mit einem Klick
setup
// Walk through initial setup and authentication for this Daloopa starter kit
// Walk through initial setup and authentication for this Daloopa starter kit
Build an industry comp sheet Excel model with deep operational KPIs
Trading comparables analysis with peer multiples and implied valuation
Rapid first-read earnings flash for a given company
Pre-earnings preparation report for the night before a company reports
Full earnings analysis with guidance tracking for a given company
Bull/bear/base case scenario framework for a given company
| name | setup |
| description | Walk through initial setup and authentication for this Daloopa starter kit |
Walk the user through setting up this Daloopa starter kit step by step. Be conversational and helpful.
Confirm Claude Code is running (if the user is seeing this, it is — tell them they're good).
Check if required packages are installed. Offer to install them:
pip3 install -r requirements.txt
This installs: requests, beautifulsoup4, html2text, yfinance, openpyxl, python-docx, docxtpl, matplotlib, fredapi.
These are needed for market data, chart generation, Excel model building, and Word document rendering.
Ask the user which authentication method they'd like to use:
Option A: OAuth (Recommended)
.mcp.json is already configured for OAuthOption B: API Key
.env with their key: DALOOPA_API_KEY=<their_key>daloopa entry in .mcp.json to include the API key header (keep the daloopa-docs entry as-is):{
"mcpServers": {
"daloopa": {
"type": "http",
"url": "https://mcp.daloopa.com/server/mcp",
"headers": {
"x-api-key": "${DALOOPA_API_KEY}"
}
},
"daloopa-docs": {
"type": "http",
"url": "https://docs.daloopa.com/mcp"
}
}
}
Ask if they want to configure optional API keys for enhanced functionality:
FRED API Key (recommended for DCF/valuation work):
.env: FRED_API_KEY=<their_key>This project connects to two Daloopa MCP servers:
mcp.daloopa.com/server/mcp) — Financial data (fundamentals, KPIs, SEC filings)docs.daloopa.com/mcp) — Daloopa knowledgebase (API docs, how-tos, usage help)Run a quick test by calling discover_companies with a well-known ticker like "AAPL" to confirm the data MCP server is connected and responding. Show the user the result.
If the user has a market data MCP configured (e.g., a financial data provider with stock quote tools), test it by looking up AAPL.
If no market data MCP is available, fall back to the infra script: python infra/market_data.py quote AAPL
This should return current price, market cap, etc.
Run: python scripts/create_template.py
This creates the research note template at templates/research_note.docx.
Tell the user about the available slash commands:
Building Block Skills (markdown reports):
/earnings-review TICKER — Full earnings analysis with guidance tracking/tearsheet TICKER — Quick one-page company overview/industry TICKER1 TICKER2 ... — Cross-company comparison/bull-bear TICKER — Bull/bear/base scenario framework/guidance-tracker TICKER — Track management guidance accuracy/inflection TICKER — Auto-detect metric accelerations/decelerations/capital-allocation TICKER — Buybacks, dividends, shareholder yield/dcf TICKER — DCF valuation with sensitivity analysis/comps TICKER — Trading comparables with peer multiplesInvestment Deliverables (.docx, .xlsx, .pdf):
/research-note TICKER — Professional Word research note/build-model TICKER — Multi-tab Excel financial model/initiate TICKER — Both research note + Excel model (initiating coverage)/update TICKER — Refresh existing coverage with latest data/ib-deck TICKER — Institutional-grade pitch deck (HTML → PDF)All output is saved to the reports/ directory.
Suggest they try /tearsheet AAPL as a quick first test to see everything working end-to-end.