $15.99 | Home Spending:Groceries [KandM] | Hello, My Name Is Ice Cream: The Art and Science of the Scoop: A Cookbook
  ID=1966 | 2025-12-28 | $13.60 | Electronics & Tech [Me] | Electronic Whistle Rechargeable for Referees Coaches Teachers Electric Whistle H
  ID=1967 | 2025-12-28 | $14.86 | Home Spending:Household Supplies [KandM] | NFL Buffalo Bills Dog T-Shirt for Pets, Size Small. Officially Licensed Pet Shir

=== MATCHED HIGH CONFIDENCE OVER $100 — ALL SAME CATEGORY (6 rows) ===
  ID=3229 | 2025-02-28 | $127.43 | Health & Wellness:Supplements & Vitamins [Me]
      - Filtrete 20x25x4 AC Furnace Air Filter, MPR 1550, MERV 12, Fits Lennox & Honeywell, Allergen, Bacter
  ID=2991 | 2025-04-27 | $387.92 | Home Spending:Home Improvement [KandM]
      - Summit AccuCold FS24L 19" Upright Freezer with 1.4 cu. ft. Capacity, Factory Installed Lock, Manual
  ID=2830 | 2025-06-08 | $113.66 | Health & Wellness:Fitness [Me]
      - RitFit Weighted Medicine Ball - Non-Slip Rubber Shell & Dual Texture Grip - Workout Exercise Ball fo
  ID=2707 | 2025-07-07 | $121.60 | Home Spending:Home Improvement [KandM]
      - True Thermostat-162F Fixed
  ID=2342 | 2025-10-11 | $223.07 | Health & Wellness:Fitness [Me]
      - Dye i5 Paintball Goggle (Onyx/Gold 2020)
  ID=2186 | 2025-11-17 | $318.70 | Electronics & Tech [Me]
      - Stelo Glucose Biosensor & App by Dexcom - A Leader in Continuous Glucose Monitoring (CGM). 24/7 Trac


cd /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo && python3 -c "
import json

with open('amazon_classification_results.json') as f:
    results = json.load(f)

matched = [r for r in results if r['matched']]

# Medium confidence (need review)
medium = [r for r in matched if r.get('confidence') == 'medium']
print(f'=== MEDIUM CONFIDENCE ({len(medium)} rows) ===')
for r in medium:
    print(f'  ID={r[\"cc_row_id\"]} | {r[\"cc_date\"]} | \${r[\"cc_amount\"]:.2f}')
    for item in r.get('items', []):
        print(f'    - {item[:100]}')
    print(f'    → {r.get(\"category\")} [{r.get(\"class\")}] | note: {r.get(\"notes\")}')
    print()

# Over 100 needing split
splits = [r for r in matched if r.get('split_required') and not r['under_100']]
print(f'=== OVER \$100 NEEDING SPLIT ({len(splits)} rows) ===')
for r in splits:
    print(f'  ID={r[\"cc_row_id\"]} | {r[\"cc_date\"]} | \${r[\"cc_amount\"]:.2f}')
    for ic in r.get('item_classifications', []):
        print(f'    - [{ic[\"class\"]}] {ic[\"category\"]} | {ic[\"item\"][:90]}')
    print()
" 2>&1


=== MEDIUM CONFIDENCE (8 rows) ===
  ID=3194 | 2025-03-06 | $27.61
    - 100 Pcs Gift Certificates for Business- 8x6 in Blank Gift Certificates with Envelopes Great for Smal
    → Gifts & Donations [Family] | note: Gift card — confirm recipient

  ID=2671 | 2025-07-16 | $138.54
    - Marblelife InterCare Marble and Travertine, Natural Stone & Terrazzo Liquid Cleaner, Shower & Tile S
    - Best Naturals Alpha Lipoic Acid 300 mg 120 Capsules
    - Bronson Rhodiola Rosea Vegetarian Capsules - Adaptogenic Herb - Brain, Stress & Mood Support - Non-G
    - Organic Lions Mane Mushroom Capsules (120 Count), for Brain Support and Immune Health, Third Party T
    - 88 Acres Granola Bars | Double Dark Chocolate | Gluten Free, Nut-Free Oat and Seed Snack Bar | Vegan
    → None [None] | note: Multi-category split required

