--- name: xlsx-official description: "Requirements for Outputs workflow skill. Use this skill when the user needs Unless otherwise stated by the user or existing template and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off." version: "0.0.1" category: cli-automation tags: ["xlsx-official", "unless", "otherwise", "stated", "the", "user", "existing", "template"] complexity: advanced risk: caution tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"] source: community author: "sickn33" date_added: "2026-04-15" date_updated: "2026-04-25" --- # Requirements for Outputs ## Overview This public intake copy packages `plugins/antigravity-awesome-skills-claude/skills/xlsx-official` from `https://github.com/sickn33/antigravity-awesome-skills` into the native Omni Skills editorial shape without hiding its origin. Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow. This intake keeps the copied upstream files intact and uses the `external_source` block in `metadata.json` plus `ORIGIN.md` as the provenance anchor for review. # Requirements for Outputs Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: All Excel files, Financial models, Important Requirements, Reading and analyzing data, CRITICAL: Use Formulas, Not Hardcoded Values, Recalculating formulas. ## When to Use This Skill Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request. - This skill is applicable to execute the workflow or actions described in the overview. - Use when the request clearly matches the imported source intent: Unless otherwise stated by the user or existing template. - Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch. - Use when provenance needs to stay visible in the answer, PR, or review packet. - Use when copied upstream references, examples, or scripts materially improve the answer. - Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over. ## Operating Table | Situation | Start here | Why it matters | | --- | --- | --- | | First-time use | `metadata.json` | Confirms repository, branch, commit, and imported path through the `external_source` block before touching the copied workflow | | Provenance review | `ORIGIN.md` | Gives reviewers a plain-language audit trail for the imported source | | Workflow execution | `LICENSE.txt` | Starts with the smallest copied file that materially changes execution | | Supporting context | `recalc.py` | Adds the next most relevant copied source file without loading the entire package | | Handoff decision | `## Related Skills` | Helps the operator switch to a stronger native skill when the task drifts | ## Workflow This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow. 1. Choose tool: pandas for data, openpyxl for formulas/formatting 2. Create/Load: Create new workbook or load existing file 3. Modify: Add/edit data, formulas, and formatting 4. Save: Write to file 5. Recalculate formulas (MANDATORY IF USING FORMULAS): Use the recalc.py script 6. Verify and fix any errors: 7. The script returns JSON with error details ### Imported Workflow Notes #### Imported: Excel File Workflows #### Imported: Common Workflow 1. **Choose tool**: pandas for data, openpyxl for formulas/formatting 2. **Create/Load**: Create new workbook or load existing file 3. **Modify**: Add/edit data, formulas, and formatting 4. **Save**: Write to file 5. **Recalculate formulas (MANDATORY IF USING FORMULAS)**: Use the recalc.py script ```bash python recalc.py output.xlsx ``` 6. **Verify and fix any errors**: - The script returns JSON with error details - If `status` is `errors_found`, check `error_summary` for specific error types and locations - Fix the identified errors and recalculate again - Common errors to fix: - `#REF!`: Invalid cell references - `#DIV/0!`: Division by zero - `#VALUE!`: Wrong data type in formula - `#NAME?`: Unrecognized formula name ### Creating new Excel files ```python # Using openpyxl for formulas and formatting from openpyxl import Workbook from openpyxl.styles import Font, PatternFill, Alignment wb = Workbook() sheet = wb.active # Add data sheet['A1'] = 'Hello' sheet['B1'] = 'World' sheet.append(['Row', 'of', 'data']) # Add formula sheet['B2'] = '=SUM(A1:A10)' # Formatting sheet['A1'].font = Font(bold=True, color='FF0000') sheet['A1'].fill = PatternFill('solid', start_color='FFFF00') sheet['A1'].alignment = Alignment(horizontal='center') # Column width sheet.column_dimensions['A'].width = 20 wb.save('output.xlsx') ``` ### Editing existing Excel files ```python # Using openpyxl to preserve formulas and formatting from openpyxl import load_workbook # Load existing file wb = load_workbook('existing.xlsx') sheet = wb.active # or wb['SheetName'] for specific sheet # Working with multiple sheets for sheet_name in wb.sheetnames: sheet = wb[sheet_name] print(f"Sheet: {sheet_name}") # Modify cells sheet['A1'] = 'New Value' sheet.insert_rows(2) # Insert row at position 2 sheet.delete_cols(3) # Delete column 3 # Add new sheet new_sheet = wb.create_sheet('NewSheet') new_sheet['A1'] = 'Data' wb.save('modified.xlsx') ``` #### Imported: Overview A user may ask you to create, edit, or analyze the contents of an .xlsx file. You have different tools and workflows available for different tasks. #### Imported: All Excel files ### 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 ## Examples ### Example 1: Ask for the upstream workflow directly ```text Use @xlsx-official to handle . Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer. ``` **Explanation:** This is the safest starting point when the operator needs the imported workflow, but not the entire repository. ### Example 2: Ask for a provenance-grounded review ```text Review @xlsx-official against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why. ``` **Explanation:** Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection. ### Example 3: Narrow the copied support files before execution ```text Use @xlsx-official for . Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding. ``` **Explanation:** This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default. ### Example 4: Build a reviewer packet ```text Review @xlsx-official using the copied upstream files plus provenance, then summarize any gaps before merge. ``` **Explanation:** This is useful when the PR is waiting for human review and you want a repeatable audit packet. ## Best Practices Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution. - pandas: Best for data analysis, bulk operations, and simple data export - openpyxl: Best for complex formatting, formulas, and Excel-specific features - Cell indices are 1-based (row=1, column=1 refers to cell A1) - Use dataonly=True to read calculated values: loadworkbook('file.xlsx', data_only=True) - Warning: If opened with data_only=True and saved, formulas are replaced with values and permanently lost - For large files: Use readonly=True for reading or writeonly=True for writing - Formulas are preserved but not evaluated - use recalc.py to update values ### Imported Operating Notes #### Imported: Best Practices ### Library Selection - **pandas**: Best for data analysis, bulk operations, and simple data export - **openpyxl**: Best for complex formatting, formulas, and Excel-specific features ### Working with openpyxl - Cell indices are 1-based (row=1, column=1 refers to cell A1) - Use `data_only=True` to read calculated values: `load_workbook('file.xlsx', data_only=True)` - **Warning**: If opened with `data_only=True` and saved, formulas are replaced with values and permanently lost - For large files: Use `read_only=True` for reading or `write_only=True` for writing - Formulas are preserved but not evaluated - use recalc.py to update values ### Working with pandas - Specify data types to avoid inference issues: `pd.read_excel('file.xlsx', dtype={'id': str})` - For large files, read specific columns: `pd.read_excel('file.xlsx', usecols=['A', 'C', 'E'])` - Handle dates properly: `pd.read_excel('file.xlsx', parse_dates=['date_column'])` #### Imported: Code Style Guidelines **IMPORTANT**: When generating Python code for Excel operations: - Write minimal, concise Python code without unnecessary comments - Avoid verbose variable names and redundant operations - Avoid unnecessary print statements **For Excel files themselves**: - Add comments to cells with complex formulas or important assumptions - Document data sources for hardcoded values - Include notes for key calculations and model sections ## Troubleshooting ### Problem: The operator skipped the imported context and answered too generically **Symptoms:** The result ignores the upstream workflow in `plugins/antigravity-awesome-skills-claude/skills/xlsx-official`, fails to mention provenance, or does not use any copied source files at all. **Solution:** Re-open `metadata.json`, `ORIGIN.md`, and the most relevant copied upstream files. Check the `external_source` block first, then restate the provenance before continuing. ### Problem: The imported workflow feels incomplete during review **Symptoms:** Reviewers can see the generated `SKILL.md`, but they cannot quickly tell which references, examples, or scripts matter for the current task. **Solution:** Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it. ### Problem: The task drifted into a different specialization **Symptoms:** The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. **Solution:** Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind. ## Related Skills - `@00-andruia-consultant` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@00-andruia-consultant-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith` - Use when the work is better handled by that native specialization after this imported skill establishes context. - `@10-andruia-skill-smith-v2` - Use when the work is better handled by that native specialization after this imported skill establishes context. ## Additional Resources Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding. | Resource family | What it gives the reviewer | Example path | | --- | --- | --- | | `references` | copied reference notes, guides, or background material from upstream | `references/n/a` | | `examples` | worked examples or reusable prompts copied from upstream | `examples/n/a` | | `scripts` | upstream helper scripts that change execution or validation | `scripts/n/a` | | `agents` | routing or delegation notes that are genuinely part of the imported package | `agents/n/a` | | `assets` | supporting assets or schemas copied from the source package | `assets/n/a` | - [LICENSE.txt](LICENSE.txt) - [recalc.py](recalc.py) ### Imported Reference Notes #### Imported: 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)**: Links pulling from other worksheets within same workbook - **Red text (RGB: 255,0,0)**: