Works with the AI tools you already use
Local Asset Intelligence Engine — High-ROI Acquisition Syste
by Lorky Rongai
A business intelligence engine for identifying and acquiring undervalued local assets using data science and SQL.
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See it in action
You say
Identify undervalued assets in the Austin, Texas boutique gym niche.
Your agent does
KATEGORI B. 1. Digital Footprint Audit: Sourced via Texas SOS filings and Travis County tax records. 2. Logic Matrix: SELECT * FROM gym_data WHERE physical_status = 'closed' AND social_engagement > 0.7. 3. Valuation: High liquidation value assigned to 5,000+ email lists and high-end equipment.
What you get
About this skill
The problem
Identifying profitable business opportunities usually requires manual digging through fragmented public records and registries. Most developers and entrepreneurs miss high-value distressed assets because they lack the data science logic to filter noise from actual acquisition targets.
WHAT YOU RECEIVE UPON PURCHASE (PROFESSIONAL GRADE BUNDLE):
- The Universal AI Skill (.md): A master logic framework that leverages advanced variables to force the AI into deep-data analysis. This versatile asset can be deployed across any major LLM by tailoring parameters to each model's specific logic requirements.
- The Claude-Optimized Skill (.md): A high-performance version specifically re-engineered for the Claude ecosystem to ensure maximum reasoning accuracy.
- The Openclaw-Optimized Skill (.md): A precision-tuned version built for seamless integration within the Openclaw agent environment.
- README.txt Quick Start Guide: A zero-friction deployment manual to get your asset hunting engine running in under three minutes.
What it does
- Performs deep audits of local business registries and public data to find undervalued physical or digital assets.
- Generates SQL and Excel logic to filter large datasets for high-ROI signals like expired domains with high authority.
- Produces technical SOPs for asset acquisition, including outreach strategies and valuation frameworks.
- Maps data-to-dollar workflows using Mermaid.js diagrams for visual architecture.
- Classifies business risks and implements a "Why Layer" to explain market mechanics behind ROI claims.
Frameworks & tools
Python for data extraction, SQL for filtration logic, Excel for financial modeling, and Mermaid.js for process mapping.
Why this beats prompting it yourself
Standard prompts result in generic business advice. This skill enforces a professional-grade BI framework that mandates granular technical details, strict data integrity, and specific extraction methods that most LLMs skip by default. It prevents lazy summaries and forces the agent to act as a senior consultant rather than a chatbot.
Use cases
- Finding closed local businesses with valuable equipment or location rights for brokerage.
- Identifying expired domains with established SEO authority in specific service niches.
- Analyzing local government filings to spot distressed companies before they hit public auctions.
- Developing automated data pipelines to monitor niche-specific liquidation notices.
Known limitations
Output is classified as Category B (high-risk). Requires human oversight for final financial valuations, legal contracts, and physical inspections. Digital data may have a 5 to 15 percent latency.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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