  ID=2035 | 2025-12-17 | $23.36
    - ENJBRICK Typewriter Building Set Compatible with Lego for Adults,Ideas Cute Building Toys for Girls
    → Kids [Kids] | note: Possible kids item — verify

  ID=2041 | 2025-12-17 | $50.00
    - AMC Theatres Gift Card $50
    → Gifts & Donations [Family] | note: Gift card — confirm recipient

  ID=2015 | 2025-12-21 | $80.99
    - Squishmallows kellytoy Veggie Squad Plush Doll Toy (8" Claudia The Beet)
    - HUES and CUES - Vibrant Color Guessing Board Game for 3-10 Players Ages 8+, Connect Clues and Guess
    - Mermaker Burrito Tortilla Taco Blanket 71 inches for Adult Kids, Realistic Tortilla Throw Blanket Fu
    - maamgic Womens Feel Scarf Large Shawls and Wraps Light Blanket Scarf for Evening Dress Warm Daily Tr
    → Kids [Kids] | note: Possible kids item — verify

  ID=2005 | 2025-12-22 | $17.73
    - thorlos mens Xj Max Cushion Crew Running Sock, White/Platinum (1 Pair), Small US
    → Personal Care [Me] | note: Clothing/apparel

  ID=2000 | 2025-12-23 | $15.99
    - thorlos mens Xj Max Cushion Crew Running Sock, White/Platinum (1 Pair), Small US
    → Personal Care [Me] | note: Clothing/apparel

  ID=1971 | 2025-12-28 | $379.20
    - CHAUVET DJ HURRICANE HAZE 1DX (HHAZE (HHAZE 1DX)
    - Nintendo Neon Purple/Neon Orange Joy-Con (L-R) - Switch
    - Chauvet DJ High Performance Haze Fluid (HFG)
    → None [None] | note: Multi-category split required

=== OVER $100 NEEDING SPLIT (3 rows) ===
  ID=2990 | 2025-04-27 | $177.01
    - [KandM] Home Spending:Household Supplies | EISCO Beaker, 50ml - Tall Form with Spout - White, 5ml Graduations - Borosilicate 3.3 Glas
    - [Me] Health & Wellness:Supplements & Vitamins | Vitamatic Magnesium Glycinate 500mg per Tablet - 180 Vegetarian Tablets (Uncoated) - Added
    - [KandM] Home Spending:Household Supplies | Amazon Basics 48-Pack AA Alkaline High-Performance Batteries, 1.5 Volt, 10-Year Shelf Life
    - [KandM] Home Spending:Groceries | Maldon Salt, Sea Salt Flakes, 3.1 lb, Bulk Tub, Kosher, Natural, Handcrafted, Gourmet, Pyr
    - [KandM] Home Spending:Household Supplies | Amazon Basics AAA Alkaline High-Performance Batteries, 1.5 Volt, 10-Year Shelf Life, 36 Co

  ID=2671 | 2025-07-16 | $138.54
    - [Me] Home Spending:Household Supplies | Marblelife InterCare Marble and Travertine, Natural Stone & Terrazzo Liquid Cleaner, Showe
    - [Me] Health & Wellness:Supplements & Vitamins | Best Naturals Alpha Lipoic Acid 300 mg 120 Capsules
    - [Me] Health & Wellness:Supplements & Vitamins | Bronson Rhodiola Rosea Vegetarian Capsules - Adaptogenic Herb - Brain, Stress & Mood Suppo
    - [Me] Health & Wellness:Supplements & Vitamins | Organic Lions Mane Mushroom Capsules (120 Count), for Brain Support and Immune Health, Thi
    - [Me] Health & Wellness:Supplements & Vitamins | 88 Acres Granola Bars | Double Dark Chocolate | Gluten Free, Nut-Free Oat and Seed Snack B

  ID=1971 | 2025-12-28 | $379.20
    - [Me] Home Spending:Household Supplies | CHAUVET DJ HURRICANE HAZE 1DX (HHAZE (HHAZE 1DX)
    - [Me] Electronics & Tech | Nintendo Neon Purple/Neon Orange Joy-Con (L-R) - Switch
    - [Me] Home Spending:Household Supplies | Chauvet DJ High Performance Haze Fluid (HFG)


I've got a clear picture now. Here's the full breakdown:

---

## Amazon Classification Results

**52 of 116 matched** to orders in the DB. 64 didn't find a match (those need a re-scrape or manual lookup — more on that below).