External links to other files - **Yellow background (RGB: 255,255,0)**: Key assumptions needing attention or cells that need to be updated ### Number Formatting Standards #### Required Format Rules - **Years**: Format as text strings (e.g., "2024" not "2,024") - **Currency**: Use $#,##0 format; ALWAYS specify units in headers ("Revenue ($mm)") - **Zeros**: Use number formatting to make all zeros "-", including percentages (e.g., "$#,##0;($#,##0);-") - **Percentages**: Default to 0.0% format (one decimal) - **Multiples**: Format as 0.0x for valuation multiples (EV/EBITDA, P/E) - **Negative numbers**: Use parentheses (123) not minus -123 ### Formula Construction Rules #### Assumptions Placement - Place ALL assumptions (growth rates, margins, multiples, etc.) in separate assumption cells - Use cell references instead of hardcoded values in formulas - Example: Use =B5*(1+$B$6) instead of =B5*1.05 #### Formula Error Prevention - Verify all cell references are correct - Check for off-by-one errors in ranges - Ensure consistent formulas across all projection periods - Test with edge cases (zero values, negative numbers) - Verify no unintended circular references #### Documentation Requirements for Hardcodes - Comment or in cells beside (if end of table). Format: "Source: [System/Document], [Date], [Specific Reference], [URL if applicable]" - Examples: - "Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]" - "Source: Company 10-Q, Q2 2025, Exhibit 99.1, [SEC EDGAR URL]" - "Source: Bloomberg Terminal, 8/15/2025, AAPL US Equity" - "Source: FactSet, 8/20/2025, Consensus Estimates Screen" # XLSX creation, editing, and analysis #### Imported: Important Requirements **LibreOffice Required for Formula Recalculation**: You can assume LibreOffice is installed for recalculating formula values using the `recalc.py` script. The script automatically configures LibreOffice on first run #### Imported: Reading and analyzing data ### Data analysis with pandas For data analysis, visualization, and basic operations, use **pandas** which provides powerful data manipulation capabilities: ```python import pandas as pd # Read Excel df = pd.read_excel('file.xlsx') # Default: first sheet all_sheets = pd.read_excel('file.xlsx', sheet_name=None) # All sheets as dict # Analyze df.head() # Preview data df.info() # Column info df.describe() # Statistics # Write Excel df.to_excel('output.xlsx', index=False) ``` #### Imported: CRITICAL: Use Formulas, Not Hardcoded Values **Always use Excel formulas instead of calculating values in Python and hardcoding them.** This ensures the spreadsheet remains dynamic and updateable. ### ❌ WRONG - Hardcoding Calculated Values ```python # Bad: Calculating in Python and hardcoding result total = df['Sales'].sum() sheet['B10'] = total # Hardcodes 5000 # Bad: Computing growth rate in Python growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue'] sheet['C5'] = growth # Hardcodes 0.15 # Bad: Python calculation for average avg = sum(values) / len(values) sheet['D20'] = avg # Hardcodes 42.5 ``` ### ✅ CORRECT - Using Excel Formulas ```python # Good: Let Excel calculate the sum sheet['B10'] = '=SUM(B2:B9)' # Good: Growth rate as Excel formula sheet['C5'] = '=(C4-C2)/C2' # Good: Average using Excel function sheet['D20'] = '=AVERAGE(D2:D19)' ``` This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes. #### Imported: Recalculating formulas Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided `recalc.py` script to recalculate formulas: ```bash python recalc.py [timeout_seconds] ``` Example: ```bash python recalc.py output.xlsx 30 ``` The script: - Automatically sets up LibreOffice macro on first run - Recalculates all formulas in all sheets - Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.) - Returns JSON with detailed error locations and counts - Works on both Linux and macOS #### Imported: Formula Verification Checklist Quick checks to ensure formulas work correctly: ### Essential Verification - [ ] **Test 2-3 sample references**: Verify they pull correct values before building full model - [ ] **Column mapping**: Confirm Excel columns match (e.g., column 64 = BL, not BK) - [ ] **Row offset**: Remember Excel rows are 1-indexed (DataFrame row 5 = Excel row 6) ### Common Pitfalls - [ ] **NaN handling**: Check for null values with `pd.notna()` - [ ] **Far-right columns**: FY data often in columns 50+ - [ ] **Multiple matches**: Search all occurrences, not just first - [ ] **Division by zero**: Check denominators before using `/` in formulas (#DIV/0!) - [ ] **Wrong references**: Verify all cell references point to intended cells (#REF!) - [ ] **Cross-sheet references**: Use correct format (Sheet1!A1) for linking sheets ### Formula Testing Strategy - [ ] **Start small**: Test formulas on 2-3 cells before applying broadly - [ ] **Verify dependencies**: Check all cells referenced in formulas exist - [ ] **Test edge cases**: Include zero, negative, and very large values ### Interpreting recalc.py Output The script returns JSON with error details: ```json { "status": "success", // or "errors_found" "total_errors": 0, // Total error count "total_formulas": 42, // Number of formulas in file "error_summary": { // Only present if errors found "#REF!": { "count": 2, "locations": ["Sheet1!B5", "Sheet1!C10"] } } } ``` #### Imported: Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.