InvestIQ: local skills and tools

A self-contained investing research agent based on the Microsoft Agent Framework Agent Harness, local skills, and custom market-data and research tools. Anthropic provides the model through Azure API Management (APIM), and Microsoft Foundry hosts the agent.

For the underlying framework and hosting guidance, see:

• Agent Harness concepts (Python)
• Get started with Agent Harness
• Host an agent (Python)

What’s here

This repository demonstrates an agent that researches stocks and ETFs and explains investing concepts. The model selects relevant local Markdown skills, loads their instructions on demand, and decides which tools are needed to answer each question.

main.py                    Harness factory, two tools, and Responses host
skills/stock-analysis/     Local stock-analysis SKILL.md
skills/etf-analysis/       Local ETF-analysis SKILL.md
requirements.txt          Standalone dependencies
azure.yaml                Foundry hosted-agent deployment configuration

The model selects and loads skills on demand and decides which tools to call:

• fetch_market_data(ticker, function, period) uses Yahoo Finance for quote, fundamentals, price_history, and etf_holdings. History ranges: 5d, 1mo, 3mo, 6mo, 1y, 2y. Returned data includes ticker, function, range, and UTC fetch timestamp.
• search_research(query, kind, limit) uses the DuckDuckGo backend explicitly for news or web. It returns up to eight qualitative snippets with source links, dates when available, and fetch time.

Answers cite the fetched market data and research sources and report missing data or tool errors. Concept questions can be answered without data tools. Harness planning, todos, compaction, and history are enabled, with initial mode execute. Built-in web search and filesystem access are disabled; local skill loading is explicitly allowed. Analysis is educational, not investment advice.

Architecture diagram

A Responses API request reaches the Harness agent hosted in Microsoft Foundry, which calls Anthropic through APIM. The same agent selects skills, calls tools, and produces a sourced answer.

• Skill selection: SkillsProvider discovers local Markdown skills and exposes their names and descriptions. The model matches them to the question by meaning, then reads the relevant full instructions on demand before stock or ETF analysis: stock-analysis for company research,