financial-analyst

Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Use when analyzi...

Security Scan

The skill's description, templates, and runtime instructions align with a local financial-analysis toolkit, but the Python scripts themselves were not provided for review, so hidden behavior (network calls, credential access, or data exfiltration) cannot be ruled out.

Purpose & Capability

Name, description, SKILL.md, templates, reference docs, and the four analysis scripts (ratio, DCF, variance, forecast) are coherent and proportional to a financial-analysis skill — nothing in the manifest asks for unrelated credentials, binaries, or system paths.

Instruction Scope

SKILL.md limits runtime actions to running local Python scripts against user-provided JSON files and generating reports/templates. It does instruct validating input data and cross-checking outputs (appropriate). It does not mention contacting external endpoints, but the provided SKILL.md and assets do not include the actual Python source content for inspection, so we cannot verify whether the scripts themselves perform network I/O, write outside expected locations, or read unrelated system files.

Install Mechanism

No install specification — instruction-only skill that relies on local Python. This is low-risk in terms of installation because nothing is downloaded or written during install.

Credentials

No required environment variables, no primary credential, and no config paths are declared. That is appropriate for a local financial-analysis tool that operates on files supplied by the user.

Persistence & Privilege

Skill is not always-enabled and uses normal model invocation. It does not request persistent platform privileges in the registry metadata.

What to consider before installing

This package appears consistent with its stated purpose, but the Python scripts (scripts/*.py) were not included in the provided contents for review — that is the main unknown. Before running this skill on sensitive or production financial data, review the Python sources for these risks: any outbound network calls (requests, urllib, sockets, http clients), arbitrary subprocess execution (subprocess, os.system, eval/exec), file reads outside the working/input files, credential harvesting (os.environ access and transmission), and obfuscated or minified code (base64, exec of decoded strings). If you can't inspect the code, run it in an isolated sandbox/container with no network access and test with synthetic or redacted data. Consider static checks (grep for 'requests', 'socket', 'subprocess', 'eval', 'exec', 'open' with absolute paths) and a quick dynamic monitor (network traffic, file writes) when first executing.

Financial Analyst Skill

Overview

Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial modeling, forecasting & budgeting, management reporting, business performance analysis, and investment analysis.

5-Phase Workflow

Phase 1: Scoping

• Define analysis objectives and stakeholder requirements
• Identify data sources and time periods
• Establish materiality thresholds and accuracy targets
• Select appropriate analytical frameworks

Phase 2: Data Analysis & Modeling

• Collect and validate financial data (income statement, balance sheet, cash flow)
• Validate input data completeness before running ratio calculations (check for missing fields, nulls, or implausible values)
• Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
• Build DCF models with WACC and terminal value calculations; cross-check DCF outputs against sanity bounds (e.g., implied multiples vs. comparables)
• Construct budget variance analyses with favorable/unfavorable classification
• Develop driver-based forecasts with scenario modeling

Phase 3: Insight Generation

• Interpret ratio trends and benchmark against industry standards
• Identify material variances and root causes
• Assess valuation ranges through sensitivity analysis
• Evaluate forecast scenarios (base/bull/bear) for decision support

Phase 4: Reporting

• Generate executive summaries with key findings
• Produce detailed variance reports by department and category
• Deliver DCF valuation reports with sensitivity tables
• Present rolling forecasts with trend analysis

Phase 5: Follow-up

• Track forecast accuracy (target: +/-5% revenue, +/-3% expenses)
• Monitor report delivery timeliness (target: 100% on time)
• Update models with actuals as they become available
• Refine assumptions based on variance analysis

Tools

1. Ratio Calculator (scripts/ratio_calculator.py)

Calculate and interpret financial ratios from financial statement data.

Ratio Categories:

• Profitability: ROE, ROA, Gross Margin, Operating Margin, Net Margin
• Liquidity: Current Ratio, Quick Ratio, Cash Ratio
• Leverage: Debt-to-Equity, Interest Coverage, DSCR
• Efficiency: Asset Turnover, Inventory Turnover, Receivables Turnover, DSO
• Valuation: P/E, P/B, P/S, EV/EBITDA, PEG Ratio

python scripts/ratio_calculator.py sample_financial_data.json
python scripts/ratio_calculator.py sample_financial_data.json --format json
python scripts/ratio_calculator.py sample_financial_data.json --category profitability

2. DCF Valuation (scripts/dcf_valuation.py)

Discounted Cash Flow enterprise and equity valuation with sensitivity analysis.

Features:

• WACC calculation via CAPM
• Revenue and free cash flow projections (5-year default)
• Terminal value via perpetuity growth and exit multiple methods
• Enterprise value and equity value derivation
• Two-way sensitivity analysis (discount rate vs growth rate)

python scripts/dcf_valuation.py valuation_data.json
python scripts/dcf_valuation.py valuation_data.json --format json
python scripts/dcf_valuation.py valuation_data.json --projection-years 7

3. Budget Variance Analyzer (scripts/budget_variance_analyzer.py)

Analyze actual vs budget vs prior year performance with materiality filtering.

Features:

• Dollar and percentage variance calculation
• Materiality threshold filtering (default: 10% or $50K)
• Favorable/unfavorable classification with revenue/expense logic
• Department and category breakdown
• Executive summary generation

python scripts/budget_variance_analyzer.py budget_data.json
python scripts/budget_variance_analyzer.py budget_data.json --format json
python scripts/budget_variance_analyzer.py budget_data.json --threshold-pct 5 --threshold-amt 25000

4. Forecast Builder (scripts/forecast_builder.py)

Driver-based revenue forecasting with rolling cash flow projection and scenario modeling.

Features:

• Driver-based revenue forecast model
• 13-week rolling cash flow projection
• Scenario modeling (base/bull/bear cases)
• Trend analysis using simple linear regression (standard library)

python scripts/forecast_builder.py forecast_data.json
python scripts/forecast_builder.py forecast_data.json --format json
python scripts/forecast_builder.py forecast_data.json --scenarios base,bull,bear

Knowledge Bases

Reference	Purpose
references/financial-ratios-guide.md	Ratio formulas, interpretation, industry benchmarks
references/valuation-methodology.md	DCF methodology, WACC, terminal value, comps
references/forecasting-best-practices.md	Driver-based forecasting, rolling forecasts, accuracy
references/industry-adaptations.md	Sector-specific metrics and considerations (SaaS, Retail, Manufacturing, Financial Services, Healthcare)

Templates

Template	Purpose
assets/variance_report_template.md	Budget variance report template
assets/dcf_analysis_template.md	DCF valuation analysis template
assets/forecast_report_template.md	Revenue forecast report template

Key Metrics & Targets

Metric	Target
Forecast accuracy (revenue)	+/-5%
Forecast accuracy (expenses)	+/-3%
Report delivery	100% on time
Model documentation	Complete for all assumptions
Variance explanation	100% of material variances

Input Data Format

All scripts accept JSON input files. See assets/sample_financial_data.json for the complete input schema covering all four tools.

Dependencies

None - All scripts use Python standard library only (math, statistics, json, argparse, datetime). No numpy, pandas, or scipy required.