---

### ✅ High Confidence — Ready to Post (40 rows, ~$1,400)

No questions needed. Here's a sample, all look clean:

| Date | Amount | Item(s) | Category | Class |
|---|---|---|---|---|
| Feb 20 | $12.72 | Vicks VapoInhaler | Personal Care | Me |
| Feb 24 | $25.79 | Cat carrier (for Gram) | Household Supplies | KandM |
| Mar 1 | $15.88 | Vitamin B Complex | Supplements | Me |
| Mar 28 | $44.95 | nutpods creamer | Supplements | Me |
| Mar 30 | $60.51 | Omega 3 fish oil | Supplements | Me |
| Apr 6 | $52.02 | Dish sponges | Household Supplies | KandM |
| May 8 | $21.20 | L-Theanine | Supplements | Me |
| Jul 16 | $78.09 | Creatine + sponges + pens | Household Supplies | KandM |
| Aug 19 | $24.65 | Lion's Mane | Supplements | Me |
| Sep 23 | $19.64 + $32.89 | AA/C/D batteries | Household Supplies | KandM |
| Nov 16 | $27.76 | Book: "What We Can Know" | Books & Media | Me |

Also: furnace filter ($127.43 → Supplements? **See Q1 below**), freezer ($387.92 → Home Improvement), medicine ball ($113.66 → Fitness), thermostat ($121.60 → Home Improvement), paintball goggle ($223.07 → Fitness), Dexcom CGM ($318.70 → Electronics)

---

### ❓ Questions for You (8 matched rows)

**Q1 — Feb 28, $127.43 — Furnace filters (Filtrete 20x25x4, 2-pack)**
I classified this as Supplements by accident — it's clearly a furnace filter. Should be: **Home Spending:Home Improvement** or **Household Supplies**, class **KandM** or **Bennett**? Which house?

**Q2 — Mar 6, $27.61 — 100 Blank Gift Certificates (for business/salon)**
I flagged this as Gifts — but these look like business gift certs (spa/salon/restaurant). Is this **Forge** expense or personal? Could also be Bennett property.

**Q3 — Jul 16, $138.54 — Mixed order** (Marble cleaner + 4 supplements + granola bars)
All class Me. Marble cleaner → Household Supplies. Supplements → Supplements. Granola → Groceries. **Needs a 3-way split.** Rough split: ~$17 cleaning, ~$100 supplements, ~$21 granola. Want me to split it that way?

**Q4 — Dec 17, $23.36 — Typewriter Lego Building Set**
"Ideas Cute Building Toys for Girls 8-14." Is this a gift for one of your kids (Cassandra? Marky?), or did you buy it for yourself? → **Kids/Cassandra** vs **Me**

**Q5 — Dec 17, $50 — AMC Theatres Gift Card**
Who's this for? → Category = **Gifts & Donations**, but class depends on recipient (Me, K, Kids, Family?)

**Q6 — Dec 21, $80.99 — Squishmallow + board game + burrito blanket + women's scarf**
Looks like a gift bundle. Squishmallow/game → Kids. Women's scarf → Krista? Should I split this 3 ways or just class it **KandM / Gifts**?

**Q7 — Dec 28, $379.20 — Chauvet DJ Hazer + Nintendo Joy-Con + Haze Fluid**
Big mixed order. Hazer + fluid = **Entertainment** (party machine?). Joy-Con = **Electronics/Kids**? What class for the hazer — is that for home parties, Forge events, or Bennett?

**Q8 — Apr 27, $177.01 — Mixed order** (Lab beaker + magnesium + batteries + Maldon salt + batteries)
Beaker is unusual — lab equipment? Is that for a specific use (home experiment, Forge, Bennett)? Rest is clearly Me/KandM.

---

### ❌ 64 Rows With No DB Match (~$7,500)

These are mostly **Aug–Dec 2025** and include some big ones ($514, $457, $427). The DB has 92 orders for 2025 but they don't line up. Likely the scrape caught some months better than others.

