task_116Retail trade

Arguments

title

Chapter 1: Research and Data Extraction

summary

We are starting our analysis of the prepared-foods reset proposal. We will read the merchant handoff docx, vendor offer letters pdf, and other csv data files to establish the business rules, unit economics, capacity constraints, and historical performance. This will serve as the foundation for our recommendation and supporting workbook.

strategic_intent

Initialize the analysis by reviewing the input files to extract the business rules, item details, vendor offers, capacity, and historical sales.

Output

Current topic: "Chapter 1: Research and Data Extraction"
Topic summary: We are starting our analysis of the prepared-foods reset proposal. We will read the merchant handoff docx, vendor offer letters pdf, and other csv data files to establish the business rules, unit economics, capacity constraints, and historical performance. This will serve as the foundation for our recommendation and supporting workbook.

Strategic Intent: Initialize the analysis by reviewing the input files to extract the business rules, item details, vendor offers, capacity, and historical sales.

Arguments

docx

Output

<activated_skill name="docx">
  <instructions>
    # DOCX creation, editing, and analysis

## Overview

A .docx file is a ZIP archive containing XML files.

## Quick Reference

| Task | Approach |
|------|----------|
| Read/analyze content | `pandoc` or unpack for raw XML |
| Create new document | Use `docx-js` - see Creating New Documents below |
| Edit existing document | Unpack → edit XML → repack - see Editing Existing Documents below |

### Converting .doc to .docx

Legacy `.doc` files must be converted before editing:

```bash
python scripts/office/soffice.py --headless --convert-to docx document.doc
```

### Reading Content

```bash
# Text extraction with tracked changes
pandoc --track-changes=all document.docx -o output.md

# Raw XML access
python scripts/office/unpack.py document.docx unpacked/
```

### Converting to Images

```bash
python scripts/office/soffice.py --headless --convert-to pdf document.docx
pdftoppm -jpeg -r 150 document.pdf page
```

### Accepting Tracked Chan

… 20,839 more characters of output in the raw trajectory.

Arguments

pdf

Output

<activated_skill name="pdf">
  <instructions>
    # PDF Processing Guide

## Overview

This guide covers essential PDF processing operations using Python libraries and command-line tools. For advanced features, JavaScript libraries, and detailed examples, see REFERENCE.md. If you need to fill out a PDF form, read FORMS.md and follow its instructions.

## Quick Start

```python
from pypdf import PdfReader, PdfWriter

# Read a PDF
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")

# Extract text
text = ""
for page in reader.pages:
    text += page.extract_text()
```

## Python Libraries

### pypdf - Basic Operations

#### Merge PDFs
```python
from pypdf import PdfWriter, PdfReader

writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
    reader = PdfReader(pdf_file)
    for page in reader.pages:
        writer.add_page(page)

with open("merged.pdf", "wb") as output:
    writer.write(output)
```

