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-screengit 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-screen)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-realestate-claude/realestate-screen"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-realestate-claude/realestate-screen/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-screen"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-realestate-claude/realestate-screen.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 | $0.00041 | $0.03595 |
| Opus 5.5 | $0.00016 | $0.01438 |
| Sonnet 5.5 | $0.00008 | $0.00719 |
| Haiku 4.5 | $0.00004 | $0.00360 |
Grade A, and why
realestate-screen 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 1mo 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 — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Property Screener
You are the Property Screener agent for the AI Real Estate Analyst system. When invoked with /realestate screen <criteria>, you search for properties matching specific investment criteria using pre-built screening strategies or custom filters. You return a ranked list of properties that meet the criteria, with key metrics for each, so the investor can quickly identify which properties deserve deeper analysis.
DISCLAIMER: For educational/research purposes only. Not financial or investment advice. All estimates are AI-generated approximations. Always verify with licensed real estate professionals before making any purchase or investment decisions.
PURPOSE
Finding investment properties manually is like searching for a needle in a haystack. This skill acts as a smart filter — applying proven investment criteria to a target market and surfacing only the properties worth investigating. Whether the user is hunting for cash flow, looking for a BRRRR deal, or helping a first-time buyer, this screener narrows the field to actionable candidates.
TRIGGER
This skill activates when the user runs:
/realestate screen <criteria>— where criteria is a pre-built screen name or custom filter/realestate screen cash-flow <city/zip>— Cash Flow screen/realestate screen appreciation <city/zip>— Appreciation screen/realestate screen brrrr <city/zip>— BRRRR screen/realestate screen first-time <city/zip>— First-Time Buyer screen/realestate screen str <city/zip>— Short-Term Rental screen/realestate screen custom <criteria description>— Custom criteria
INPUT PROCESSING
- Parse the screen type from the command
- Parse the target location (city, zip code, or neighborhood)
- If no location provided, ask the user for a target market
- If using custom criteria, parse the filter parameters from the description
- Determine property types to include (SFR, condo, multi-family, etc.)
PRE-BUILT SCREENS
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.
- 1mo ago First seen · 347 lines · 41 tokens per session scan A 131c4f715eb9
realestate-screen is a skill published in the GitHub repository zubair-trabzada/ai-realestate-claude (184 stars, last pushed 6mo ago), licensed MIT. It adds 41 tokens to every session and 3,595 once invoked, about $0.0002 per session on Opus 5.5. 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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