smithery.ai

defeatbeta-analyst

Professional financial analysis using 60+ market data APIs. Use for: company fundamentals (revenue, margins, EPS, balance sheet), valuation (P/E, P/B, P/S, PEG, DCF, intrinsic value), profitability (ROE, ROA, ROIC), growth trends (YoY revenue/earnings/FCF), earnings transcripts (key data, changes, guidance), industry benchmarking, segment/geography revenue breakdown, business model analysis, competitive landscape, risk analysis, investment thesis, bull/bear scenarios. Trigger on: stock tickers,…

First seen Mar 29, 2026

Installation

$ npx skills add https://smithery.ai

Summary

  • Professional financial analysis using 60+ market data APIs.
  • Use for: company fundamentals (revenue, margins, EPS, balance sheet), valuation (P/E, P/B, P/S, PEG, DCF, intrinsic value), profitability (ROE, ROA, ROIC), growth trends (YoY revenue/earnings/FCF), earnings transcripts (key data, changes, guidance), industry benchmarking, segment/geography revenue breakdown, business model analysis, competitive landscape, risk analysis, investment thesis, bull/bear scenarios.
  • Trigger on: stock tickers, company names, financial metrics, or any investment research request.
  • DO NOT trigger for: general economics, non-public companies, crypto/commodities with no equity ticker.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Skill metadata

Parsed from SKILL.md frontmatter.

CompatibilityRequires defeatbeta MCP server

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 6,734 B
  • docs SUMMARY.md 444 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Financial Analyst

Professional-grade financial analysis using historical market data and comprehensive financial metrics from the defeatbeta dataset.

Defaults

Unless the user specifies otherwise:

  • Always call getlatestdataupdatedate first — this is "today" for all relative time queries
  • Default analysis: When given only a ticker (e.g., /defeatbeta-analyst AAPL), run Template 1: Quick Investment Screening
  • ROIC and Equity Multiplier: Not applicable to banks/financial institutions — check sector in profile first
  • Date format: "YYYY-MM-DD"
  • Data limits: Price/valuation APIs return max 1000 rows — use date ranges for large datasets

Template Selection

Choose the template that best matches the user's request. Read [analysis-templates.md](./references/analysis-templates.md) for the full workflow of any template.

User request Template
Quick check / "should I look at this stock?" T1: Quick Investment Screening
Full company deep-dive T2: Full Fundamental Analysis
Overvalued / undervalued / P/E / P/B / DCF T3: Valuation-Focused or T10: DCF Valuation
Revenue growth / earnings quality / ROIC T4: Growth Quality Assessment
ROE decomposition / DuPont T5: DuPont Analysis
Margin trends / vs peers T6: Margin Analysis & Peer Comparison
Accruals / FCF quality / working capital T7: Earnings Quality Assessment
Industry positioning T8: Industry Positioning Analysis
Latest earnings release numbers T9: Quarterly Earnings Analysis
"What did management report this quarter?" T11: Extract Key Financial Data from Earnings Call
"What changed vs last quarter/year?" T12: Financial Metric Changes from Earnings Call
"What is management's guidance/outlook?" T13: Financial Metric Forecasts from Earnings Call
"What does this company do?" / business model T14: Business Understanding
Revenue segments / geography breakdown T15: Revenue Breakdown
Industry trends / tailwinds / headwinds T16: Industry Context
Competitors / moat / pricing power T17: Competitive Landscape
Balance sheet strength / debt / FCF quality T18: Financial Quality
Risks / downside / what could go wrong T19: Risks and Downside
Management track record / capital allocation T20: Management and Execution
Bull case / bear case / scenarios T21: Bull and Bear Scenarios
How to value / valuation assumptions T22: Valuation Framework
Long-term thesis / why invest / must go right T23: Long-Term Investment Thesis

API Gotchas

REQUIRED: Before calling any API, read the relevant section in [defeatbeta-api-reference.md](./references/defeatbeta-api-reference.md) to confirm the correct parameters and response schema.

  1. Fiscal periods: Earnings transcripts use fiscal periods (may differ from calendar) — specify both fiscalyear and fiscalquarter
  2. SEC filing access: Must use secuseragent field value as User-Agent header when accessing SEC URLs (SEC blocks without it)

Rendering Financial Statements

When displaying results from getstockincomestatement, getstockbalancesheet, or getstock*cashflow, you MUST follow these rules. The response contains a statement array where each row has label, indent, is_section, and values fields.

Rules:

  1. Preserve row order — never reorder, skip, or merge rows
  2. Indent — prefix each label with indent × 2 spaces (indent=0 → no prefix, indent=1 → 2 spaces, indent=2 → 4 spaces, etc.)
  3. is_section=true — render the label in bold; this row is a section header and the rows immediately below it (with indent = this.indent + 1) are its children
  4. is_section=false — render the label in normal weight
  5. values[i] corresponds to periods[i]; null means data not available
  6. Do NOT add calculated rows (e.g. margin %) that are not present in the data

Example — given these rows:

label indent is_section
Total Revenue 0 true
Operating Revenue 1 false
Cost of Revenue 0 false
Operating Expense 0 true
SG&A 1 false
R&D 1 false

Correct rendered output:

Item 2025Q4 2025Q3
Total Revenue 10,270 9,246
  Operating Revenue 10,270 9,246
Cost of Revenue 4,693 4,466
Operating Expense 3,825 3,510
  SG&A 1,198 1,069
  R&D 2,330 2,139

Reference Documentation

Detailed step-by-step workflows (T1–T23) → [analysis-templates.md](./references/analysis-templates.md)

API parameters, response schemas, examples (60+ APIs) → [defeatbeta-api-reference.md](./references/defeatbeta-api-reference.md)