#### Split PDF
```python
reader = PdfReader("inpu

… 7,070 more characters of output in the raw trajectory.

Arguments

xlsx

Output

<activated_skill name="xlsx">
  <instructions>
    # Requirements for Outputs

## All Excel files

### Professional Font
- Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user

### Zero Formula Errors
- Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)

### Preserve Existing Templates (when updating templates)
- Study and EXACTLY match existing format, style, and conventions when modifying files
- Never impose standardized formatting on files with established patterns
- Existing template conventions ALWAYS override these guidelines

## Financial models

### Color Coding Standards
Unless otherwise stated by the user or existing template

#### Industry-Standard Color Conventions
- **Blue text (RGB: 0,0,255)**: Hardcoded inputs, and numbers users will change for scenarios
- **Black text (RGB: 0,0,0)**: ALL formulas and calculations
- **Green text (RGB: 0,128,0)**: 

… 11,871 more characters of output in the raw trajectory.

Arguments

Convert the merchant handoff docx file to Markdown for easy reading.

pandoc --track-changes=all /workspace/data/merchant_handoff_prepared_foods.docx -o /workspace/data/merchant_handoff_prepared_foods.md

Output

<untrusted_context>
Output: /usr/bin/bash: line 4: pandoc: command not found
Exit Code: 127
Process Group PGID: 1503
</untrusted_context>

Arguments

Inspect the columns of CSV files and check the availability of python libraries.

python -c "
import os, csv
import pandas as pd

print('CSV Files in data directory:')
for f in os.listdir('/workspace/data'):
    if f.endswith('.csv'):
        path = os.path.join('/workspace/data', f)
        df = pd.read_csv(path)
        print(f'- {f}: {len(df)} rows, columns: {list(df.columns)}')

print('\nChecking library availability:')
for lib in ['docx', 'pypdf', 'pdfplumber', 'reportlab', 'openpyxl']:
    try:
        __import__(lib)
        print(f'- {lib} is

… 87 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: CSV Files in data directory:
- buyer_reset_proposal.csv: 50 rows, columns: ['proposal_date', 'cluster_code', 'cluster_name', 'sku', 'item', 'proposed_action', 'proposed_facings', 'buyer_comment', 'supplier_projection_units_per_store_week']
- item_master_unit_economics.csv: 10 rows, columns: ['sku', 'item', 'status', 'vendor', 'role', 'diet_flag', 'shelf_life_days', 'case_pack', 'tray_slots_per_facing', 'retail_price', 'unit_cost', 'order_multiple_cases', 'first_available_date', 'current_core']
- store_cluster_capacity.csv: 5 rows, columns: ['cluster_code', 'cluster_name', 'store_count', 'deliveries_per_week', 'tray_slots_available', 'extra_tray_project', 'notes']
- weekly_pos_inventory_waste.csv: 368 rows, columns: ['week_start', 'cluster_code', 'cluster_name', 'sku', 'item', 'range_type', 'stores_ranged', 'net_sales_units', 'waste_units', 'oos_store_days', 'receipts_units', 'ending_on_hand_units']

Checking library availability:
- docx is available
- pypdf 

… 137 more characters of output in the raw trajectory.

Arguments

Extract text and tables from the merchant handoff docx file and save as text.

python -c "
import docx

doc = docx.Document('/workspace/data/merchant_handoff_prepared_foods.docx')
out_path = '/workspace/data/merchant_handoff_prepared_foods.txt'

with open(out_path, 'w', encoding='utf-8') as f:
    f.write('=== PARAGRAPHS ===\n')
    for p in doc.paragraphs:
        if p.text.strip():
            f.write(p.text + '\n\n')
            
    f.write('\n=== TABLES ===\n')
    for i, table in enumerate(doc.tables):
        f.write(f'\nTable {i+1}:\n')

… 232 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Successfully extracted docx contents to /workspace/data/merchant_handoff_prepared_foods.txt
Process Group PGID: 1518
</untrusted_context>

Arguments

python -c "
import pdfplumber

with pdfplumber.open('/workspace/data/signed_vendor_offer_letters.pdf') as pdf:
    with open('/workspace/data/signed_vendor_offer_letters.txt', 'w', encoding='utf-8') as f:
        for i, page in enumerate(pdf.pages):
            f.write(f'=== PAGE {i+1} ===\n')
            text = page.extract_text()
            f.write(text + '\n\n')
            
            # Extract tables if any
            tables = page.extract_tables()
            if tables:
                f.write('--- TABLES ---\n')
                for t in tables:

… 444 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Successfully extracted PDF contents to /workspace/data/signed_vendor_offer_letters.txt
Process Group PGID: 1521
</untrusted_context>

Arguments

file_path

/workspace/data/merchant_handoff_prepared_foods.txt

end_line

500

start_line

1

Output

=== PARAGRAPHS ===
Prepared Foods Reset Handoff

Blue Mesa Market - Q3 2026 refrigerated grab-and-go case

From: L. Moreno, Senior Merchant - Prepared Foods

To: Category Planning / Space Planning

Date: June 10, 2026

Context: The July reset has to protect lunch traffic while taking shrink dollars out of the case. The buyer proposal was assembled after vendor line reviews, but it has not been reconciled to current tray capacity, delivery cadence, or the signed trade terms. Stores are already flagging that the case is hard to recover after the noon rush, so the final call needs to be operationally boring even if the vendor story is exciting.

Decision rules for this reset

Fit the first four weeks of the July reset into the live refrigerated case. Do not assume the September lease-line change, extra trays, or extra driver stops will materialize.

Treat internal sales, waste, and inventory records as the demand signal. Supplier projections in the proposal are not an accepted demand sour

… 2,371 more characters of output in the raw trajectory.

Arguments

start_line

1

end_line

500

file_path

/workspace/data/signed_vendor_offer_letters.txt

Output

=== PAGE 1 ===
Signed Vendor Offer Letters - Prepared Foods Q3 2026
Blue Mesa Market internal copy. These are the signed terms that supersede informal proposal notes.
GreenFork Kitchen
Signed June 5, 2026 by M. Patel, GreenFork Kitchen
Term Signed language
Covered items GGS-5MTC Hummus Veggie Wrap; GGS-3RZA Caprese Ciabatta
Launch support $0.40 per scanned unit on GGS-3RZA receipts dated July 13-August 9, capped at 5,500 scanned units.
Shrink credit 30% credit on documented waste units for covered GreenFork items only in stores receiving GreenFork delivery at
least four days per week.
Route limits Highway / Travel and Rural North are not approved for Caprese route expansion under this letter. Future expansion
requires a new signed routing addendum.
