AI-powered financial analysis suite. Generates executive CFO briefings from QuickBooks exports (P&L, Balance Sheet, General Ledger, Cash Flow, etc.) with anomaly detection, burn rate, runway analysis, and scenario modeling. Also estimates codebase development costs with organizational overhead and AI ROI analysis. Triggers on: 'CFO briefing', 'financial analysis', 'cost briefing', 'expense review', 'runway analysis', 'burn rate', 'cost estimate', 'how much would this cost to build', 'development cost', 'Claude ROI'.
# 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. Seetelemetry/README.md.
Two tools: CFO Briefing Generator and Codebase Cost Estimator.
Generate executive financial summaries from QuickBooks exports.
Place QuickBooks export files (CSV, XLSX, XLS) in a working directory. Accepted report types (any subset works — P&L alone is sufficient):
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 historyThe script:
references/metrics-guide.md for definitions and healthy ranges)After running the CFO analysis, model base/bull/bear scenarios:
python3 scripts/scenario-modeler.py --input ./data/financial-latest.json
This generates 12-month projections for:
The script outputs a formatted briefing with emoji status indicators (🟢🟡🔴), suitable for Slack, email, or any messaging surface.
See references/quickbooks-formats.md for expected CSV/XLSX column formats and detection heuristics.
See references/metrics-guide.md for healthy ranges, red/yellow/green thresholds, and benchmark context. Adjust thresholds for your business size and type.
Estimate full development cost of a codebase.
Read the entire codebase. Catalog total lines of code by language/type, architectural complexity, advanced features, testing coverage, and documentation quality.
Apply productivity rates from references/rates.md. Calculate base hours per code type, then apply overhead multipliers for architecture, debugging, review, docs, integration, and learning curve.
Use web search to find current hourly rates for the relevant specializations. Build a rate table with low / median / high for the project's tech stack.
Convert raw dev hours to calendar time using efficiency factors from references/org-overhead.md. Show estimates across company types (Solo through Enterprise).
Apply supporting role ratios and team multipliers from references/team-cost.md. Show role-by-role breakdown, plus summary across all company stages.
Output the full estimate using the template in references/output-template.md. Include all sections: codebase metrics, dev hours, calendar time, market rates, engineering cost, full team cost, grand total summary, and assumptions.
If the codebase was built with AI assistance, calculate value per AI hour using references/claude-roi.md. Determine active hours via git history clustering, calculate speed multiplier vs human developer, and compute cost savings and ROI.