xlsx

Name: xlsx
Author: henkisdabro

34installs9forks

How to install xlsx?

henkisdabro/wookstar-claude-plugins/xlsx

$npx -y skills add henkisdabro/wookstar-claude-plugins --skill xlsx

Installs into the current project.

Summary

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualisation. Use when working with spreadsheets (.xlsx, .xlsm, .csv, .tsv) for creating new spreadsheets with formulas and formatting, reading or analysing data,

Files · 1

View on GitHub

SKILL.md

1	# Requirements for Outputs
2
3	## All Excel Files
4
5	### Zero Formula Errors
6
7	- Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)
8
9	### Preserve Existing Templates (when updating templates)
10
11	- Study and EXACTLY match existing format, style, and conventions when modifying files
12	- Never impose standardised formatting on files with established patterns
13	- Existing template conventions ALWAYS override these guidelines
14
15	## Financial Models
16
17	For financial models, DCFs, and valuations - read `references/financial-model-standards.md` for colour coding, number formatting, formula construction rules, and documentation requirements.
18
19	# XLSX Creation, Editing, and Analysis
20
21	## Overview
22
23	A user may ask you to create, edit, or analyse the contents of an .xlsx file. You have different tools and workflows available for different tasks.
24
25	## Important Requirements
26
27	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.
28
29	## Reading and Analysing Data
30
31	### Data analysis with pandas
32
33	For data analysis, visualisation, and basic operations, use pandas which provides powerful data manipulation capabilities:
34
35	```python
36	import pandas as pd
37
38	# Read Excel
39	df = pd.read_excel('file.xlsx') # Default: first sheet
40	all_sheets = pd.read_excel('file.xlsx', sheet_name=None) # All sheets as dict
41
42	# Analyse
43	df.head() # Preview data
44	df.info() # Column info
45	df.describe() # Statistics
46
47	# Write Excel
48	df.to_excel('output.xlsx', index=False)
49	```
50
51	## CRITICAL: Use Formulas, Not Hardcoded Values
52
53	Always use Excel formulas instead of calculating values in Python and hardcoding them. This ensures the spreadsheet remains dynamic and updateable.
54
55	### Bad - Hardcoding Calculated Values
56
57	```python
58	# Bad: Calculating in Python and hardcoding result
59	total = df['Sales'].sum()
60	sheet['B10'] = total # Hardcodes 5000
61
62	# Bad: Computing growth rate in Python
63	growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue']
64	sheet['C5'] = growth # Hardcodes 0.15
65
66	# Bad: Python calculation for average
67	avg = sum(values) / len(values)
68	sheet['D20'] = avg # Hardcodes 42.5
69	```
70
71	### Correct - Using Excel Formulas
72
73	```python
74	# Good: Let Excel calculate the sum
75	sheet['B10'] = '=SUM(B2:B9)'
76
77	# Good: Growth rate as Excel formula
78	sheet['C5'] = '=(C4-C2)/C2'
79
80	# Good: Average using Excel function
81	sheet['D20'] = '=AVERAGE(D2:D19)'
82	```
83
84	This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.
85
86	## Common Workflow
87
88	1. Choose tool: pandas for data, openpyxl for formulas/formatting
89	2. Create/Load: Create new workbook or load existing file
90	3. Modify: Add/edit data, formulas, and formatting
91	4. Save: Write to file
92	5. Recalculate formulas (MANDATORY IF USING FORMULAS): Use the recalc.py script
93	```bash
94	python recalc.py output.xlsx
95	```
96	6. Verify and fix any errors:
97	- The script returns JSON with error details
98	- If `status` is `errors_found`, check `error_summary` for specific error types and locations
99	- Fix the identified errors and recalculate again
100	- Common errors to fix:
101	- `#REF!`: Invalid cell references
102	- `#DIV/0!`: Division by zero
103	- `#VALUE!`: Wrong data type in formula
104	- `#NAME?`: Unrecognised formula name
105
106	For detailed code examples (creating/editing files), read `references/openpyxl-patterns.md`.
107
108	## Recalculating Formulas
109
110	Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided `recalc.py` script to recalculate:
111
112	```bash
113	python recalc.py <excel_file> [timeout_seconds]
114	```
115
116	Example:
117
118	```bash
119	python recalc.py output.xlsx 30
120	```
121
122	The script:
123
124	- Automatically sets up LibreOffice macro on first run
125	- Recalculates all formulas in all sheets
126	- Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.)
127	- Returns JSON with detailed error locations and counts
128	- Works on both Linux and macOS
129
130	For the formula verification checklist and recalc.py output interpretation, read `references/formula-verification.md`.
131
132	## Code Style Guidelines
133
134	IMPORTANT: When generating Python code for Excel operations:
135
136	- Write minima