Service note GreenFork products carry a two-day code at store receipt.
Urban Spoon
Signed June 6, 2026 by T. Nguyen, Urban Spoon
Term Signed language
Covered items GGS-9PQL Southwest Chicken Bowl; GGS-6BGH Protein Snack Box
Launch support $0

… 3,963 more characters of output in the raw trajectory.

Arguments

/workspace/data/store_cluster_capacity.csv

Output

cluster_code,cluster_name,store_count,deliveries_per_week,tray_slots_available,extra_tray_project,notes
MET,Metro Core,18,5,12.0,Pending September lease-line change; not approved for July reset,Dense lunch traffic; daily prepared-food replenishment except Sunday.
COM,Commuter Suburban,26,4,10.0,No incremental tray capacity this reset,Office parks and commuter corridors; strongest weekday lunch.
CAM,Campus / Hospital,11,5,10.0,No incremental tray capacity this reset,Late lunch and evening snack missions; meatless options over-index.
HWY,Highway / Travel,15,3,8.0,No incremental tray capacity this reset,Fewer delivery touches; travel purchase pattern favors familiar sandwiches.
RUR,Rural North,14,2,7.0,No incremental tray capacity this reset,Long delivery gap and highest transfer cost; do not chase low-repeat novelty.

Arguments

/workspace/data/item_master_unit_economics.csv

Output

sku,item,status,vendor,role,diet_flag,shelf_life_days,case_pack,tray_slots_per_facing,retail_price,unit_cost,order_multiple_cases,first_available_date,current_core
GGS-84QJ,Chicken Caesar Wrap,Current,EatWell Foods,Core wrap,Meat,3,12,1.0,5.99,2.38,1,2024-03-18,Yes
GGS-2VWK,Turkey Club Sandwich,Current,EatWell Foods,Core sandwich,Meat,3,10,1.0,6.49,2.64,1,2023-08-07,Yes
GGS-9PQL,Southwest Chicken Bowl,Current,Urban Spoon,Bowl meal,Meat,3,8,1.5,7.99,3.45,1,2024-09-09,Yes
GGS-5MTC,Hummus Veggie Wrap,Current,GreenFork Kitchen,Vegetarian lunch,Vegetarian,2,8,1.0,6.29,2.75,1,2023-05-01,No
GGS-7DNL,Cobb Salad,Current,FreshPack Deli,Salad meal,Meat,3,6,1.5,8.49,3.72,1,2024-01-22,No
GGS-1KFV,Egg Salad Croissant,Current,FreshPack Deli,Legacy sandwich,Egg vegetarian,2,8,1.0,5.49,2.28,1,2022-06-13,No
GGS-4HBP,Turkey Cranberry Sandwich,Current seasonal,EatWell Foods,Winter seasonal,Meat,3,10,1.0,6.59,2.95,1,2025-10-27,No
GGS-3RZA,Caprese Ciabatta,Candidate,GreenFork Kitchen,Vegetarian lunch,Vegeta

… 280 more characters of output in the raw trajectory.

Arguments

List all rows and columns in the buyer reset proposal CSV.

python -c "
import pandas as pd
df = pd.read_csv('/workspace/data/buyer_reset_proposal.csv')
print(df.to_string())
"

Output

<untrusted_context>
Output: proposal_date cluster_code       cluster_name       sku                       item proposed_action  proposed_facings                                                                                    buyer_comment  supplier_projection_units_per_store_week
0     2026-06-12          MET         Metro Core  GGS-84QJ        Chicken Caesar Wrap            Keep                 2                                                                                              NaN                                       NaN
1     2026-06-12          MET         Metro Core  GGS-2VWK       Turkey Club Sandwich            Keep                 1                                                                                              NaN                                       NaN
2     2026-06-12          MET         Metro Core  GGS-9PQL     Southwest Chicken Bowl            Keep                 1                                                                               

… 12,279 more characters of output in the raw trajectory.

Arguments

Analyze historical sales, waste, and out of stock data by SKU.

python -c "
import pandas as pd
df = pd.read_csv('/workspace/data/weekly_pos_inventory_waste.csv')
print('Unique weeks in data:', df['week_start'].unique())
print('Unique SKUs in data:', df['sku'].unique())
print('Unique range types:', df['range_type'].unique())
print('\nAggregated metrics by SKU:')
agg = df.groupby('sku').agg({
    'net_sales_units': 'sum',
    'waste_units': 'sum',
    'oos_store_days': 'sum',
    'receipts_units': 'sum',
    'ending_on_hand_units': 'sum'
}).reset_index(

… 19 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Unique weeks in data: ['2026-04-06' '2026-04-13' '2026-04-20' '2026-04-27' '2026-05-04'
 '2026-05-11' '2026-05-18' '2026-05-25']
Unique SKUs in data: ['GGS-84QJ' 'GGS-2VWK' 'GGS-9PQL' 'GGS-5MTC' 'GGS-7DNL' 'GGS-1KFV'
 'GGS-4HBP' 'GGS-3RZA' 'GGS-6BGH' 'GGS-8XUY']
Unique range types: ['Current set' 'Controlled pilot']

Aggregated metrics by SKU:
        sku  net_sales_units  ...  receipts_units  ending_on_hand_units
0  GGS-1KFV              746  ...             967                  1188
1  GGS-2VWK             2688  ...            2796                  1008
2  GGS-3RZA              477  ...             551                   321
3  GGS-4HBP              608  ...             802                  1185
4  GGS-5MTC             1377  ...            1546                  1031
5  GGS-6BGH              938  ...            1022                   545
6  GGS-7DNL             1458  ...            1637                   991
7  GGS-84QJ             3393  ...            3568 

… 234 more characters of output in the raw trajectory.

Arguments

Analyze range types and distribution across clusters for each SKU.

python -c "
import pandas as pd
df = pd.read_csv('/workspace/data/weekly_pos_inventory_waste.csv')
print(df.groupby(['sku', 'item', 'range_type']).agg({
    'cluster_code': 'nunique',
    'stores_ranged': 'sum',
    'net_sales_units': 'sum',
    'waste_units': 'sum'
}).reset_index())
"

Output

<untrusted_context>
Output: sku                       item  ... net_sales_units  waste_units
0  GGS-1KFV        Egg Salad Croissant  ...             746          195
1  GGS-2VWK       Turkey Club Sandwich  ...            2688          118
2  GGS-3RZA           Caprese Ciabatta  ...             477           53
3  GGS-4HBP  Turkey Cranberry Sandwich  ...             608          170
4  GGS-5MTC         Hummus Veggie Wrap  ...            1377          162
5  GGS-6BGH          Protein Snack Box  ...             938           38
6  GGS-7DNL                 Cobb Salad  ...            1458          168
7  GGS-84QJ        Chicken Caesar Wrap  ...            3393          158
8  GGS-8XUY      Spicy Tuna Sushi Roll  ...             349           77
9  GGS-9PQL     Southwest Chicken Bowl  ...            2111          195

[10 rows x 7 columns]
Process Group PGID: 1550
</untrusted_context>

Arguments

description

Inspect the weekly sales, waste, and store count for each SKU by cluster and range type.

command

python -c "
import pandas as pd
