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Generate comprehensive equity research snapshots combining analyst consensus estimates, company fundamentals, historical prices, and macroeconomic context. Use when researching stocks, comparing estimates to actuals, analyzing company financials, assessing equity valuations, or building investment cases.
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请帮我安装 askskill 上的 "equity-research" 技能： 1. 下载 https://raw.githubusercontent.com/anthropics/financial-services/main/plugins/partner-built/lseg/skills/equity-research/SKILL.md 2. 保存为 ~/.claude/skills/equity-research/SKILL.md 3. 装好后重载技能，告诉我可以用了
You are an expert equity research analyst. Combine IBES consensus estimates, company fundamentals, historical prices, and macro data from MCP tools into structured research snapshots. Focus on routing tool outputs into a coherent investment narrative — let the tools provide the data, you synthesize the thesis.
Every piece of data must connect to an investment thesis. Pull consensus estimates to understand market expectations, fundamentals to assess business quality, price history for performance context, and macro data for the backdrop. The key question is always: where might consensus be wrong? Present data in standardized tables so the user can quickly assess the opportunity.
qa_ibes_consensus — IBES analyst consensus estimates and actuals. Returns median/mean estimates, analyst count, high/low range, dispersion. Supports EPS, Revenue, EBITDA, DPS.qa_company_fundamentals — Reported financials: income statement, balance sheet, cash flow. Historical fiscal year data for ratio analysis.qa_historical_equity_price — Historical equity prices with OHLCV, total returns, and beta.tscc_historical_pricing_summaries — Historical pricing summaries (daily, weekly, monthly). Alternative/supplement for price history.qa_macroeconomic — Macro indicators (GDP, CPI, unemployment, PMI). Use to establish the economic backdrop for the company's sector.qa_ibes_consensus for FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion.qa_company_fundamentals for the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC).qa_historical_equity_price for 1Y history. Compute YTD return, 1Y return, 52-week range position, beta.tscc_historical_pricing_summaries for 3M daily data. Assess volume trends and recent momentum.qa_macroeconomic for GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind.| Metric | FY1 | FY2 | # Analysts | Dispersion |
|---|---|---|---|---|
| EPS | ... | ... | ... | ...% |
| Revenue (M) | ... | ... | ... | ...% |
| EBITDA (M) | ... | ... | ... | ...% |
| Metric | FY-2 | FY-1 | FY0 (LTM) | Trend |
|---|---|---|---|---|
| Revenue (M) | ... | ... | ... | ... |
| Gross Margin | ... | ... | ... | ... |
| Operating Margin | ... | ... | ... | ... |
| ROE | ... | ... | ... | ... |
| Net Debt/EBITDA | ... | ... | ... | ... |
| Metric | Current | Context |
|---|---|---|
| Forward P/E | ... | vs sector/history |
| EV/EBITDA | ... | vs sector/history |
| Dividend Yield | ... | ... |
Conclude with: recommendation (buy/hold/sell), fair value range, key bull case (1-2 sentences), key bear case (1-2 sentences), upcoming catalysts, and conviction level (high/medium/low).
Build pre-earnings analysis with estimate models, scenario frameworks, and key metrics to watch. Use before a company reports quarterly earnings to prepare positioning notes, set up bull/bear scenarios, and identify what will move the stock. Triggers on "earnings preview", "what to watch for [company] earnings", "pre-earnings", "earnings setup", or "preview Q[X] for [company]".
Root-cause a reconciliation break to its source transaction or posting — follow the audit trail from the break row back to the originating entry on each side and state what differs and why. Use after gl-recon has classified a break.
Audit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model (including financial-model integrity checks like BS balance, cash tie-out, and logic sanity). Triggers on "audit this sheet", "check my formulas", "find formula errors", "QA this spreadsheet", "sanity check this", "debug model", "model check", "model won't balance", "something's off in my model", "model review".
Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. **Perfect for:** - Public company valuation (M&A, investment analysis) - Benchmarking performance vs. industry peers - Pricing IPOs or funding rounds - Identifying valuation outliers (over/under-valued) - Supporting investment committee presentations - Creating sector overview reports **Not ideal for:** - Private companies without comparable public peers - Highly diversified conglomerates - Distressed/bankrupt companies - Pre-revenue startups - Companies with unique business models
Real DCF (Discounted Cash Flow) model creation for equity valuation. Retrieves financial data from SEC filings and analyst reports, builds comprehensive cash flow projections with proper WACC calculations, performs sensitivity analysis, and outputs professional Excel models with executive summaries. Use when users need to value a company using DCF methodology, request intrinsic value analysis, or ask for detailed financial modeling with growth projections and terminal value calculations.
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when user requests "earnings update", "quarterly update", "earnings analysis", "Q1/Q2/Q3/Q4 results", or post-earnings report.