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Alaa-TaiebandTunic Assistant
55ba1c1652
validate_data() accepted category values that are not {count?, index?}
objects. They slipped through to format_entry(), which then raised
AttributeError on a normal table lookup (or serialized a malformed shape
under --json). It also accepted duplicate company names silently.
- collect_validation_issues() now also flags a non-object category value
(and non-numeric count / non number-or-string index) as a hard error,
and duplicate company names as a warning.
- validate_data() keeps its eager-fail behavior (same messages), so
existing tests and load_data() are unchanged.
- --validate runs the checks standalone and prints an actionable report
(exit 1 on errors, 0 on warnings-only/clean), letting users pre-flight
their BYO salary_data.json.
Reproduced on master: validate_data({'companies':[{'company':'Acme',
'categories':{'eng':'not_a_dict'}}]}) returns without error, but
format_entry then raises AttributeError.
Co-authored-by: Tunic Assistant <assistant@tunic.local>
125 lines
4.1 KiB
Markdown
125 lines
4.1 KiB
Markdown
# Salary Benchmark Tool
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## What is this?
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The salary lookup tool (`salary_lookup.py`) lets you benchmark company salaries against a baseline from your own data. It's used during the `/apply` workflow to show how a company's compensation compares to market rates.
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**This tool is optional.** If you don't have salary data, the salary step is simply skipped during `/apply`.
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## How it works
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The tool reads a `salary_data.json` file in the repo root containing company salary benchmarks. It uses fuzzy matching to find companies by name, handling Danish/Nordic characters, legal suffixes (A/S, ApS), and common spelling variations.
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The data format supports any index-based or absolute salary data. For example:
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- Index 100 = median salary, higher is better
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- Absolute salary values in your currency
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- Any custom metric you want to track
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## Data format
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The tool expects `salary_data.json` with this structure:
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```json
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{
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"metadata": {
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"source": "My Union Statistics 2025",
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"index_baseline": 100,
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"index_label": "Index",
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"baseline_description": "Index 100 = median salary for private sector"
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},
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"companies": [
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{
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"company": "Novo Nordisk A/S",
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"city": "Bagsværd",
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"categories": {
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"all_employees": { "count": 500, "index": 108.5 },
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"engineering": { "count": 120, "index": 112.3 }
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}
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},
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{
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"company": "Ørsted A/S",
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"city": "Fredericia",
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"categories": {
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"all_employees": { "count": 200, "index": 105.2 }
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}
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}
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]
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}
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```
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### Fields
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- **metadata.source**: Where the data comes from (for reference)
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- **metadata.index_baseline**: The baseline value (e.g., 100 for index-based data)
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- **metadata.index_label**: Label for the index column in output
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- **metadata.baseline_description**: Human-readable explanation of the baseline
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- **companies[].company**: Company name (required)
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- **companies[].city**: City/location (optional, used for filtering)
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- **companies[].categories**: Named salary categories, each with `count` and/or `index`
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## Setup options
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### Option A: Create salary_data.json manually
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Create the file by hand with data from any source: union statistics, Glassdoor, salary surveys, networking, or personal research.
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### Option B: Convert from Excel
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If you have salary data in an Excel file:
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```bash
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pip install openpyxl
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python3 tools/convert_salary_excel.py path/to/salary-data.xlsx \
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--source "My Salary Data 2025" \
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--baseline 100 \
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--baseline-desc "Index 100 = median salary"
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```
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On Windows, use `py` if that is how Python is exposed on your PATH. If your system uses `python` instead of `python3`, substitute that in the examples.
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The converter auto-detects the Excel layout:
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- Looks for a "Company"/"Firma" column and an optional "City"/"By" column
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- Treats remaining columns as salary data (auto-pairs count/index columns)
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### Option C: Build from research
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Start with an empty template and add companies as you research them:
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```json
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{
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"metadata": {
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"source": "Personal research",
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"index_baseline": 0,
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"index_label": "Monthly salary (DKK)",
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"baseline_description": "Approximate monthly salary before tax"
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},
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"companies": [
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{
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"company": "Example Corp",
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"city": "Copenhagen",
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"categories": {
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"entry_level": { "index": 42000 },
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"senior": { "index": 55000 }
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}
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}
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]
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}
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```
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## Usage
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```bash
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python3 salary_lookup.py "Novo Nordisk"
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python3 salary_lookup.py "Ørsted" --city "Fredericia"
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python3 salary_lookup.py "COWI" --json
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python3 salary_lookup.py --list-all
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python3 salary_lookup.py --validate # pre-flight check your salary_data.json
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```
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## Important notes
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- The data file (`salary_data.json`) is **excluded from git** (see `.gitignore`). Your salary data may be proprietary or confidential.
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- If the data file is missing, `salary_lookup.py` exits with a helpful error message and the `/apply` workflow skips the salary benchmark step.
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- The fuzzy matcher handles Danish company name variations: legal suffixes, Nordic characters, anglicized spellings, and partial matches.
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- `--validate` checks your data file for malformed category values and duplicate company names and prints a report, without performing a lookup.
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