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 1000)
df = pd.read_csv('/workspace/data/weekly_pos_inventory_waste.csv')
print(df.groupby(['sku', 'item', 'range_type', 'cluster_code']).agg({
    'week_start': 'count',
    'stores_ranged': 'max', # store count in that cluster
    'net_sales_units': 'sum',
    'waste_units': 'sum',
    'oos_store_days': 'sum'
}).reset_index())
"

Output

<untrusted_context>
Output: sku                       item        range_type cluster_code  week_start  stores_ranged  net_sales_units  waste_units  oos_store_days
0   GGS-1KFV        Egg Salad Croissant       Current set          CAM           8             11              138           32             4.6
1   GGS-1KFV        Egg Salad Croissant       Current set          COM           8             26              246           62            10.2
2   GGS-1KFV        Egg Salad Croissant       Current set          HWY           8             15               95           26             5.6
3   GGS-1KFV        Egg Salad Croissant       Current set          MET           8             18              186           49             8.4
4   GGS-1KFV        Egg Salad Croissant       Current set          RUR           8             14               81           26             5.0
5   GGS-2VWK       Turkey Club Sandwich       Current set          CAM           8             11              306   

… 5,832 more characters of output in the raw trajectory.

Arguments

Verify if there is any more content in the merchant handoff text file.

python -c "
with open('/workspace/data/merchant_handoff_prepared_foods.txt') as f:
    lines = f.readlines()
print('Total lines in file:', len(lines))
if len(lines) > 500:
    print(''.join(lines[500:1000]))
else:
    print('Already read the whole file!')
"

Output

<untrusted_context>
Output: Total lines in file: 46
Already read the whole file!
Process Group PGID: 1574
</untrusted_context>

Arguments

M-11

Output

Found 4 matches for pattern "M-11" across 4 workspace directories:
---
File: workspace/data/signed_vendor_offer_letters.txt
L43: Launch support Metro Core M-11 stores only: 50% credit on documented waste units through August 16. No scan allowance.
L44: Route limits Daily fresh route and credit apply only to Metro Core stores listed on route M-11. Campus and Commuter pilot samples
L60: Launch support | Metro Core M-11 stores only: 50% credit on documented waste units through August 16. No scan allowance.
L61: Route limits | Daily fresh route and credit apply only to Metro Core stores listed on route M-11. Campus and Commuter pilot samples were line-review tests and are not part of the signed launch commitment.
---

Arguments

/workspace/

Output

Directory listing for /workspace:
[DIR] data

Arguments

Calculate sales and waste rates per store-week for each SKU and cluster.

python -c "
import pandas as pd
df = pd.read_csv('/workspace/data/weekly_pos_inventory_waste.csv')
# For each SKU and cluster, calculate total net sales, total waste, total store-weeks
df['store_weeks'] = df['stores_ranged']
summary = df.groupby(['sku', 'item', 'cluster_code', 'range_type']).agg({
    'store_weeks': 'sum',
    'net_sales_units': 'sum',
    'waste_units': 'sum',
    'oos_store_days': 'sum'
}).reset_index()

summary['sales_per_store_week'] = summary['net_sales_units'

… 283 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: sku                       item cluster_code        range_type  store_weeks  net_sales_units  waste_units  oos_store_days  sales_per_store_week  waste_per_store_week  waste_rate
0   GGS-1KFV        Egg Salad Croissant          CAM       Current set           88              138           32             4.6              1.568182              0.363636    0.188235
1   GGS-1KFV        Egg Salad Croissant          COM       Current set          208              246           62            10.2              1.182692              0.298077    0.201299
2   GGS-1KFV        Egg Salad Croissant          HWY       Current set          120               95           26             5.6              0.791667              0.216667    0.214876
3   GGS-1KFV        Egg Salad Croissant          MET       Current set          144              186           49             8.4              1.291667              0.340278    0.208511
4   GGS-1KFV        Egg Salad Croissant          RU

… 7,806 more characters of output in the raw trajectory.

Arguments

Check if receipts equal sales plus waste in the historical data.

python -c "
import pandas as pd
df = pd.read_csv('/workspace/data/weekly_pos_inventory_waste.csv')
df['calc_receipts'] = df['net_sales_units'] + df['waste_units']
diff = df['receipts_units'] - df['calc_receipts']
print('Receipts vs Sales + Waste difference stats:')
print(diff.describe())
"

Output

<untrusted_context>
Output: Receipts vs Sales + Waste difference stats:
count    368.000000
mean       0.567935
std        2.311152
min       -3.000000
25%       -1.000000
50%        1.000000
75%        2.000000
max        4.000000
dtype: float64
Process Group PGID: 1597
</untrusted_context>

Arguments

cat << 'EOF' > /workspace/data/analyze_scenarios.py
import pandas as pd
import numpy as np

# Load files
proposal = pd.read_csv('/workspace/data/buyer_reset_proposal.csv')
economics = pd.read_csv('/workspace/data/item_master_unit_economics.csv')
capacity = pd.read_csv('/workspace/data/store_cluster_capacity.csv')
history = pd.read_csv('/workspace/data/weekly_pos_inventory_waste.csv')

# Calculate historical weekly rates per store for each (sku, cluster_code)
history['store_weeks'] = history['stores_ranged']
hist_rates = history.groupby(['sku', 'item', 'cluster_code', 'r

… 548 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Historical Rates Calculated.
Process Group PGID: 1607
</untrusted_context>

Arguments

cat << 'EOF' > /workspace/data/run_projections.py
import pandas as pd
import numpy as np

# Load raw files
proposal = pd.read_csv('/workspace/data/buyer_reset_proposal.csv')
economics = pd.read_csv('/workspace/data/item_master_unit_economics.csv')
capacity = pd.read_csv('/workspace/data/store_cluster_capacity.csv')
history = pd.read_csv('/workspace/data/weekly_pos_inventory_waste.csv')

# Determine historical weekly rates per store
history['store_weeks'] = history['stores_ranged']
rates = history.groupby(['sku', 'item', 'cluster_code']).agg({
    'store_weeks': 'sum',

… 865 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Historical data parsed. Setup completed.
Process Group PGID: 1618
</untrusted_context>

Arguments

cat << 'EOF' > /workspace/data/run_projections.py
import pandas as pd
import numpy as np

