d-wwei/openclaw-financial-services · Archived

fsi-lseg-equity-research

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.

First seen Jun 21, 2026

Installation

$ npx skills add d-wwei/openclaw-financial-services --skill fsi-lseg-equity-research

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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
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 15
License LICENSE
Default branch main
Open issues 0
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,763 B
  • docs SUMMARY.md 337 B

History

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

SKILL.md

Equity Research Analysis

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.

Core Principles

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.

Available MCP Tools

  • qaibesconsensus — IBES analyst consensus estimates and actuals. Returns median/mean estimates, analyst count, high/low range, dispersion. Supports EPS, Revenue, EBITDA, DPS.
  • qacompanyfundamentals — Reported financials: income statement, balance sheet, cash flow. Historical fiscal year data for ratio analysis.
  • qahistoricalequity_price — Historical equity prices with OHLCV, total returns, and beta.
  • tscchistoricalpricing_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.

Tool Chaining Workflow

  1. Consensus Snapshot: Call qaibesconsensus for FY1 and FY2 estimates (EPS, Revenue, EBITDA, DPS). Note analyst count and dispersion.
  2. Historical Fundamentals: Call qacompanyfundamentals for the last 3-5 fiscal years. Extract revenue growth, margins, leverage, returns (ROE, ROIC).
  3. Price Performance: Call qahistoricalequity_price for 1Y history. Compute YTD return, 1Y return, 52-week range position, beta.
  4. Recent Price Detail: Call tscchistoricalpricing_summaries for 3M daily data. Assess volume trends and recent momentum.
  5. Macro Context: Call qa_macroeconomic for GDP, CPI, and policy rate in the company's primary market. Summarize whether macro is tailwind or headwind.
  6. Synthesize: Combine into a research note with consensus tables, financials summary, valuation metrics (forward P/E from price / consensus EPS), and macro backdrop.

Output Format

Consensus Estimates

Metric FY1 FY2 # Analysts Dispersion
EPS ... ... ... ...%
Revenue (M) ... ... ... ...%
EBITDA (M) ... ... ... ...%

Financials Summary

Metric FY-2 FY-1 FY0 (LTM) Trend
Revenue (M) ... ... ... ...
Gross Margin ... ... ... ...
Operating Margin ... ... ... ...
ROE ... ... ... ...
Net Debt/EBITDA ... ... ... ...

Valuation Summary

Metric Current Context
Forward P/E ... vs sector/history
EV/EBITDA ... vs sector/history
Dividend Yield ... ...

Investment Thesis

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).