cellartracker-data

name

cellartracker-data

description

Parse and query CellarTracker data for wine cellar management, inventory analysis, drinking window decisions, purchase history, consumption tracking, wishlist management, and scoring lookups. Uses MCP tools as the primary data source when available, with CSV fallback. Use this skill whenever the user asks about their cellar inventory, wants drinking window guidance, asks about past purchases or consumption history, references their wishlist, needs bottle counts or storage analysis, or asks anything that requires querying their wine collection data. Also trigger when the user says "check my cellar", "what do I have", "what should I drink", "my wishlist", "drinking window", "what have I consumed", "purchase history", or references CellarTracker data. This skill works alongside the wine-purchase-evaluator skill — use both together when evaluating purchases against existing inventory.

CellarTracker Data Parser

This skill enables Claude to parse, query, and cross-reference CellarTracker data for cellar management, purchase decisions, and drinking window guidance.

Data Source

When MCP tools are available (Claude Code or Claude Desktop), use the following tools for live data:

• search-cellar — query current inventory by wine name, region, varietal, producer, etc.
• drinking-recommendations — get maturity-aware suggestions for what to drink
• cellar-stats — overview of cellar composition (counts, categories, regions)
• purchase-history — full buy history with pricing and retailer info
• recent-deliveries — wines actually received in a date range, by delivery date
• incoming-orders — wines ordered but not yet received, from the Pending table
• get-wishlist — current wishlist with notes on why each wine was added
• consumption-history — wines you've opened, with tasting context
• tasting-notes — your tasting notes and reviews with ratings and scores
• bottle-details — individual bottles (cellar + consumed) by wine name, location, bin, size, or barcode
• refresh-data — force a fresh pull from CellarTracker

For bulk or SQL-style analysis (Claude Code), MCP resources expose the same cached CSVs directly — no per-tool result caps, and no need to hunt for the cache path:

• cellartracker://tables/<Table> — the full raw CSV for one of the 8 tables (e.g. cellartracker://tables/List), text/csv, refreshed via the same cache the tools use
• cellartracker://meta/cache — JSON with per-table freshness timestamps and server version, useful to check before treating a resource read as current

When MCP is not available (Claude.ai Projects), look for uploaded CSV files:

• Individual *_latest.csv files (most common)
• Timestamped files like List_20260313_015000.csv
• Or attached directly in conversation

Table Overview

Eight tables are exported from CellarTracker. Not all will always be available — work with what's present.

Table	Primary Use	Key Columns
List	Current cellar inventory	Location, Bin, Price, Valuation, pro scores, drinking window
Notes	Tasting notes	Rating, TastingNotes, TastingDate, CScore
Purchase	Full buy history	StoreName, Price, PurchaseDate, Quantity, Remaining
Consumed	Drinking log	Consumed date, ConsumptionNote, context (who, food, occasion)
Availability	Maturity & pro scores	Drinking windows (multiple sources), all professional scores, maturity curves
Tag	Wishlists & custom lists	ListName, WinesNotes (why it's on the list)
Bottles	Individual bottle records	BottleState, Barcode, per-bottle notes, combines cellar + consumed; use bottle-details
Pending	In-transit orders	Same as Purchase but undelivered; use incoming-orders

Table Priority for Common Tasks

• "What should I drink tonight?" — List (inventory + location) + Availability (maturity) + Notes (past impressions)
• "Evaluate this purchase" — List (redundancy check) + Tag (is it on wishlist?) + Purchase (have I bought this before?)
• "What have I been drinking?" — Consumed (patterns, frequency, notes)
• "Cellar overview / audit" — List (full inventory) + Availability (what's past peak?)
• "How much have I spent?" — Purchase (complete spend history)
• "What's on the way / still coming?" — Pending (in-transit orders not yet received)
• "Where's this bottle? / What's in my wine fridge?" — Bottles via bottle-details (per-bottle location, bin, barcode, state)

Finding a Specific Bottle (bottle-details)

The bottle-details tool queries the Bottles table — individual bottle records spanning both in-cellar and consumed bottles. Use it for barcode lookups, exact bin location, and per-bottle state, which the wine-level List/Availability tables can't reach.

• Barcode from a photo: if the user attaches a photo of a bottle or a barcode, read the barcode digits directly from the image (your own vision — no OCR tooling) and pass them as the barcode filter.
• Natural-language location/bin ("what's in my wine fridge", "the bottom shelf of my cabinet"): CellarTracker Location and Bin values are account-specific labels (e.g. Wine Fridge, Bar Cabinet, Drawer 2, or row-slot codes like 1-3), not physical descriptions. Two-step it: call cellar-stats with group_by=location or group_by=bin first to learn the account's actual vocabulary, then call bottle-details with the exact value. bottle-details returns this same pointer if a location/bin filter finds nothing.
• Bin position is not derivable from the label. A code like 1-3 means row 1, slot 3, but which row is physically "top" vs "bottom" depends on the user's actual setup — if position is ambiguous after discovery, ask rather than guessing.