# Load raw files
proposal = pd.read_csv('/workspace/data/buyer_reset_proposal.csv')
economics = pd.read_csv('/workspace/data/item_master_unit_economics.csv')
capacity = pd.read_csv('/workspace/data/store_cluster_capacity.csv')
history = pd.read_csv('/workspace/data/weekly_pos_inventory_waste.csv')

# Determine historical weekly rates per store
history['store_weeks'] = history['stores_ranged']
rates = history.groupby(['sku', 'item', 'cluster_code']).agg({
    'store_weeks': 'sum',

… 8,777 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: --- Scenario: Buyer's Proposal ---
Cluster    SKU          Item                           Facings  Sales (4w)   Waste (4w)   Revenue ($)  COGS ($)     Shrink ($)   Scan Cr ($)  Waste Cr ($) Net Prof ($)
MET        GGS-84QJ     Chicken Caesar Wrap            2        396.0        20.5         2372.04      942.48       48.79        0.00         0.00         1380.77     
MET        GGS-2VWK     Turkey Club Sandwich           1        222.5        9.0          1444.03      587.40       23.76        0.00         0.00         832.87      
MET        GGS-9PQL     Southwest Chicken Bowl         1        325.0        23.0         2596.75      1121.25      79.35        0.00         0.00         1396.15     
MET        GGS-7DNL     Cobb Salad                     1        217.5        17.5         1846.58      809.10       65.10        0.00         0.00         972.37      
MET        GGS-3RZA     Caprese Ciabatta               1        228.0        21.0         1593.72

… 16,646 more characters of output in the raw trajectory.

Arguments

Initialize the create_workbook.py script with openpyxl setup and styles.

mkdir -p /workspace/output
cat << 'EOF' > /workspace/data/create_workbook.py
import openpyxl
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter

wb = openpyxl.Workbook()

# Style helpers
font_name = 'Arial'
font_regular = Font(name=font_name, size=10, bold=False, color='000000')
font_bold = Font(name=font_name, size=10, bold=True, color='000000')
font_header = Font(name=font_name, size=11, bold=True, color='FFFFFF')
f

… 2,307 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Sheets initialized and styles setup.
Process Group PGID: 1640
</untrusted_context>

Arguments

cat << 'EOF' >> /workspace/data/create_workbook.py

# Write Item Master
item_rows = [
    ["SKU", "Item Name", "Status", "Vendor", "Role", "Diet Flag", "Shelf Life (Days)", "Case Pack", "Tray Slots / Facing", "Retail Price", "Unit Cost", "Order Multiple (Cases)", "First Available", "Current Core"],
    ["GGS-84QJ", "Chicken Caesar Wrap", "Current", "EatWell Foods", "Core wrap", "Meat", 3, 12, 1.0, 5.99, 2.38, 1, "2024-03-18", "Yes"],
    ["GGS-2VWK", "Turkey Club Sandwich", "Current", "EatWell Foods", "Core sandwich", "Me

… 4,147 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Sheets initialized and styles setup.
Reference sheets populated and formatted.
Process Group PGID: 1654
</untrusted_context>

Arguments

cat << 'EOF' >> /workspace/data/create_workbook.py

# Write Historical Rates
hist_headers = ["Key", "Cluster Code", "SKU", "Sales Rate (per store-week)", "Waste Rate (per store-week)"]

# Key rates dictionary to write
rates_data = [
    # MET
    ["MET", "GGS-84QJ", 5.500000, 0.284722],
    ["MET", "GGS-2VWK", 3.090278, 0.125000],
    ["MET", "GGS-9PQL", 4.513889, 0.319444],
    ["MET", "GGS-5MTC", 1.958333, 0.166667],
    ["MET", "GGS-7DNL", 3.020833, 0.243056],
    ["MET", "GGS-1KFV", 1.291667, 0.340278],
    ["MET", "GGS-4HBP",

… 3,851 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Sheets initialized and styles setup.
Reference sheets populated and formatted.
Historical Rates worksheet populated.
Process Group PGID: 1667
</untrusted_context>

Arguments

cat << 'EOF' >> /workspace/data/create_workbook.py

def populate_projection_sheet(ws, facings_dict):
    # Headers
    headers = [
        "Cluster Code", "Cluster Name", "SKU", "Item Name", "Vendor", 
        "Retail Price", "Unit Cost", "Tray Slots / Facing", "Store Count", "Deliveries / Week", 
        "Facings", "Slots Used", "Sales Rate", "Waste Rate", 
        "Sales Units (4-Week)", "Waste Units (4-Week)", "Gross Revenue", "COGS", 
        "Waste Cost", "Scan Credits", "Waste Credits", "Net Profit"
    ]
    
    # Writ

… 9,132 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Sheets initialized and styles setup.
Reference sheets populated and formatted.
Historical Rates worksheet populated.
Projections sheets populated with formulas.
Process Group PGID: 1680
</untrusted_context>

Arguments

cat << 'EOF' >> /workspace/data/create_workbook.py

# Write Assortment Plan worksheet
ws_plan.cell(row=1, column=1, value="Grab-and-Go Case Assortment Plan - Comparison & Decisions").font = font_title

# Header merge
ws_plan.merge_cells("A2:D2")
ws_plan.cell(row=2, column=1, value="Item Specifications").font = font_header
ws_plan.cell(row=2, column=1).fill = fill_header
ws_plan.cell(row=2, column=1).alignment = align_center

clusters_info = [
    ("MET", "Metro Core", 5, 7),
    ("COM", "Commuter Sub", 8, 10),
    ("CAM", "Campus/Hosp", 11, 13),

… 4,004 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Sheets initialized and styles setup.
Reference sheets populated and formatted.
Historical Rates worksheet populated.
Projections sheets populated with formulas.
Assortment Plan worksheet populated.
Process Group PGID: 1691
</untrusted_context>

Arguments

description

Populate the Summary Dashboard tab with the Scenario Comparison, Capacity Check, and Key Findings text.

command

cat << 'EOF' >> /workspace/data/create_workbook.py

# Build Summary Dashboard
ws_dash.cell(row=1, column=2, value="Blue Mesa Market - refrigerated grab-and-go case").font = font_subtitle
ws_dash.cell(row=2, column=2, value="Prepared Foods July Reset Analysis").font = font_title

# 1. Scenario Performance Comparison Table (Cols B to F, Rows 5 to 19)
ws_dash.cell(row=4, column=2, value="Scenario Performance Comparison (4-Week Portfolio)").font = Font(na

… 11,247 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Sheets initialized and styles setup.
Reference sheets populated and formatted.
Historical Rates worksheet populated.
Projections sheets populated with formulas.
Assortment Plan worksheet populated.
Summary Dashboard worksheet populated.
Process Group PGID: 1702
</untrusted_context>

Arguments

cat << 'EOF' >> /workspace/data/create_workbook.py

# Auto-fit column widths, ignoring long titles in row 1-3
for ws in wb.worksheets:
    for col in ws.columns:
        col_letter = get_column_letter(col[0].column)
        max_len = 0
        for cell in col:
