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Agent skill

Financial Analyst

by aAAaqwq in aAAaqwq/AGI-Super-Team

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

MITAuto-check passedBusiness, Finance & HR

Install Financial Analyst

skills CLI
$ npx skills add aAAaqwq/AGI-Super-Team --skill financial-analyst -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aAAaqwq/AGI-Super-Team financial-analyst --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aAAaqwq/AGI-Super-Team.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/financial-analyst .claude/skills/financial-analyst && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
financial-analyst
GitHub stars
105
Used in
1 other repo
Token cost
~4.2k tokens
SKILL.md length
1,462 words
Files
1
Skills in repo
167
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 9 steps: Scoping → Data Analysis & Modeling → Insight Generation → …
  • Tasks that involve Financial modeling
  • SKILL.md covers Overview, 5-Phase Workflow, Tools and Knowledge Bases, plus 10 more sections
  • Calls python

What it does

Financial Analyst is an agent skill from aAAaqwq/AGI-Super-Team. Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making

Its SKILL.md is about 4.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Business, Finance & HR, covering Financial modeling, Financial analysis and Budgeting and forecasting. The repository describes itself as: An installable, cross-framework AI organization: C-suite agents, expert subagents, curated skills, independent review, and one-command setup across 18 AI client/runtime adapters. The licence is MIT.

When your agent uses it

  • Tasks that involve Financial modeling
  • Tasks that involve Financial analysis
  • Tasks that involve Budgeting and forecasting

Example prompts

  • “Use the financial-analyst skill to perform financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for…”
  • “/financial-analyst”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Scoping
  2. Data Analysis & Modeling
  3. Insight Generation
  4. Reporting
  5. Follow-up
  6. Ratio Calculator (scripts/ratio_calculator.py)
  7. DCF Valuation (scripts/dcf_valuation.py)
  8. Budget Variance Analyzer (scripts/budget_variance_analyzer.py)
  9. Forecast Builder (scripts/forecast_builder.py)

What it can do on your machine

Read from SKILL.md and the folder at commit 7cefd81. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Financial Analyst loads about 4.2k tokens when it runs. Until then it costs about 39 tokens; SKILL.md has 1,462 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~4.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aAAaqwq/AGI-Super-Team at commit 7cefd81, republished under its MIT licence (© aAAaqwq). 1,462 words, ~4,242 tokens.

Download SKILL.mdSave it as .claude/skills/financial-analyst/SKILL.md (or your agent's skills folder).
name
financial-analyst
description
Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making
license
MIT + Commons Clause
metadata.version
1.0.0
metadata.author
borghei
metadata.category
finance
metadata.domain
financial-analysis
metadata.updated
2026-03-31
metadata.tags
financial-analysis, dcf, budgeting, forecasting, ratios

Financial Analyst Skill

Overview

Production-ready financial analysis toolkit providing ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction. Designed for financial analysts with 3-6 years experience performing 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)
  • Calculate financial ratios across 5 categories (profitability, liquidity, leverage, efficiency, valuation)
  • Build DCF models with WACC and terminal value calculations
  • 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
bash
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)
bash
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
bash
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)
bash
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

ReferencePurpose
references/financial-ratios-guide.mdRatio formulas, interpretation, industry benchmarks
references/valuation-methodology.mdDCF methodology, WACC, terminal value, comps
references/forecasting-best-practices.mdDriver-based forecasting, rolling forecasts, accuracy

Templates

TemplatePurpose
assets/variance_report_template.mdBudget variance report template
assets/dcf_analysis_template.mdDCF valuation analysis template
assets/forecast_report_template.mdRevenue forecast report template

