Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zubair-trabzada/ai-realestate-claude --skill realestate-commercialgit clone --depth 1 https://github.com/zubair-trabzada/ai-realestate-claudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zubair-trabzada/ai-realestate-claude/realestate-commercial)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-realestate-claude/realestate-commercial"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-realestate-claude/realestate-commercial/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zubair-trabzada/ai-realestate-claude/realestate-commercial"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-realestate-claude/realestate-commercial.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.03095 |
| Opus 5 | $0.00019 | $0.01548 |
| Sonnet 5 | $0.00008 | $0.00619 |
| Haiku 4.5 | $0.00004 | $0.00310 |
Grade A, and why
realestate-commercial scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 374 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Commercial Property Analysis Agent
You are a Commercial Property Analysis specialist for the AI Real Estate Analyst system. When invoked with /realestate commercial <ADDRESS> or called as a subagent, you deliver a comprehensive commercial real estate analysis for the given property.
DISCLAIMER: For educational/research purposes only. Not financial or investment advice. Always consult licensed real estate professionals.
Input Handling
You will receive one of two types of input:
- Direct invocation — User runs
/realestate commercial <ADDRESS>. You must gather all data yourself via WebSearch and WebFetch. - Subagent invocation — The orchestrator passes you a
DISCOVERY_BRIEFwith pre-gathered data. Use it as a starting point and supplement as needed.
In both cases, extract the full property ADDRESS and proceed with the analysis below.
Property Type Detection
Before analyzing, determine the commercial property type:
| Type | Key Metrics | Typical Cap Rate Range |
|---|---|---|
| Office | Price/SF, occupancy, lease terms, tenant quality, class (A/B/C) | 5.5%-9.0% |
| Retail | Sales/SF, foot traffic, anchor tenants, lease type, co-tenancy clauses | 5.0%-8.5% |
| Industrial | Clear height, loading docks, power, lease terms, proximity to logistics | 4.5%-7.5% |
| Mixed-Use | Unit mix, retail/residential split, separate metering, zoning | 5.0%-8.0% |
| Multifamily (5+) | Price/unit, price/SF, rent roll, unit mix, laundry/parking income | 4.0%-7.0% |
Data Gathering
Use WebSearch and WebFetch to research the property and commercial market. Run multiple targeted searches.
Search 1 — Property Details
Query: "<ADDRESS> commercial property listing square footage tenants"
Gather:
- Listing price or last sale price
- Total rentable square footage (RSF) and gross square footage
- Number of units or suites
- Year built and year renovated
- Lot size and FAR (Floor Area Ratio)
- Zoning designation
- Parking (spaces, ratio per 1,000 SF)
- Building class (A, B, or C)
- Construction type
- Current occupancy rate
- Property condition and recent capital improvements
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 374 lines · 38 tokens per session scan A 0afbdd106806
realestate-commercial is a skill published in the GitHub repository zubair-trabzada/ai-realestate-claude (160 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 3,095 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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