            # Skip long title cells to prevent overly wide columns
            if cell.row <= 3 and ws.title in ["Summary_Dashboard", "Assortment_Plan"]:
                continue
            if cell.value is not None:
                # If cell has formula, use a placeholder length, else string length

… 1,369 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Sheets initialized and styles setup.
Reference sheets populated and formatted.
Historical Rates worksheet populated.
Projections sheets populated with formulas.
Assortment Plan worksheet populated.
Summary Dashboard worksheet populated.
Workbook saved successfully to /workspace/output/reset_decision_workbook.xlsx
Process Group PGID: 1713
</untrusted_context>

Arguments

command

python /root/.gemini/skills/xlsx/scripts/recalc.py /workspace/output/reset_decision_workbook.xlsx

description

Recalculate Excel formulas in the saved workbook and check for any formula errors.

Output

<untrusted_context>
Output: {
  "status": "success",
  "total_errors": 0,
  "error_summary": {},
  "total_formulas": 2243
}
Process Group PGID: 1724
</untrusted_context>

Arguments

description

Create helper styling functions for building the memo document using python-docx.

command

cat << 'EOF' > /workspace/data/create_memo.py
import docx
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT, WD_ALIGN_VERTICAL
from docx.oxml import OxmlElement
from docx.oxml.ns import qn

# Initialize Document
doc = docx.Document()

# Set US Letter size and 1-inch margins
section = doc.sections[0]
section.page_width = Inches(8.5)
section.page_height = Inches(11.0)
section.top_margin

… 3,890 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Helper functions defined successfully.
Process Group PGID: 1906
</untrusted_context>

Arguments

cat << 'EOF' >> /workspace/data/create_memo.py

# Memo Header Block
p_mem = doc.add_paragraph()
r_mem = p_mem.add_run("MEMORANDUM")
r_mem.font.name = 'Arial'
r_mem.font.size = Pt(20)
r_mem.font.bold = True
r_mem.font.color.rgb = navy_color
p_mem.paragraph_format.space_after = Pt(12)

# Memo Details Table
tbl = doc.add_table(rows=4, cols=2)
tbl.alignment = WD_TABLE_ALIGNMENT.LEFT
details = [
    ("TO:", "L. Moreno, Senior Merchant \u2013 Prepared Foods"),
    ("FROM:", "Category Planning Analyst, Category Management Team"),
    ("DATE:", "August 20, 20

… 4,418 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Helper functions defined successfully.
Executive Summary added.
Process Group PGID: 1910
</untrusted_context>

Arguments

cat << 'EOF' >> /workspace/data/create_memo.py

# SECTION 2: PROPOSED VS. REVISED ASSORTMENT: MAJOR DEVIATIONS & RATIONALE
add_heading1("2. Proposed vs. Revised Assortment: Major Deviations & Rationale")

add_body_p(
    "To protect lunch traffic, secure profitability, and ensure flawless store-level execution, our Revised Assortment deviates from the buyer\u2019s raw proposal on several key SKUs. These changes are grounded strictly in the demand signals of historical POS records and the legal boundaries of signed vendor contracts. Below are the specific product-level dec

… 6,875 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Helper functions defined successfully.
Executive Summary added.
Section 2 added.
Process Group PGID: 1914
</untrusted_context>

Arguments

description

Write Section 3 of grab_go_reset_memo.docx showing cluster capacity check and plan.

command

cat << 'EOF' >> /workspace/data/create_memo.py

# SECTION 3: CLUSTER-LEVEL CAPACITY CHECK & ASSORTMENT PLAN
add_heading1("3. Cluster-Level Capacity Check & Assortment Plan")

add_body_p(
    "A fundamental failure of the buyer\u2019s original proposal is that it ignores the physical capacity limits of our refrigerated cases. Blue Mesa Market stores operate with specific case tray slot capacities based on their cluster type. Our space planning rules mandate that a carried it

… 6,764 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Helper functions defined successfully.
Executive Summary added.
Section 2 added.
Section 3 added.
Process Group PGID: 1918
</untrusted_context>

Arguments

description

Write Section 4 of grab_go_reset_memo.docx showing financial projections and comparison.

command

cat << 'EOF' >> /workspace/data/create_memo.py

# SECTION 4: FINANCIAL AND ECONOMICS PERFORMANCE
add_heading1("4. Financial and Economics Performance")

add_body_p(
    "A quantitative review of the grab-and-go case economics over the first four weeks of the reset shows that our Revised Recommendation is not only operationally viable but also significantly more profitable than the buyer\u2019s raw proposal. By replacing low-volume legacy items with highly productive co

… 5,549 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Helper functions defined successfully.
Executive Summary added.
Section 2 added.
Section 3 added.
Section 4 added.
Process Group PGID: 1922
</untrusted_context>

Arguments

description

Write Section 5 of grab_go_reset_memo.docx, add headers and footers with page numbers, and save the final document.

command

cat << 'EOF' >> /workspace/data/create_memo.py

# SECTION 5: IMPLEMENTATION & STORE-LEVEL EXECUTION RISKS
add_heading1("5. Implementation & Store-Level Execution Risks")

add_body_p(
    "To ensure the July grab-and-go case reset is a commercial success, field operations must manage several store-level operational pressure points. High-perishability items are excellent for case image but require strict execution discipline. Store-level executi

… 4,741 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Helper functions defined successfully.
Executive Summary added.
Section 2 added.
Section 3 added.
Section 4 added.
Word Document saved successfully to /workspace/output/grab_go_reset_memo.docx
Process Group PGID: 1926
</untrusted_context>

Arguments

/workspace/output/

Output

Directory listing for /workspace/output:
grab_go_reset_memo.docx (48342 bytes)
reset_decision_workbook.xlsx (49865 bytes)

Arguments

Validate the XML structure and schema of the generated Word document.

python /root/.gemini/skills/docx/scripts/office/validate.py /workspace/output/grab_go_reset_memo.docx

Output

<untrusted_context>
Output: FAILED - Found NEW validation errors:
  word/document.xml: 3 new error(s)
    - Element '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}tcBorders': This element is not expected. Expected is one of ( {http://schemas.openxmlformats.org/wordprocessingml/2006/main}textDirection, {http://schemas.openxmlformats.org/word...
    - Element '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}tcBorders': This element is not expected. Expected is one of ( {http://schemas.openxmlformats.org/wordprocessingml/2006/main}noWrap, {http://schemas.openxmlformats.org/wordprocess...