**My recommendation:** I can try looser matching on these (date-only or partial amount), or we may need a targeted re-scrape for Aug–Nov 2025. Before doing that — do you want me to try pulling from the `amazon_receipts` email table for those months? That might fill in the gaps without needing another login.

---

What's your call on the 8 questions, and want me to dig into the 64 unmatched via email receipts?


Sender (untrusted metadata):
```json
{
  "label": "gateway-client",
  "id": "gateway-client"
}
```

[Mon 2026-03-09 10:09 EDT] [Slot 3 — Project: QBO Bookkeeping Catch-Up]

How could the scraper have missed? I want us to try to scrape again. We'll use 2FA if needed.


Good question — let me check what the scraper actually captured and why it missed Aug–Nov.

sqlite3 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/bookkeeping.db "
-- What months do we actually have in amazon_orders_raw for 2025?
SELECT order_date, COUNT(*) as cnt, MIN(total_amount), MAX(total_amount)
FROM amazon_orders_raw
WHERE order_year = 2025
GROUP BY substr(order_date,1,3)
ORDER BY order_date;
"

ls -la /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders*.py 2>/dev/null
ls -la /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon*.py 2>/dev/null


April 27, 2025|5|10.61|387.92
August 26, 2025|4|15.93|63.75
December 28, 2025|19|0.0|379.2
February 22, 2025|7|9.55|127.43
January 2, 2025|11|12.74|73.11
July 16, 2025|4|17.85|138.54
June 8, 2025|8|28.68|113.66
March 30, 2025|10|7.65|60.51
May 18, 2025|8|8.48|146.43
November 10, 2025|5|27.76|315.57
October 11, 2025|7|10.61|223.07
September 17, 2025|4|19.64|50.95


-rw-------@ 1 bigdaddy  admin   9548 Mar  4 06:06 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders.py
-rw-------@ 1 bigdaddy  admin   5985 Mar  4 06:07 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders2.py
-rw-------@ 1 bigdaddy  admin   8256 Mar  4 06:08 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders3.py
-rw-------@ 1 bigdaddy  admin  14232 Mar  4 06:10 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders4.py
-rw-r--r--@ 1 bigdaddy  admin   8067 Mar  6 16:16 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_browser_loop.py
-rw-r--r--@ 1 bigdaddy  admin   8134 Mar  6 16:13 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_browser_scrape.py
-rw-------@ 1 bigdaddy  admin  25043 Mar  9 10:07 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_classify.py
-rw-r--r--@ 1 bigdaddy  admin   2213 Mar  6 16:16 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_collect.py
-rw-r--r--@ 1 bigdaddy  admin   7879 Mar  6 16:09 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_login_test.py
-rw-r--r--@ 1 bigdaddy  admin  21877 Mar  6 10:36 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_match_fix.py
-rw-r--r--@ 1 bigdaddy  admin  14263 Mar  6 14:51 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_order_scraper.py
-rw-------@ 1 bigdaddy  admin   9548 Mar  4 06:06 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders.py
-rw-------@ 1 bigdaddy  admin   5985 Mar  4 06:07 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders2.py
-rw-------@ 1 bigdaddy  admin   8256 Mar  4 06:08 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders3.py
-rw-------@ 1 bigdaddy  admin  14232 Mar  4 06:10 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_orders4.py
-rw-r--r--@ 1 bigdaddy  admin  35637 Mar  6 10:19 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_receipt_processor.py
-rw-r--r--@ 1 bigdaddy  admin   8534 Mar  6 16:15 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_requests_scrape.py
-rw-r--r--@ 1 bigdaddy  admin   2313 Mar  6 16:14 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_save_orders.py
-rw-------@ 1 bigdaddy  admin   3652 Mar  3 17:14 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_scrape2.py
-rw-r--r--@ 1 bigdaddy  admin   7631 Mar  6 16:28 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_scrape_all.py
-rw-r--r--@ 1 bigdaddy  admin  19450 Mar  6 16:08 /Users/bigdaddy/.openclaw-markbot/workspace/skills/qbo/amazon_scraper.py