Industry Adaptations

SaaS
  • Key metrics: MRR, ARR, CAC, LTV, Churn Rate, Net Revenue Retention
  • Revenue recognition: subscription-based, deferred revenue tracking
  • Unit economics: CAC payback period, LTV/CAC ratio
  • Cohort analysis for retention and expansion revenue
Retail
  • Key metrics: Same-store sales, Revenue per square foot, Inventory turnover
  • Seasonal adjustment factors in forecasting
  • Gross margin analysis by product category
  • Working capital cycle optimization
Manufacturing
  • Key metrics: Gross margin by product line, Capacity utilization, COGS breakdown
  • Bill of materials cost analysis
  • Absorption vs variable costing impact
  • Capital expenditure planning and ROI
Financial Services
  • Key metrics: Net Interest Margin, Efficiency Ratio, ROA, Tier 1 Capital
  • Regulatory capital requirements
  • Credit loss provisioning and reserves
  • Fee income analysis and diversification
Healthcare
  • Key metrics: Revenue per patient, Payer mix, Days in A/R, Operating margin
  • Reimbursement rate analysis by payer
  • Case mix index impact on revenue
  • Compliance cost allocation

Key Metrics & Targets

MetricTarget
Forecast accuracy (revenue)+/-5%
Forecast accuracy (expenses)+/-3%
Report delivery100% on time
Model documentationComplete for all assumptions
Variance explanation100% 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.

Troubleshooting

ProblemCauseSolution
All ratios return 0.00Missing or zeroed financial statement fields in input JSONVerify income_statement, balance_sheet, and cash_flow keys are populated with non-zero values; check field names match expected schema
DCF yields negative equity valueNet debt exceeds enterprise value, or WACC is set lower than terminal growth rateConfirm net_debt is accurate; ensure terminal_growth_rate < WACC (typically 2-3% vs 8-12%); review capital structure assumptions
Sensitivity table shows "N/A" across entire rowWACC value in that row is less than or equal to every terminal growth rate in the rangeWiden the gap between WACC and terminal growth; raise WACC inputs or lower the growth range in assumptions.terminal_growth_rate
Budget variance analyzer flags every line as materialMateriality thresholds set too low relative to the data scaleIncrease --threshold-pct (e.g., from 5 to 10) and --threshold-amt (e.g., from 25000 to 100000) to match organizational materiality policy
Forecast builder produces flat projectionsHistorical data has fewer than 2 periods, or revenue_growth_rate is set to 0Provide at least 3-4 historical periods in historical_periods; set a non-zero revenue_growth_rate in assumptions
JSON parsing error on script executionMalformed JSON input file (trailing commas, unquoted keys, encoding issues)Validate input with python -m json.tool input_file.json; ensure UTF-8 encoding; remove trailing commas and comments
Valuation ratios all show "Insufficient data"Missing market_data section in input JSON (share price, shares outstanding)Add the market_data object with share_price, shares_outstanding, and earnings_growth_rate fields to the input file
Show full SKILL.md (579 more words)Show less

Success Criteria

  • Forecast Accuracy: Revenue forecasts land within +/-5% of actuals; expense forecasts within +/-3% over rolling 12-month periods
  • Variance Coverage: 100% of material variances (exceeding threshold) include documented root-cause explanations and corrective action plans
  • Valuation Confidence: DCF-derived equity value falls within 15% of comparable-company and precedent-transaction benchmarks, validated through sensitivity analysis
  • Report Timeliness: All financial analysis deliverables (ratio reports, variance analyses, forecast updates) published within agreed SLA -- target 100% on-time delivery
  • Model Integrity: Every assumption in DCF and forecast models is documented with source, rationale, and last-reviewed date; WACC inputs refresh quarterly against market data
  • Stakeholder Adoption: Financial models and dashboards referenced in at least 80% of executive budget reviews, board presentations, and investment committee decisions
  • Analytical Efficiency: End-to-end analysis cycle time (data collection through report delivery) reduced by 40%+ compared to manual spreadsheet workflows, measured per reporting period

Scope & Limitations

This skill covers:

  • Quantitative financial ratio analysis across profitability, liquidity, leverage, efficiency, and valuation categories with built-in industry benchmarking
  • Discounted Cash Flow (DCF) enterprise and equity valuation using CAPM-based WACC, perpetuity growth and exit multiple terminal value methods, and two-way sensitivity analysis
  • Budget variance analysis with materiality filtering, favorable/unfavorable classification, department and category breakdowns, and executive summary generation
  • Driver-based revenue forecasting with 13-week rolling cash flow projection, base/bull/bear scenario modeling, and linear regression trend analysis

This skill does NOT cover:

  • Real-time market data feeds, live stock price retrieval, or automated data ingestion from ERP/accounting systems (all input is via static JSON files)
  • Qualitative analysis such as management quality assessment, competitive moat evaluation, ESG scoring, or regulatory risk judgment
  • Tax optimization, transfer pricing, multi-entity consolidation, or jurisdiction-specific accounting treatments (IFRS vs GAAP reconciliation)
  • Monte Carlo simulation, options pricing (Black-Scholes), credit risk modeling, or any analysis requiring external libraries beyond the Python standard library

Integration Points

Related SkillDomainIntegration Use Case
c-level-advisor/ceo-advisorC-Level AdvisoryFeed DCF valuation outputs and scenario comparisons into CEO strategic investment decisions and board-ready presentations
c-level-advisor/cto-advisorC-Level AdvisoryProvide technology investment ROI analysis and CapEx forecasts to support build-vs-buy and infrastructure scaling decisions
business-growth/revenue-operationsBusiness & GrowthConnect revenue forecasts and unit-economics metrics (CAC, LTV, payback period) to pipeline and go-to-market planning
product-team/product-managerProduct TeamSupply budget variance data and RICE-weighted financial projections for feature prioritization and resource allocation
data-analytics/data-analystData AnalyticsExport ratio analysis and forecast outputs as structured JSON for BI dashboard integration and trend visualization
project-management/project-financial-managementProject ManagementAlign budget variance analysis with project-level cost tracking, earned value management, and milestone-based funding releases

Tool Reference

scripts/ratio_calculator.py

Calculate and interpret financial ratios across 5 categories with industry benchmarking.

usage: ratio_calculator.py [-h] [--format {text,json}]
                           [--category {profitability,liquidity,leverage,efficiency,valuation}]
                           input_file

positional arguments:
  input_file            Path to JSON file with financial statement data
                        (must contain income_statement, balance_sheet,
                        cash_flow, and optionally market_data objects)

options:
  -h, --help            Show help message and exit
  --format {text,json}  Output format (default: text)
  --category {profitability,liquidity,leverage,efficiency,valuation}
                        Calculate only a specific ratio category;
                        omit to calculate all 5 categories (20 ratios)

Ratios computed: ROE, ROA, Gross Margin, Operating Margin, Net Margin, Current Ratio, Quick Ratio, Cash Ratio, Debt-to-Equity, Interest Coverage, DSCR, Asset Turnover, Inventory Turnover, Receivables Turnover, DSO, P/E, P/B, P/S, EV/EBITDA, PEG Ratio.

scripts/dcf_valuation.py

Discounted Cash Flow enterprise and equity valuation with WACC calculation and sensitivity analysis.

usage: dcf_valuation.py [-h] [--format {text,json}]
                        [--projection-years PROJECTION_YEARS]
                        input_file

positional arguments:
  input_file            Path to JSON file with valuation data
                        (must contain historical and assumptions objects)

options:
  -h, --help            Show help message and exit
  --format {text,json}  Output format (default: text)
  --projection-years PROJECTION_YEARS
                        Number of projection years; overrides the value
                        in the input file (default: 5)

Outputs: WACC (CAPM), projected revenue and FCF, terminal value (perpetuity growth + exit multiple), enterprise value, equity value, value per share, and a two-way sensitivity table (WACC vs terminal growth rate).

scripts/budget_variance_analyzer.py

Analyze actual vs budget vs prior year performance with materiality filtering and executive summaries.