Parsing Instructions

All CT export CSVs share these characteristics:

• UTF-8 encoded (charset detected from CellarTracker's response headers, falling back to windows-1252)
• Quoted fields, comma-delimited
• First row is always headers
• iWine is the universal join key across all tables
• NV (non-vintage) wines use vintage 1001

Reading CSVs

import csv
with open('List_latest.csv') as f:
    reader = csv.DictReader(f)
    rows = list(reader)

Key Join Pattern

Cross-reference tables using iWine:

# Example: Match inventory to availability/maturity data
list_by_wine = {row['iWine']: row for row in list_rows}
for avail_row in availability_rows:
    cellar_row = list_by_wine.get(avail_row['iWine'])
    if cellar_row:
        # Now have both inventory location AND maturity data

Column Reference

For full column schemas for all 8 tables, see the schema reference file in references/schema.md.

Load the schema reference when you need to:

• Look up specific column names or meanings
• Decode professional reviewer abbreviations (WA, WS, AG, etc.)
• Understand maturity curve fields (Available, Bell, Linear, etc.)
• Parse drinking window source hierarchy

Professional Score Abbreviations

The most commonly relevant scores (full list in schema reference):

Code	Reviewer
WA	Wine Advocate (Robert Parker / successors)
WS	Wine Spectator
AG	Antonio Galloni (Vinous)
JR	Jancis Robinson
WE	Wine Enthusiast
BH	Burghound (Allen Meadows)
JS	James Suckling
CT	CellarTracker community average
MY	User's personal score

Score display rules:

• CT community scores are often decimals (e.g., 88.7777...) — round to 1 decimal
• MY is the user's personal rating — always flag when present
• Empty string = no score available, not zero
• In Availability table, scores also have Web (link) and Sort (numeric) variants

Drinking Window Logic

The Availability table provides the richest maturity data. Key fields:

• BeginConsume / EndConsume: Consensus or personal drinking window (date format: M/D/YYYY or YYYY)
• Source: Where the window comes from — Personal, Community, or a professional reviewer name
• Available: Maturity percentage (0-1 = approaching peak, ~1 = at peak, >1 = past peak)
• Bell / Linear / Early / Late / Fast / TwinPeak / Simple: Different maturity curve models

Maturity interpretation:

• Available < 0.3 — Too young, needs significant time
• Available 0.3-0.7 — Approaching window, can open with decanting
• Available 0.7-1.0 — In window, good to drink
• Available > 1.0 — Past peak or at tail end of window

Window source priority: Personal > Professional reviewer > Community

The List table also has BeginConsume/EndConsume as year integers — use these as quick reference, Availability for detail.

Location & Storage Mapping

The List table's Location and Bin fields map to physical storage. These are example locations — your CellarTracker locations will vary based on your setup:

Location value	Physical space	Notes
Wine Fridge	Dual-zone fridge	Bin format: row-position (e.g., 1-3 = row 1, slot 3)
Bar Cabinet	Dark cabinet storage	Bin: Drawer 1, Drawer 2, Shelf Rack
Rack	Floor racks	Usually no bin specified
Boxed	Still in shipping box	Overflow / recently arrived
Cellar	Generic / unspecified	May need location update

Common Query Patterns

Bottle Count & Capacity

# Active cellar size (exclude pending)
cellar_count = sum(int(row['Quantity']) for row in list_rows)

Report against your cellar capacity targets when known.

Category Breakdown

# Group by color, region, varietal, etc.
from collections import Counter
by_color = Counter(row['Color'] for row in list_rows)
by_region = Counter(row['Region'] for row in list_rows)
by_varietal = Counter(row['MasterVarietal'] for row in list_rows)

Redundancy Check (for purchase evaluator)

When checking if a new wine would be redundant:

1. Same producer? — Flag
2. Same varietal + region? — Count existing
3. Same broad style (e.g., "California Chardonnay")? — Count existing
4. On wishlist? — Note as positive signal

Drinking Priority

Combine List + Availability to find what should be opened soon:

• Available > 1.0 — Past peak, drink ASAP
• EndConsume year <= current year — Window closing
• Location = Rack — Already in drink-soon storage

Spend Analysis

Purchase table tracks all historical buys:

• Price = cost per bottle in USD
• Remaining vs Quantity shows consumption rate
• StoreName tracks where bottles were sourced
• OrderNumber sometimes has context (e.g., "Gift from Liz")

Wishlist Cross-Reference

Tag table with ListName = *Wishlist:

• WinesNotes often has context on why it was added (e.g., "Reddit QPR white burgundy", "Konstantin Baum best wines of 2025")
• Use during purchase evaluation: if a wine is on the wishlist, it's a positive signal

Integration with Other Skills

wine-purchase-evaluator

When evaluating purchases, this skill provides:

• Redundancy data from inventory (same producer, varietal, region) — use search-cellar MCP tool or List CSV
• Wishlist match from wishlists — use get-wishlist MCP tool or Tag CSV
• Historical pricing from purchase history — use purchase-history MCP tool or Purchase CSV
• Consumption velocity from consumption log — use Consumed CSV

General wine advisory

• "What should I drink with X?" — Query inventory for available bottles matching the pairing need, check maturity
• "Tell me about my cellar" — Full inventory analysis with category breakdown, maturity overview, valuation summary
• "What's past its peak?" — Find wines where Available > 1.0

Output Style

When presenting cellar data:

• Round CT community scores to 1 decimal place
• Format valuations as USD with 2 decimal places
• Use drinking window as year range (e.g., "2025-2030"), not full dates
• Flag wines past peak with a clear indicator
• When listing inventory, include Location for actionability
• Keep tables tight — don't dump all columns, select what's relevant to the query