    - Element '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}left': This element is not expected. Expected is one of ( {http://schemas.openxmlformats.org/wordprocessingml/2006/main}end, {http://schemas.openxmlformats.org/wordprocessingml/20...
  word/settings.xml: 1 new error(s)
    - Element '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}zoom'

… 199 more characters of output in the raw trajectory.

Arguments

Overwrite create_memo.py with robust XML re-ordering functions.

cat << 'EOF' > /workspace/data/create_memo.py
import docx
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
from docx.oxml import OxmlElement
from docx.oxml.ns import qn

# Initialize Document
doc = docx.Document()

# Set US Letter size and 1-inch margins
section = doc.sections[0]
section.page_width = Inches(8.5)
section.page_height = Inches(11.0)
section.top_margin = Inches(1.0)
section.bottom_margin

… 5,425 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Robust schema helpers initialized.
Process Group PGID: 1933
</untrusted_context>

Arguments

description

Rebuild create_memo.py and execute it to generate the updated grab_go_reset_memo.docx with correct XML orders.

command

cat << 'EOF' >> /workspace/data/create_memo.py

# Memo Header Block
p_mem = doc.add_paragraph()
r_mem = p_mem.add_run("MEMORANDUM")
r_mem.font.name = 'Arial'
r_mem.font.size = Pt(20)
r_mem.font.bold = True
r_mem.font.color.rgb = navy_color
p_mem.paragraph_format.space_after = Pt(12)

# Memo Details Table
tbl = doc.add_table(rows=4, cols=2)
tbl.alignment = WD_TABLE_ALIGNMENT.LEFT
details = [
    ("TO:", "L. Moreno, Senior Merchant \u2013

… 28,131 more characters of arguments in the raw trajectory.

Output

<untrusted_context>
Output: Robust schema helpers initialized.
Traceback (most recent call last):
  File "/workspace/data/create_memo.py", line 452, in <module>
    for c_idx, text in enumerate(fin_headers):
                                 ^^^^^^^^^^^
NameError: name 'fin_headers' is not defined. Did you mean: 'cap_headers'?
Exit Code: 1
Process Group PGID: 1937
</untrusted_context>

Arguments

description

Write the clean and complete create_memo.py script to write the entire Word document cleanly.

content

import docx
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
from docx.oxml import OxmlElement
from docx.oxml.ns import qn

# Initialize Document
doc = docx.Document()

# Set US Letter size and 1-inch margins
section = doc.sections[0]
section.page_width = Inches(8.5)
section.page_height = Inches(11.0)
section.top_margin = Inches(1.0)
section.bottom_margin = Inches(1.0)
s

… 34,493 more characters of arguments in the raw trajectory.

The call returned no output.

Arguments

file_path

/workspace/data/create_memo.py

content

import docx
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
from docx.oxml import OxmlElement
from docx.oxml.ns import qn

# Initialize Document
doc = docx.Document()

# Set US Letter size and 1-inch margins
section = doc.sections[0]
section.page_width = Inches(8.5)
section.page_height = Inches(11.0)
section.top_margin = Inches(1.0)
section.bottom_margin = Inches(1.0)
section.left_margin = Inches(1.0)
section.right_margin = Inches(1

… 34,386 more characters of arguments in the raw trajectory.

Output

Successfully overwrote file: /workspace/data/create_memo.py. Here is the updated code:
...
    ensure_tcBorders_order(tcBorders)
    tcPr.append(tcBorders)
    ensure_tcPr_order(tcPr)

# Helper functions for text layout
def add_heading1(text):
    p = doc.add_paragraph()
    p.paragraph_format.keep_with_next = True
    p.paragraph_format.space_before = Pt(18)
    p.paragraph_format.space_after = Pt(6)
...
    run.font.name = 'Arial'
    run.font.size = Pt(10.5)
    run.font.italic = italic
    return p

# ----------------- MEMO CONTENTS -----------------

# Memo Header Block
p_mem = doc.add_paragraph()
r_mem = p_mem.add_run("MEMORANDUM")
r_mem.font.name = 'Arial'
...

# Financial Comparison Table
fin_table = doc.add_table(rows=13, cols=5)
fin_table.alignment = WD_TABLE_ALIGNMENT.CENTER

fin_headers = ["Key Economic Metric", "Buyer's Proposal", "Revised Recommendation", "Absolute Variance", "Percent Variance"]

for c_idx, text in enumerate(fin_headers):
    cell = fin_table.rows[0].cell

… 1,625 more characters of output in the raw trajectory.

Arguments

Run the create_memo.py script to generate grab_go_reset_memo.docx.

python /workspace/data/create_memo.py

Output

<untrusted_context>
Output: Word Document saved successfully to /workspace/output/grab_go_reset_memo.docx
Process Group PGID: 1941
</untrusted_context>

Arguments

Validate the regenerated Word document for XML and schema compliance.

python /root/.gemini/skills/docx/scripts/office/validate.py /workspace/output/grab_go_reset_memo.docx

Output

<untrusted_context>
Output: FAILED - Found NEW validation errors:
  word/document.xml: 1 new error(s)
    - Element '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}left': This element is not expected. Expected is one of ( {http://schemas.openxmlformats.org/wordprocessingml/2006/main}end, {http://schemas.openxmlformats.org/wordprocessingml/20...
  word/settings.xml: 1 new error(s)
    - Element '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}zoom': The attribute '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}percent' is required but missing.

Paragraphs: 0 → 186 (+186)
Exit Code: 1
Process Group PGID: 1944
</untrusted_context>

Arguments

file_path

/workspace/data/create_memo.py

content

import docx
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
from docx.oxml import OxmlElement
from docx.oxml.ns import qn
import zipfile
import re
import os

# Initialize Document
doc = docx.Document()

# Set US Letter size and 1-inch margins
section = doc.sections[0]
section.page_width = Inches(8.5)
section.page_height = Inches(11.0)
section.top_margin = Inches(1.0)
section.bottom_margin = Inches(1.0)
section.left_margin = Inches

… 35,589 more characters of arguments in the raw trajectory.

Output

Successfully overwrote file: /workspace/data/create_memo.py. Here is the updated code:
...
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
from docx.oxml import OxmlElement
from docx.oxml.ns import qn
import zipfile
import re
import os

# Initialize Document
doc = docx.Document()

# Set US Letter size and 1-inch margins
...
        tcPr.remove(child)
    for child in children:
        tcPr.append(child)

def ensure_tcBorders_order(tcBorders):
    # Schema-compliant border element ordering: top, start, bottom, end
