ask-q6-business-model

Name: ask-q6-business-model
Availability: InStock
Author: shadowinlife

Analyze multi-year mainbz data to assess business model stability and concentration risks.

2|1|Updated Apr 17, 2026

Search Tags:#evidence #business-model #stock-analysis #annual-report #mainbz #multi-year

Ready to run

npx skills add https://github.com/shadowinlife/nano_quant_skills --skill ask-q6-business-model

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ask-q6-business-model
Source: https://github.com/shadowinlife/nano_quant_skills/tree/main/2min-company-analysis/ask-q6-business-model
Command: npx skills add https://github.com/shadowinlife/nano_quant_skills --skill ask-q6-business-model

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill helps analysts quickly assess the stability and diversification of a company's business model by aggregating multi-year main business segment data (from annual reports and company disclosures) to identify concentration risks and track evolution over time.

Core Features & Use Cases

Multi-year mainbz analysis to measure top-segment share, segment changes, and the appearance of new business lines.
Evidence harness that combines annual reports and segment disclosures to support a ready/partial assessment with traceable sources.
Use Case: evaluate whether revenue depends on a single product or client, or whether a second curve is emerging, to inform investment or strategic decisions.

Quick Start

Run the Q6 business-model analysis script with a stock code to generate an evidence-backed readiness assessment.

Frequently Asked Questions about ask-q6-business-model

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I assess business model stability using multi-year mainbz data?▼

Evaluating business model stability involves aggregating multi-year mainbz data from annual reports to detect product or client concentration, track segment changes, and flag new revenue items. This produces a structured assessment with readiness status and traceable evidence sources.

What is the best way to track segment changes across multiple years in annual reports?▼

Tracking segment changes across multiple years involves applying analysis across stock codes to identify top-segment share variations and the emergence of new business lines. This combines annual report disclosures to support an assessment with traceable evidence sources.

Can I detect product or client concentration risks from company disclosures?▼

Yes, detecting product or client concentration risks is achieved by analyzing multi-year mainbz data to measure top-segment share and evaluate revenue dependency. This combines annual reports and segment disclosures to produce an evidence-backed assessment for decision making.

How do I identify if a second revenue curve is emerging from historical business segments?▼

Identifying an emerging second revenue curve involves tracking the appearance of new business lines and segment changes across multiple years of mainbz data. This flags new revenue items and produces a structured assessment with rating and traceable evidence sources.

Does this stock analysis approach require specific dependencies to run?▼

No, this stock analysis approach requires no external dependencies to run. The script operates independently across stock codes, aggregating evidence from annual reports and segment disclosures to produce a structured readiness status for decision making.