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
• get_wishlist — current wishlist with notes on why each wine was added
• refresh_data — force a fresh pull from CellarTracker

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 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)

Parsing Instructions

All CT export CSVs share these characteristics:

• UTF-8 encoded (converted from windows-1252 by export script)
• 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):

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:

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