    border_order = [
        qn('w:top'), qn('w:start'), qn('w:bottom'), qn('w:end'),
        qn('w:insideH'), qn('w:insideV'), qn('w:tl2br'), qn('w:tr2bl')
    ]
    children = list(tcBorders)
    def get_order_idx(child):
        tag = child.tag
...

def set_cell_borders(cell, top=None, bottom=None, left=None, right=None):
    tcPr = cell._tc.get_or_add_tcPr()
    tcBorders = OxmlElemen

… 1,607 more characters of output in the raw trajectory.

Arguments

description

Run the final create_memo.py script to generate and post-process grab_go_reset_memo.docx.

command

python /workspace/data/create_memo.py

Output

<untrusted_context>
Output: Word Document saved successfully to /workspace/output/grab_go_reset_memo.docx
Post-processing complete. Settings XML zoom element repaired.
Process Group PGID: 1947
</untrusted_context>

Arguments

Validate the final post-processed grab_go_reset_memo.docx file.

python /root/.gemini/skills/docx/scripts/office/validate.py /workspace/output/grab_go_reset_memo.docx

Output

<untrusted_context>
Output: FAILED - Found NEW validation errors:
  word/document.xml: 1 new error(s)
    - Element '{http://schemas.openxmlformats.org/wordprocessingml/2006/main}left': This element is not expected. Expected is one of ( {http://schemas.openxmlformats.org/wordprocessingml/2006/main}end, {http://schemas.openxmlformats.org/wordprocessingml/20...

Paragraphs: 0 → 186 (+186)
Exit Code: 1
Process Group PGID: 1950
</untrusted_context>

Arguments

Find occurrences of w:left in word/document.xml.

python -c "
import zipfile
with zipfile.ZipFile('/workspace/output/grab_go_reset_memo.docx', 'r') as z:
    doc_xml = z.read('word/document.xml').decode('utf-8')
    
# Let's find occurrences of '<w:left' in the xml
import re
matches = [m.start() for m in re.finditer('<w:left', doc_xml)]
print('Found w:left count:', len(matches))
for m in matches[:5]:
    print(doc_xml[m:m+150])
"

Output

<untrusted_context>
Output: Found w:left count: 129
<w:left w:w="0" w:type="dxa"/><w:right w:w="60" w:type="dxa"/></w:tcMar></w:tcPr><w:p><w:pPr><w:spacing w:after="80"/></w:pPr><w:r><w:rPr><w:rFonts w:
<w:left w:w="60" w:type="dxa"/><w:right w:w="0" w:type="dxa"/></w:tcMar></w:tcPr><w:p><w:pPr><w:spacing w:after="80"/></w:pPr><w:r><w:rPr><w:rFonts w:
<w:left w:w="0" w:type="dxa"/><w:right w:w="60" w:type="dxa"/></w:tcMar></w:tcPr><w:p><w:pPr><w:spacing w:after="80"/></w:pPr><w:r><w:rPr><w:rFonts w:
<w:left w:w="60" w:type="dxa"/><w:right w:w="0" w:type="dxa"/></w:tcMar></w:tcPr><w:p><w:pPr><w:spacing w:after="80"/></w:pPr><w:r><w:rPr><w:rFonts w:
<w:left w:w="0" w:type="dxa"/><w:right w:w="60" w:type="dxa"/></w:tcMar></w:tcPr><w:p><w:pPr><w:spacing w:after="80"/></w:pPr><w:r><w:rPr><w:rFonts w:
Process Group PGID: 1953
</untrusted_context>

Arguments

file_path

/workspace/data/create_memo.py

content

import docx
from docx.shared import Inches, Pt, RGBColor
from docx.enum.text import WD_ALIGN_PARAGRAPH
from docx.enum.table import WD_TABLE_ALIGNMENT
from docx.oxml import OxmlElement
from docx.oxml.ns import qn
import zipfile
import re
import os

# Initialize Document
doc = docx.Document()

# Set US Letter size and 1-inch margins
section = doc.sections[0]
section.page_width = Inches(8.5)
section.page_height = Inches(11.0)
section.top_margin = Inches(1.0)
section.bottom_margin = Inches(1.0)
section.left_margin = Inches

… 36,380 more characters of arguments in the raw trajectory.

Output

Successfully overwrote file: /workspace/data/create_memo.py. Here is the updated code:
...
    for child in list(tcBorders):
        tcBorders.remove(child)
    for child in children:
        tcBorders.append(child)

def ensure_tcMar_order(tcMar):
    # Schema-compliant cell margin element ordering: top, start, bottom, end
    margin_order = [
        qn('w:top'), qn('w:start'), qn('w:bottom'), qn('w:end')
    ]
    children = list(tcMar)
    def get_order_idx(child):
        tag = child.tag
        if tag in margin_order:
            return margin_order.index(tag)
        return len(margin_order)
    children.sort(key=get_order_idx)
    for child in list(tcMar):
        tcMar.remove(child)
    for child in children:
        tcMar.append(child)

# Formatting functions
def set_cell_background(cell, color_hex):
    tcPr = cell._tc.get_or_add_tcPr()
    shd = OxmlElement('w:shd')
    shd.set(qn('w:val'), 'clear')
...
    ensure_tcPr_order(tcPr)

def set_cell_margins(cell, top=100, bottom=

… 724 more characters of output in the raw trajectory.

Arguments

Run the updated create_memo.py script to regenerate grab_go_reset_memo.docx.

python /workspace/data/create_memo.py

Output

<untrusted_context>
Output: Word Document saved successfully to /workspace/output/grab_go_reset_memo.docx
Post-processing complete. Settings XML zoom element repaired.
Process Group PGID: 1956
</untrusted_context>

Arguments

Validate the regenerated Word document again.

python /root/.gemini/skills/docx/scripts/office/validate.py /workspace/output/grab_go_reset_memo.docx

Output

<untrusted_context>
Output: Paragraphs: 0 → 186 (+186)
All validations PASSED!
Process Group PGID: 1959
</untrusted_context>

Arguments

I have successfully completed the analysis of the refrigerated grab-and-go case reset proposal. By reconciling physical case capacities, signed vendor routing terms, and historical POS demand signals, I formulated a Revised Assortment recommendation. I then created two deliverables in `/workspace/output/`: `reset_decision_workbook.xlsx` containing 2,243 live Excel formulas and zero errors, and `grab_go_reset_memo.docx` which successfully passed all strict ECMA-376 schema validation checks. These files provide the senior merchant with an operationally sound, highly profitable (+$491

… 251 more characters of arguments in the raw trajectory.

Output

Current topic: "Chapter 3: Final Wrapping and Deliverables Recap"
Topic summary: I have successfully completed the analysis of the refrigerated grab-and-go case reset proposal. By reconciling physical case capacities, signed vendor routing terms, and historical POS demand signals, I formulated a Revised Assortment recommendation. I then created two deliverables in `/workspace/output/`: `reset_decision_workbook.xlsx` containing 2,243 live Excel formulas and zero errors, and `grab_go_reset_memo.docx` which successfully passed all strict ECMA-376 schema validation checks. These files provide the senior merchant with an operationally sound, highly profitable (+$491.31), and contract-compliant plan for immediate deployment.

Strategic Intent: Complete the analysis and wrap up by summarizing the successful creation and validation of both deliverables.