Minimax Xlsx

Every spreadsheet task moves through five phases in strict order. Do not skip or reorder phases.

<phase_intake>

Phase 1 — Understand the Task

Before writing any code:

1. Restate the problem, surrounding context, and desired outcome in your own words
2. Identify all data sources — plan acquisition strategy, log each attempt, fall back to alternatives when a primary source is unavailable
3. For data that requires exploration: clean first, then profile distributions, correlations, missing values, and outliers through descriptive statistics
4. Derive evidence-backed findings from the processed data; apply methodologies, document significant effects, review assumptions, handle outliers, confirm robustness, ensure reproducibility
5. Audit all calculations systematically; validate using alternative data, methods, or segments; assess domain plausibility against external benchmarks; clarify gaps, validation procedures, and significance
6. Numeric data must be stored in numeric format — never as text strings
7. Financial or monetary datasets require currency formatting with the appropriate symbol

External data provenance — if the deliverable incorporates data fetched via datasource, web_search, API calls, or any retrieval tool:

• Append two traceability columns next to the data: Provider | Reference Link
• Embed URLs as plain strings — HYPERLINK() causes formula-evaluation overhead and occasional corruption
• Sample:

• When row-level attribution is impractical, add a footnote section at the bottom of the relevant sheet (separated by a blank row and a "References" label), or create a standalone "References" worksheet
• Delivering a workbook that contains retrieved data without provenance metadata is forbidden

</phase_intake>

<phase_design>

Phase 2 — Design the Workbook

Create a sheet-level blueprint before writing any code. For each sheet, document:

• Cell layout (headers, data region, summary rows, computed columns)
• Every formula and which cells it references
• Cross-sheet dependencies and lookup relationships

Dynamic computation rule (non-negotiable):

Any value derivable from a formula must be expressed as a formula. Static values are only acceptable for external-fetch data, true constants, or circular-dependency avoidance.

# Live formulas — correct
ws['D3'] = '=B3*C3'
ws['E3'] = '=D3/SUM($D$3:$D$50)'
ws['F3'] = '=AVERAGE(B3:B50)'

# Frozen snapshots — wrong
result = price * qty
ws['D3'] = result  # loses traceability

Cross-table lookups — step by step:

When two tables share a common key (signals: "based on", "from another table", "match against", or columns like ProductID / EmployeeID appear in both):

1. Identify the shared key column in both the source and the target table
2. Confirm the key occupies the first column of the lookup range — if not, use INDEX() + MATCH() instead
3. Build the formula with absolute anchoring and an error wrapper:

   ws['D3'] = '=IFERROR(VLOOKUP(B3,$E$2:$H$120,2,FALSE),"")'
   

4. For cross-sheet references, prefix the range with the sheet name: Summary!$A$2:$D$80
5. Multi-file scenarios: consolidate all sources into a single workbook before writing any lookup formulas — substituting pandas merge() for VLOOKUP is not allowed

Common pitfalls: #N/A usually means the key does not exist in the target range; #REF! means the column index exceeds the width of the lookup range.

Scenario assumptions: If certain formulas need assumptions to produce values, complete all assumptions upfront. Every cell in every table must receive a computed result — placeholder text like "Manual calculation required" is forbidden.

</phase_design>

<phase_fabrication>

Phase 3 — Build, Audit, Repeat

Construct the workbook one sheet at a time. Audit immediately after each sheet — never defer checks to the end.

FOR EACH sheet:
    1. BUILD  — populate cells with data, formulas, and visual formatting
    2. SAVE   — wb.save('output.xlsx')
    3. RECALC — python ./scripts/recalc.py output.xlsx (if sheet has formulas)
    4. AUDIT  — ./scripts/MiniMaxXlsx recalc output.xlsx
               ./scripts/MiniMaxXlsx refcheck output.xlsx
               (if the sheet has charts) ./scripts/MiniMaxXlsx chart output.xlsx -v
    5. FIX    — resolve every finding; loop back to step 1 until zero issues
    6. NEXT   — advance to the next sheet only when the current one is clean

Recheck outcomes are authoritative — no negotiation allowed.

The recalc subcommand identifies formula errors (#VALUE!, #DIV/0!, #REF!, #NAME?, #N/A, etc.) and zero-result cells. Follow these rules without exception:

1. Zero tolerance: If recalc flags ANY issue, resolve it before delivery. Period.
2. Do NOT assume issues will self-correct:
   • Wrong: "These errors will disappear when the user opens the file in Excel"
   • Wrong: "Excel will recalculate and fix these automatically"
   • Right: Fix ALL flagged issues until error_count = 0
3. Every finding is an action item:
   • error_count: 5 means 5 problems to solve
   • zero_value_count: 3 means 3 suspicious cells to examine
   • Only error_count: 0 allows advancing to the next step
4. Common rationalizations to avoid:
   • Wrong: "The #REF! happens because openpyxl doesn't evaluate formulas" — fix it!
   • Wrong: "The #VALUE! will resolve when opened in Excel" — fix it!
   • Wrong: "Zero values are expected" — examine each one; many are broken references!
5. Delivery gate: Files with ANY recalc findings cannot be shipped.