usage: budget_variance_analyzer.py [-h] [--format {text,json}]
                                   [--threshold-pct THRESHOLD_PCT]
                                   [--threshold-amt THRESHOLD_AMT]
                                   input_file

positional arguments:
  input_file            Path to JSON file with budget data
                        (must contain line_items array with actual,
                        budget, and optionally prior_year values)

options:
  -h, --help            Show help message and exit
  --format {text,json}  Output format (default: text)
  --threshold-pct THRESHOLD_PCT
                        Materiality threshold as percentage (default: 10.0)
  --threshold-amt THRESHOLD_AMT
                        Materiality threshold as dollar amount (default: 50000.0)

Outputs: Executive summary (revenue/expense/net impact), all variances with favorability classification, material variances filtered by threshold, department summary, and category summary.

scripts/forecast_builder.py

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

usage: forecast_builder.py [-h] [--format {text,json}]
                           [--scenarios SCENARIOS]
                           input_file

positional arguments:
  input_file            Path to JSON file with forecast data
                        (must contain historical_periods, drivers,
                        assumptions, cash_flow_inputs, and scenarios objects)

options:
  -h, --help            Show help message and exit
  --format {text,json}  Output format (default: text)
  --scenarios SCENARIOS
                        Comma-separated list of scenarios to model
                        (default: base,bull,bear)

Outputs: Trend analysis (linear regression, growth rates, seasonality index), scenario comparison table, per-period forecast detail (revenue, COGS, gross profit, OpEx, operating income), and 13-week rolling cash flow projection with runway calculation.

© aAAaqwq, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/financial-analyst of aAAaqwq/AGI-Super-Team.

Open the folder on GitHubat commit 7cefd81

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in aAAaqwq/AGI-Super-Team, which our catalogue first saw on October 9, 2026.

Compare with similar skills

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Categories

Questions about Financial Analyst

What does Financial Analyst do?

Performs financial ratio analysis, DCF valuation, budget variance analysis, and rolling forecast construction for strategic decision-making. Financial Analyst is an agent skill from aAAaqwq/AGI-Super-Team.

When should I use Financial Analyst?

Financial Analyst fits situations like: tasks that involve Financial modeling; tasks that involve Financial analysis; tasks that involve Budgeting and forecasting.

How do I install Financial Analyst in Claude Code?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill financial-analyst -a claude-code`. Or copy the skill folder (skills/financial-analyst in aAAaqwq/AGI-Super-Team) into .claude/skills/financial-analyst in your project. Claude Code loads it when a task matches its description.

How do I install Financial Analyst in Codex?

Run `npx skills add aAAaqwq/AGI-Super-Team --skill financial-analyst -a codex`. Or copy the skill folder (skills/financial-analyst in aAAaqwq/AGI-Super-Team) into .agents/skills/financial-analyst in your project. Codex loads it when a task matches its description.

Can I use Financial Analyst in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aAAaqwq/AGI-Super-Team --skill financial-analyst -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/financial-analyst, .gemini/skills/financial-analyst, .github/skills/financial-analyst and .opencode/skills/financial-analyst in your project.

What does Financial Analyst need to run?

Going by SKILL.md and its folder, Financial Analyst needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Financial Analyst access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Financial Analyst safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Financial Analyst use?

Financial Analyst is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Financial Analyst use?

About 4.2k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Financial Analyst?

Skills that share tags, products or a category with Financial Analyst: Financial Analyst (alirezarezvani/claude-skills, 28k stars), Finance Skills (alirezarezvani/claude-skills, 28k stars), SaaS Economics and Efficiency Metrics (deanpeters/Product-Manager-Skills, 7.2k stars) and Three-Statement Model Builder (ginlix-ai/LangAlpha, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Financial Analyst?

aAAaqwq (a GitHub user) maintains it in aAAaqwq/AGI-Super-Team, which has 105 GitHub stars. The repository holds 167 skills in this directory. The repository was last updated on October 8, 2026.

Source: aAAaqwq/AGI-Super-Team on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.