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Use when working with Google Sheets data output — writing arrest records, managing tabs, handling schema, debugging API errors.
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SOC 職業分類に基づく
| name | google-sheets-integration |
| description | Use when working with Google Sheets data output — writing arrest records, managing tabs, handling schema, debugging API errors. |
| source | Adapted from googleworkspace/cli GWS skills |
Google Sheets is our primary arrest data store. Each county gets its own tab.
The core/writers/sheets_writer.py module handles all writes using the
Google Sheets API v4 via a service account.
Solver → returns List[dict] → Runner → SheetsWriter.append_rows()
→ Google Sheets API v4
→ Sheet: "121z5R...IV_0E"
→ Tab: "{County}" (auto-created)
Our standard HEADER_ROW (defined in sheets_writer.py):
HEADER_ROW = [
'Booking_Number', 'County', 'State', 'Facility',
'Full_Name', 'First_Name', 'Last_Name', 'Middle_Name',
'DOB', 'Sex', 'Race', 'Height', 'Weight',
'Hair_Color', 'Eye_Color',
'Address', 'City', 'Zipcode',
'Booking_Date', 'Release_Date', 'Status',
'Charges', 'Bond_Amount', 'Bond_Type', 'Bond_Status',
'Agency', 'Case_Number',
'Court_Date', 'Court_Type', 'Court_Location',
'Mugshot_URL', 'Detail_URL', 'Scrape_Timestamp',
'Person_ID', 'Notes', 'Phone', 'Email',
'Emergency_Contact', 'Occupation'
]
Every write checks for existing Booking_Number + County combination:
# The writer handles dedup internally
writer = SheetsWriter(sheet_id, service_account_path)
new_count = writer.append_rows(county_tab, records)
# new_count = number of genuinely new records appended
Solvers return dicts — the writer maps them to columns:
record = {
'Booking_Number': '2026-04140123',
'County': 'Collier',
'State': 'FL',
'Full_Name': 'SMITH, JOHN A',
'First_Name': 'JOHN',
'Last_Name': 'SMITH',
'DOB': '01/15/1990',
'Charges': 'Battery | Disorderly Conduct',
'Bond_Amount': '5000.00',
'Booking_Date': '04/14/2026',
'Scrape_Timestamp': '2026-04-14 15:30:00',
}
# Missing fields default to empty string
GOOGLE_SERVICE_ACCOUNT_KEYcreds/service-account-key.json| Issue | Cause | Fix |
|---|---|---|
HttpError 403: insufficient permissions | Sheet not shared with SA | Share sheet with SA email |
HttpError 429: Rate limit exceeded | Too many API calls | Add retry with exponential backoff |
| Tab not found | County tab doesn't exist yet | Writer auto-creates tabs |
| Wrong column count | Schema mismatch | Verify HEADER_ROW matches |
| Duplicate records | Dedup check failed | Verify Booking_Number is populated |
Booking_Number — it's the dedup keyCounty — second part of dedup keyMM/DD/YYYY or YYYY-MM-DD (both work)Charge 1 | Charge 2 | Charge 3"5000.00" not 5000.00 (avoids float issues)datetime.now().strftime('%Y-%m-%d %H:%M:%S')Use this skill when expanding the scraper network to new Florida counties. Contains the 67-county expansion roadmap, prioritization framework, site reconnaissance procedure, and lead analysis strategy. The goal is total Florida coverage for maximum bail bond lead generation.
Use this skill when creating a new county arrest scraper, modifying an existing solver, or adding a new county to the scraping pipeline. Contains the canonical architecture, naming conventions, and integration checklist.
Non-negotiable rules, safe refactoring patterns, shared code modification guidelines, secrets management, config hierarchy, and schema change process for the swfl-arrest-scrapers repo. Read this before modifying any core infrastructure.
Testing patterns, fixture guidelines, parser test templates, smoke tests, and red-green TDD methodology for the scraper pipeline. Use when writing tests for new counties, verifying core/ changes, or debugging test failures.
Google Sheets: Read and write spreadsheets via the Sheets API v4.
Systematically strengthen scraper pipelines against network failures, anti-bot measures, data edge cases, and real-world brittleness that breaks idealized scraper designs.