Workbook scaffold:

from openpyxl import Workbook
from openpyxl.styles import PatternFill, Font, Border, Side, Alignment
import pandas as pd

wb = Workbook()
ws = wb.active
ws.title = "Data"
ws.sheet_view.showGridLines = False  # mandatory on every sheet

ws['B2'] = "Title"
ws['B2'].font = Font(size=16, bold=True)
ws.row_dimensions[2].height = 30  # prevent title clipping

wb.save('output.xlsx')

Visual design — before writing any styling code, read ./styling.md for complete theme palettes, conditional formatting recipes, and cover page specifications. Key rules:

• Gridlines off on every sheet; content starts at B2, not A1
• Four themes are available: grayscale (default), financial (monetary/fiscal work), verdant (ecology, education, humanities), dusk (technology, creative, scientific). Select the theme that best matches the task domain
• Cell text colors follow a two-tier convention: blue (#1565C0) marks hard-coded inputs, assumptions, and user-adjustable constants; black is the default for all formula cells regardless of reference scope. Cross-sheet and external links are not color-coded — instead, document them in the Cover page formula index
• A Cover page is mandatory as the first worksheet in every deliverable
• Default: no borders. Use thin borders within models only when they clarify structure.

Merged cells: Use ws.merge_cells() for titles, multi-column headers, or grouped labels. Apply formatting to the top-left cell only. Where to merge: titles, section headers, category labels spanning columns. Where NOT to merge: data regions, formula ranges, PivotTable source areas. Always set alignment on merged cells.

Charts — when the request contains any of: "visual", "chart", "graph", "visualization", "diagram":

Read ./charts.md in full before creating any chart object. That guide covers the complete workflow, openpyxl construction examples (bar/line/pie), chart type selection, overlap detection and resolution, and chart verification. Do not attempt chart creation without it.

PivotTables — activate when you detect any of these signals:

• Explicit: "pivot table", "data pivot", "数据透视表"
• Implicit: roll up, grouped summary, category totals, segment analysis, distribution view, frequency split, total per category
• The dataset exceeds 50 rows with natural grouping dimensions
• Multi-dimensional cross-tabulation is needed

When a PivotTable is warranted:

1. Read ./pivot.md cover-to-cover before doing anything
2. Follow the execution sequence documented there
3. Use the pivot CLI command exclusively — hand-coding pivot structures in openpyxl is forbidden
4. The pivot output is read-only from this point forward — any subsequent openpyxl load_workbook() call will silently break internal XML references, producing a file Excel refuses to open

Execution order is strict: Complete all openpyxl-authored sheets (Cover, Summary, data tabs) first, then run pivot as the final write step. After pivot emits the file, do not modify that file again.

</phase_fabrication>

<phase_verification>

Phase 4 — Certify the File

After every sheet has passed its individual audit, run the structural gate:

./scripts/MiniMaxXlsx check output.xlsx

• Exit code 0 → safe to deliver
• Non-zero → the file will not open in Microsoft Excel. Do NOT attempt incremental patches — regenerate the workbook from corrected code.

</phase_verification>

<phase_release>

Phase 5 — Delivery Checklist

Before handing the file to the user, confirm every item:

• At least one .xlsx file in the delivery
• Every sheet with headers also contains data rows — no empty tables
• No formula cell evaluates to null (if any do, verify the referenced cells hold values)
• Row and column dimensions are proportional — no extremely narrow columns paired with tall rows
• All computations use real data unless the user explicitly requested synthetic data
• Measurement units appear in column headers, not inline with cell values
• Theme matches the task domain: financial for fiscal work, verdant for ecology/education/humanities, dusk for technology/creative/scientific, grayscale for everything else
• External data includes provenance metadata (Provider + Reference Link) in the workbook
• Charts are real embedded objects, not "chart data" sheets with manual instructions
• PivotTables were built via the pivot CLI, not hand-coded in openpyxl
• Cross-table lookups use VLOOKUP/INDEX-MATCH formulas, not pandas merge()
• check returned exit code 0
• Chart overlaps have been resolved (if charts exist) — no overlapping bounding boxes

</phase_release>