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xlsx

34installs9forks

View on GitHub

How to install xlsx?

henkisdabro/wookstar-claude-plugins/xlsx

$npx -y skills add henkisdabro/wookstar-claude-plugins --skill xlsx

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Requirements for Outputs
2
3	## All Excel Files
4
5	### Zero Formula Errors
6
7	- Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)
8
9	### Preserve Existing Templates (when updating templates)
10
11	- Study and EXACTLY match existing format, style, and conventions when modifying files
12	- Never impose standardised formatting on files with established patterns
13	- Existing template conventions ALWAYS override these guidelines
14
15	## Financial Models
16
17	For financial models, DCFs, and valuations - read `references/financial-model-standards.md` for colour coding, number formatting, formula construction rules, and documentation requirements.
18
19	# XLSX Creation, Editing, and Analysis
20
21	## Overview
22
23	A user may ask you to create, edit, or analyse the contents of an .xlsx file. You have different tools and workflows available for different tasks.
24
25	## Important Requirements
26
27	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.
28
29	## Reading and Analysing Data
30
31	### Data analysis with pandas
32
33	For data analysis, visualisation, and basic operations, use pandas which provides powerful data manipulation capabilities:
34
35	```python
36	import pandas as pd
37
38	# Read Excel
39	df = pd.read_excel('file.xlsx') # Default: first sheet
40	all_sheets = pd.read_excel('file.xlsx', sheet_name=None) # All sheets as dict
41
42	# Analyse
43	df.head() # Preview data
44	df.info() # Column info
45	df.describe() # Statistics
46
47	# Write Excel
48	df.to_excel('output.xlsx', index=False)
49	```
50
51	## CRITICAL: Use Formulas, Not Hardcoded Values
52
53	Always use Excel formulas instead of calculating values in Python and hardcoding them. This ensures the spreadsheet remains dynamic and updateable.
54
55	### Bad - Hardcoding Calculated Values
56
57	```python
58	# Bad: Calculating in Python and hardcoding result
59	total = df['Sales'].sum()
60	sheet['B10'] = total # Hardcodes 5000
61
62	# Bad: Computing growth rate in Python
63	growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue']
64	sheet['C5'] = growth # Hardcodes 0.15
65
66	# Bad: Python calculation for average
67	avg = sum(values) / len(values)
68	sheet['D20'] = avg # Hardcodes 42.5
69	```
70
71	### Correct - Using Excel Formulas
72
73	```python
74	# Good: Let Excel calculate the sum
75	sheet['B10'] = '=SUM(B2:B9)'
76
77	# Good: Growth rate as Excel formula
78	sheet['C5'] = '=(C4-C2)/C2'
79
80	# Good: Average using Excel function
81	sheet['D20'] = '=AVERAGE(D2:D19)'
82	```
83
84	This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.
85
86	## Common Workflow
87
88	1. Choose tool: pandas for data, openpyxl for formulas/formatting
89	2. Create/Load: Create new workbook or load existing file
90	3. Modify: Add/edit data, formulas, and formatting
91	4. Save: Write to file
92	5. Recalculate formulas (MANDATORY IF USING FORMULAS): Use the recalc.py script
93	```bash
94	python recalc.py output.xlsx
95	```
96	6. Verify and fix any errors:
97	- The script returns JSON with error details
98	- If `status` is `errors_found`, check `error_summary` for specific error types and locations
99	- Fix the identified errors and recalculate again
100	- Common errors to fix:
101	- `#REF!`: Invalid cell references
102	- `#DIV/0!`: Division by zero
103	- `#VALUE!`: Wrong data type in formula
104	- `#NAME?`: Unrecognised formula name
105
106	For detailed code examples (creating/editing files), read `references/openpyxl-patterns.md`.
107
108	## Recalculating Formulas
109
110	Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided `recalc.py` script to recalculate:
111
112	```bash
113	python recalc.py <excel_file> [timeout_seconds]
114	```
115
116	Example:
117
118	```bash
119	python recalc.py output.xlsx 30
120	```
121
122	The script:
123
124	- Automatically sets up LibreOffice macro on first run
125	- Recalculates all formulas in all sheets
126	- Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.)
127	- Returns JSON with detailed error locations and counts
128	- Works on both Linux and macOS
129
130	For the formula verification checklist and recalc.py output interpretation, read `references/formula-verification.md`.
131
132	## Code Style Guidelines
133
134	IMPORTANT: When generating Python code for Excel operations:
135
136	- Write minima

People who liked this also viewed

6 picks

Type