finance-ops

Preamble (runs on skill start)

# Version check (silent if up to date)
python3 telemetry/version_check.py 2>/dev/null || true

# Telemetry opt-in (first run only, then remembers your choice)
python3 telemetry/telemetry_init.py 2>/dev/null || true

Privacy: This skill logs usage locally to ~/.ai-marketing-skills/analytics/. Remote telemetry is opt-in only. No code, file paths, or repo content is ever collected. See telemetry/README.md.

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AI Finance Ops

Two tools: CFO Briefing Generator and Codebase Cost Estimator.

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Tool 1: CFO Briefing Generator

Generate executive financial summaries from QuickBooks exports.

Workflow

1. Ingest Files

Place QuickBooks export files (CSV, XLSX, XLS) in a working directory. Accepted report types (any subset works — P&L alone is sufficient):

• P&L Summary — Revenue, COGS, expenses, net income (MOST IMPORTANT)
• P&L by Customer — Revenue breakdown by client
• P&L Detail — Transaction-level detail (XLSX)
• Balance Sheet — Assets, liabilities, equity
• General Ledger — All account transactions
• Expenses by Vendor — Vendor-level expense breakdown
• Transaction List by Vendor — Detailed vendor transactions
• Bill Payments — AP payment history
• Cash Flow Statement — Operating/investing/financing flows (XLSX)
• Account List — Chart of accounts

2. Run Analysis

python3 scripts/cfo-analyzer.py --input ./data/uploads/ [--period YYYY-MM]

Options:

• --input DIR — Directory with QB exports
• --period YYYY-MM — Override period label (default: auto-detected from files)
• --history DIR — History directory for MoM comparison (default: ./data/history/)
• --no-history — Skip saving to history

The script:

1. Auto-detects file types by scanning headers
2. Parses each file into structured data
3. Computes all KPIs (see references/metrics-guide.md for definitions and healthy ranges)
4. Loads prior period from history for MoM comparison
5. Saves current period to history
6. Outputs formatted executive summary to stdout