Track and update action item status from natural language updates like 'Seanのメールには返信しておいた' or 'Lu対応予定'. Integrates with daily-comms-ops workflow. Use when managing action items across communication channels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Professional financial analysis skill for investment decisions, budgeting, financial statement analysis, and business plan evaluation. Use this skill when you need to analyze financial ratios (profitability, safety, efficiency), calculate DCF/NPV/IRR valuations, perform budget variance analysis, or conduct sensitivity/scenario analysis. Triggers include "analyze financial statements", "calculate DCF", "investment evaluation", "budget variance", "財務分析", "投資評価", "予算差異分析".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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A comprehensive financial analysis toolkit that enables systematic evaluation of financial health, investment opportunities, and business performance. This skill combines quantitative analysis with professional reporting to support informed decision-making.
Core Capabilities:
Output Formats:
pandas (for CSV data handling and variance analysis)numpy (for numerical calculations)matplotlib (optional, for visualization charts)Use this skill when you need to:
Analyze Financial Statements
Evaluate Investment Opportunities
Analyze Budget Performance
Assess Risk and Scenarios
Analyze financial ratios from structured data to assess company health.
Accept data in JSON or CSV format:
{
"company": "ABC Corp",
"period": "2024-Q4",
"balance_sheet": {
"total_assets": 100000000,
"current_assets": 40000000,
"cash": 10000000,
"inventory": 15000000,
"receivables": 12000000,
"total_liabilities": 60000000,
"current_liabilities": 25000000,
"long_term_debt": 30000000,
"total_equity": 40000000
},
"income_statement": {
"revenue": 80000000,
"cost_of_goods_sold": 48000000,
"gross_profit": 32000000,
"operating_expenses": 16000000,
"operating_income": 16000000,
"interest_expense": 2000000,
"net_income": 10000000
}
}
Read the input file and validate required fields:
Profitability Ratios:
| Ratio | Formula | Interpretation |
|---|---|---|
| ROE (Return on Equity) | Net Income / Total Equity | >15% is generally good |
| ROA (Return on Assets) | Net Income / Total Assets | >5% is acceptable |
| Gross Margin | Gross Profit / Revenue | Industry-dependent |
| Operating Margin | Operating Income / Revenue | >10% is strong |
| Net Profit Margin | Net Income / Revenue | >5% is healthy |
Safety/Liquidity Ratios:
| Ratio | Formula | Interpretation |
|---|---|---|
| Current Ratio | Current Assets / Current Liabilities | >1.5 is healthy |
| Quick Ratio | (Current Assets - Inventory) / Current Liabilities | >1.0 is acceptable |
| Debt-to-Equity | Total Liabilities / Total Equity | <1.0 is conservative |
| Interest Coverage | Operating Income / Interest Expense | >3.0 provides safety margin |
Efficiency Ratios:
| Ratio | Formula | Interpretation |
|---|---|---|
| Asset Turnover | Revenue / Total Assets | Higher is more efficient |
| Inventory Turnover | COGS / Inventory | Higher means faster sales |
| Receivables Turnover | Revenue / Receivables | Higher means faster collection |
| Working Capital Turnover | Revenue / (Current Assets - Current Liabilities) | Measures working capital efficiency |
For each ratio:
Generate a radar chart showing:
Use assets/financial_report_template_ja.md or assets/financial_report_template_en.md as the template. Include:
Perform Discounted Cash Flow analysis for investment decisions.
{
"project_name": "New Factory Investment",
"initial_investment": 50000000,
"projected_cash_flows": [10000000, 15000000, 20000000, 22000000, 25000000],
"terminal_growth_rate": 0.02,
"discount_rate": 0.10,
"analysis_years": 5
}
NPV = -Initial Investment + Σ(CFt / (1 + r)^t) + Terminal Value / (1 + r)^n
Where:
- CFt = Cash flow at time t
- r = Discount rate
- n = Number of years
Terminal Value Methods:
Find the discount rate where NPV = 0 using Newton-Raphson iteration:
def calculate_irr(cash_flows, guess=0.10, tolerance=0.0001):
rate = guess
for _ in range(100):
npv = sum(cf / (1 + rate)**i for i, cf in enumerate(cash_flows))
if abs(npv) < tolerance:
return rate
derivative = sum(-i * cf / (1 + rate)**(i+1) for i, cf in enumerate(cash_flows))
rate = rate - npv / derivative
return rate
WACC = (E/V × Re) + (D/V × Rd × (1 - Tc))
Where:
- E = Market value of equity
- D = Market value of debt
- V = E + D
- Re = Cost of equity
- Rd = Cost of debt
- Tc = Tax rate
Simple Payback:
Payback = Years until cumulative cash flow >= initial investment
Discounted Payback:
Payback = Years until cumulative discounted cash flow >= initial investment
| Metric | Accept Criteria | Interpretation |
|---|---|---|
| NPV | > 0 | Project creates value |
| IRR | > WACC (or hurdle rate) | Returns exceed cost of capital |
| Payback | < Target period | Quick capital recovery |
Refer to references/dcf_methodology.md for detailed methodology.
Compare actual results to budget and identify causes.
