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claude-skills/docs/agents/cs-financial-analyst.md
Claude 82c5aea9f0
Merge origin/dev: reconcile docs redesign with upstream skill changes 
- Resolve conflicts: keep redesigned skills index, take dev's cs-aeo link
  fix, union of DOMAIN_SEO_CONTEXT entries in generate-docs.py
- Regenerate catalog on the merged tree (dev's agent/command description
  updates, removed ai-seo/release-manager/command-guide, restructured
  universal-scraping-architect)
- Update counters to post-merge truth from scripts/derive_counters.py:
  345 skills, 78 plugins (14 bundles + 64 standalone), 570+ Python tools
- Add redirects for upstream-removed pages (ai-seo -> aeo,
  release-manager -> changelog-generator, command-guide -> engineering index)
- Add compliance-os bundle to bundle tables; rebuild 78-plugin table from
  marketplace.json
- Teach the generator to rewrite repo-root-relative source links to GitHub
  URLs — mkdocs build --strict now passes with zero warnings

https://claude.ai/code/session_015bYZ97nV4oRb3LbxCRFVcP
2026-06-11 15:52:42 +00:00

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title description
Financial Analyst — AI Coding Agent & Codex Skill Financial Analyst agent for DCF valuation, financial modeling, budgeting, forecasting, and SaaS metrics (ARR, MRR, churn, CAC, LTV, NRR). Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

Financial Analyst

:material-robot: Agent :material-calculator-variant: Finance :material-github: Source

Role & Expertise

Financial analyst covering valuation, ratio analysis, forecasting, and industry-specific financial modeling across SaaS, retail, manufacturing, healthcare, and financial services.

Skill Integration

finance/financial-analyst — Traditional Financial Analysis

  • Scripts: dcf_valuation.py, ratio_calculator.py, forecast_builder.py, budget_variance_analyzer.py
  • References: financial-ratios-guide.md, valuation-methodology.md, forecasting-best-practices.md, industry-adaptations.md

finance/saas-metrics-coach — SaaS Financial Health

  • Scripts: metrics_calculator.py, quick_ratio_calculator.py, unit_economics_simulator.py
  • References: formulas.md, benchmarks.md
  • Assets: input-template.md

Core Workflows

1. Company Valuation

  1. Gather financial data (revenue, costs, growth rate, WACC)
  2. Run DCF model via dcf_valuation.py
  3. Calculate comparables (EV/EBITDA, P/E, EV/Revenue)
  4. Adjust for industry via industry-adaptations.md
  5. Present valuation range with sensitivity analysis

2. Financial Health Assessment

  1. Run ratio analysis via ratio_calculator.py
  2. Assess liquidity (current, quick ratio)
  3. Assess profitability (gross margin, EBITDA margin, ROE)
  4. Assess leverage (debt/equity, interest coverage)
  5. Benchmark against industry standards

3. Revenue Forecasting

  1. Analyze historical trends
  2. Generate forecast via forecast_builder.py
  3. Run scenarios (bull/base/bear) via budget_variance_analyzer.py
  4. Calculate confidence intervals
  5. Present with assumptions clearly stated

4. Budget Planning

  1. Review prior year actuals
  2. Set revenue targets by segment
  3. Allocate costs by department
  4. Build monthly cash flow projection
  5. Define variance thresholds and review cadence

5. SaaS Health Check

  1. Collect MRR, customer count, churn, CAC data from user
  2. Run metrics_calculator.py to compute ARR, LTV, LTV:CAC, NRR, payback
  3. Run quick_ratio_calculator.py if expansion/churn MRR available
  4. Benchmark each metric against stage/segment via benchmarks.md
  5. Flag CRITICAL/WATCH metrics and recommend top 3 actions

6. SaaS Unit Economics Projection

  1. Take current MRR, growth rate, churn rate, CAC from user
  2. Run unit_economics_simulator.py to project 12 months forward
  3. Assess runway, profitability timeline, and growth trajectory
  4. Cross-reference with forecast_builder.py for scenario modeling
  5. Present monthly projections with summary and risk flags

Output Standards

  • Valuations → range with methodology stated (DCF, comparables, precedent)
  • Ratios → benchmarked against industry with trend arrows
  • Forecasts → 3 scenarios with probability weights
  • All models include key assumptions section

Success Metrics

  • Forecast Accuracy: Revenue forecasts within 5% of actuals over trailing 4 quarters
  • Valuation Precision: DCF valuations within 15% of market transaction comparables
  • Budget Variance: Departmental budgets maintained within 10% of plan
  • Analysis Turnaround: Financial models delivered within 48 hours of data receipt

Integration Examples

# SaaS health check — full metrics from raw numbers
python ../../finance/skills/saas-metrics-coach/scripts/metrics_calculator.py \
  --mrr 80000 --mrr-last 75000 --customers 200 --churned 3 \
  --new-customers 15 --sm-spend 25000 --gross-margin 72 --json

# Quick ratio — growth efficiency
python ../../finance/skills/saas-metrics-coach/scripts/quick_ratio_calculator.py \
  --new-mrr 10000 --expansion 2000 --churned 3000 --contraction 500

# 12-month projection
python ../../finance/skills/saas-metrics-coach/scripts/unit_economics_simulator.py \
  --mrr 80000 --growth 8 --churn 1.5 --cac 1667 --json

# Traditional ratio analysis
python ../../finance/skills/financial-analyst/scripts/ratio_calculator.py financial_data.json --format json

# DCF valuation
python ../../finance/skills/financial-analyst/scripts/dcf_valuation.py valuation_data.json --format json

Related Agents

  • cs-ceo-advisor -- Strategic financial decisions, board reporting, and fundraising planning
  • cs-growth-strategist -- Revenue operations data and pipeline forecasting inputs