Terminal.skills
Skills/dexter-finance
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dexter-finance

Build autonomous financial research agents that analyze stocks, SEC filings, earnings calls, and market data to produce investment reports. Use when: automating investment research, building AI-powered stock analysis, creating financial due diligence agents.

#finance#stocks#sec-filings#investment#research
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed dexter-finance v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Analyze the sales data in revenue.csv and identify trends"
  • "Create a visualization comparing Q1 vs Q2 performance metrics"

Information

Version
1.0.0
Author
terminal-skills
Category
Data & AI
License
MIT

Use Cases

→build-autonomous-financial-research-agent

Documentation

Build autonomous financial research agents using Dexter — an agent framework for deep financial analysis covering SEC filings, earnings calls, market data, and investment report generation.

Overview

Dexter follows a four-stage research pipeline: data collection (EDGAR API, market data, transcripts), analysis (financial ratios, sentiment), synthesis (cross-referencing patterns and anomalies), and report generation (investment memos in PDF/Markdown). It supports single-stock deep dives and batch research across watchlists.

Instructions

Installation

bash
npm install dexter-finance
# or: pip install dexter-finance

Set up API keys:

bash
export OPENAI_API_KEY="sk-..."        # or ANTHROPIC_API_KEY
export SEC_EDGAR_USER_AGENT="Company Name email@example.com"
export ALPHA_VANTAGE_KEY="..."         # optional, for market data

Research a Single Stock

typescript
import { DexterAgent } from "dexter-finance";

const agent = new DexterAgent({
  model: "claude-sonnet-4-20250514",
  tools: ["sec-filings", "market-data", "earnings-transcripts"],
});

const report = await agent.research({
  ticker: "AAPL",
  depth: "full",
  periods: 4,
});

console.log(report.summary);
console.log(report.recommendation);
await report.save("aapl-report.md");

SEC Filing Analysis

typescript
import { EdgarClient } from "dexter-finance";

const edgar = new EdgarClient({ userAgent: "MyApp research@example.com" });

const filing = await edgar.getFiling({ ticker: "MSFT", type: "10-K", latest: true });

console.log(filing.sections.riskFactors);
console.log(filing.sections.financialStatements);
console.log(filing.sections.mdAndA);

for (const table of filing.financialTables) {
  console.log(`${table.name}:`, table.toJSON());
}

Earnings Call Analysis

typescript
import { EarningsAnalyzer } from "dexter-finance";

const analyzer = new EarningsAnalyzer({ model: "claude-sonnet-4-20250514" });

const analysis = await analyzer.analyze({ ticker: "NVDA", quarter: "Q4-2025" });

console.log(analysis.sentiment);
console.log(analysis.guidanceChanges);
console.log(analysis.managementTone);
console.log(analysis.analystConcerns);

Financial Ratios

typescript
import { FinancialMetrics } from "dexter-finance";

const ratios = await new FinancialMetrics().calculate({ ticker: "AMZN", period: "TTM" });

console.log(ratios.profitability); // { grossMargin, operatingMargin, netMargin, roe }
console.log(ratios.valuation);     // { pe, ps, pb, evEbitda }
console.log(ratios.growth);        // { revenueYoY, epsYoY, fcfYoY }

Anomaly Detection

typescript
import { AnomalyDetector } from "dexter-finance";

const flags = await new AnomalyDetector().scan({
  ticker: "XYZ",
  checks: ["accounting-changes", "insider-trading", "guidance-cuts",
           "audit-opinions", "related-party", "revenue-recognition"],
});

for (const flag of flags) {
  console.log(`${flag.severity.toUpperCase()}: ${flag.type} — ${flag.description}`);
}

Examples

Example 1: Full Investment Memo for META

typescript
import { ReportGenerator } from "dexter-finance";

const generator = new ReportGenerator({
  model: "claude-sonnet-4-20250514",
  template: "investment-memo",
});

const report = await generator.generate({
  ticker: "META",
  sections: ["executive-summary", "business-overview", "financial-analysis",
             "competitive-position", "risk-factors", "valuation", "recommendation"],
  format: "markdown",
  maxPages: 5,
});

await report.save("meta-investment-memo.md");
await report.toPDF("meta-investment-memo.pdf");
// Produces a 5-page memo with financial tables, ratio analysis, and buy/hold/sell recommendation

Example 2: Daily Market Briefing for a Watchlist

typescript
import { BriefingAgent } from "dexter-finance";

const briefing = new BriefingAgent({
  model: "claude-sonnet-4-20250514",
  watchlist: ["AAPL", "GOOGL", "MSFT", "AMZN", "NVDA"],
});

const daily = await briefing.generate({
  includePreMarket: true,
  includeEarningsCalendar: true,
  includeMacroEvents: true,
});

console.log(daily.marketOverview);
console.log(daily.watchlistMoves);
console.log(daily.earningsToday);
// Output: structured briefing with price changes, upcoming earnings, and macro events

Guidelines

  • Set SEC_EDGAR_USER_AGENT to a valid company/email — EDGAR rate-limits anonymous requests
  • Use depth: "quick" for screening, "full" for deep dives — saves tokens and time
  • Batch research runs concurrently — set concurrency based on your API rate limits
  • Anomaly detection is most useful on small/mid-cap stocks where coverage is thin
  • Combine with a scheduler (cron) for automated daily briefings
  • Always validate AI-generated financial analysis — treat outputs as research drafts, not advice