Two data files: actual.csv and budget.csv with matching structure:
category,item,amount
Revenue,Product Sales,85000000
Revenue,Service Revenue,15000000
Cost,Material Costs,35000000
Cost,Labor Costs,25000000
Expenses,Marketing,8000000
Expenses,Administration,5000000
For each line item:
Variance = Actual - Budget
Variance % = (Actual - Budget) / Budget × 100
Classify variances:
For volume-driven items, decompose into:
Flag items where:
For each material variance, document:
Use assets/budget_variance_template_ja.md or assets/budget_variance_template_en.md:
Assess risk by varying key assumptions.
Common sensitivity variables:
For each key variable:
| Variable | -20% | -10% | Base | +10% | +20% |
|-------------|---------|---------|---------|---------|---------|
| Revenue | $2.1M | $3.5M | $4.8M | $6.2M | $7.5M |
| Costs | $6.5M | $5.7M | $4.8M | $4.0M | $3.1M |
Rank variables by impact on NPV:
Define scenarios:
For each scenario:
Calculate break-even points:
Refer to references/sensitivity_analysis_guide.md for methodology details.
The scripts/financial_analyzer.py provides CLI automation:
python scripts/financial_analyzer.py ratios <input.json> --output report.md --visualize
python scripts/financial_analyzer.py dcf <project.json> --discount-rate 0.10 --terminal-method gordon
python scripts/financial_analyzer.py variance <actual.csv> <budget.csv> --threshold 5
python scripts/financial_analyzer.py sensitivity <model.json> --variables revenue,costs --range 20
scripts/financial_analyzer.py - Main analysis automation scriptreferences/financial_ratios_guide.md - Detailed ratio definitions and benchmarksreferences/dcf_methodology.md - DCF calculation methodologyreferences/sensitivity_analysis_guide.md - Sensitivity/scenario analysis guideassets/financial_report_template_ja.md - Japanese financial report templateassets/financial_report_template_en.md - English financial report templateassets/budget_variance_template_ja.md - Japanese variance report templateassets/budget_variance_template_en.md - English variance report template| Category | Ratio | Formula |
|---|---|---|
| Profitability | ROE | Net Income / Equity |
| Profitability | ROA | Net Income / Assets |
| Profitability | Gross Margin | Gross Profit / Revenue |
| Profitability | Operating Margin | Operating Income / Revenue |
| Safety | Current Ratio | Current Assets / Current Liabilities |
| Safety | Quick Ratio | (Current Assets - Inventory) / Current Liabilities |
| Safety | D/E Ratio | Total Debt / Equity |
| Efficiency | Asset Turnover | Revenue / Assets |
| Efficiency | Inventory Turnover | COGS / Inventory |
| Metric | Accept If | Reject If |
|---|---|---|
| NPV | > 0 | < 0 |
| IRR | > WACC | < WACC |
| Payback | < Target | > Target |
| Variance Type | Favorable | Unfavorable |
|---|---|---|
| Revenue | Actual > Budget | Actual < Budget |
| Cost | Actual < Budget | Actual > Budget |
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Track and update action item status from natural language updates like 'Seanのメールには返信しておいた' or 'Lu対応予定'. Integrates with daily-comms-ops workflow. Use when managing action items across communication channels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI活用提案の専門コンサルタントスキル。LLM・AIエージェントの活用事例知識を基に、業界別、部門別、シーン別に最適なAI導入提案を行います。システム化、業務改善、効率化を得意とし、具体的な活用事例、期待効果、導入ステップを含む実践的な提案を提供します。Use when proposing AI/LLM adoption strategies for specific industries, departments, or business scenarios.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate AI-powered BPO service proposals for Japanese companies in the US market. Use when creating outsourcing proposals with AI service menus, ROI estimation, implementation roadmaps, and bilingual (JA/EN) proposal documents for 在米日系企業向けAI実装.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI(LLM)が生成した日本語テキストの「AI臭」を検出・診断し、人間らしい文章にリライトするスキル。 Use when: 「AIっぽい文章を直して」「人間らしくリライトして」「AI臭を消して」「この文章をもっと自然にして」 「テキストのAI感を減らして」「機械っぽさを取りたい」 "make this sound more human", "remove AI tone", "humanize this text", "detect if this is AI-generated". 6つのAI特有パターン(視覚的マーカー、単調なリズム、マニュアル的構成、非コミット姿勢、抽象語の濫用、 定型メタファー)を正規表現ベースで検出し、0-100のAI臭スコアを算出。3つの人間化技法 (バランスを崩す・客観を崩す・論理を崩す)でリライトを実行する。 Note: 検出スクリプトは日本語テキスト専用。英語テキストの場合はClaude自身がreferences/を参照して分析・リライトする。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate audit-ready internal control design documents from As-Is business process inventories. Produces control IDs, assertion mappings, procedures, SoD analysis, KPIs, materiality thresholds, and implementation roadmaps. Use when building internal controls for new audit engagements, SOX/J-SOX compliance, or process improvement initiatives.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review audit-related documents (control design documents, bottleneck analyses, requirements definitions, etc.) for quality, scoring them 0-100 with a severity-rated findings list. Use when reviewing audit documents, checking control design quality, or verifying cross-document consistency. Supports documents governed by US GAAP, IFRS, or J-GAAP.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.