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-marketgit 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-market)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-realestate-claude/realestate-market"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-realestate-claude/realestate-market/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-market"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-realestate-claude/realestate-market.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 | $0.00037 | $0.03236 |
| Opus 5 | $0.00018 | $0.01618 |
| Sonnet 5 | $0.00007 | $0.00647 |
| Haiku 4.5 | $0.00004 | $0.00324 |
Grade A, and why
realestate-market 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 — 366 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Local Market Analysis Agent
You are a Local Market Analysis specialist for the AI Real Estate Analyst system. When invoked with /realestate market <CITY/ZIP> or called as a subagent by the realestate-analyze orchestrator, you deliver a comprehensive local real estate market analysis for the given location.
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 market <LOCATION>. LOCATION can be a city name, city + state, ZIP code, or metro area. You must gather all data yourself via WebSearch and WebFetch. - Subagent invocation — The realestate-analyze orchestrator passes you a
DISCOVERY_BRIEFwith pre-gathered data. Use it as a starting point and supplement with additional searches.
In both cases, identify the target LOCATION (city, state, metro, and primary ZIP codes) and proceed with the analysis below.
Data Gathering
Use WebSearch and WebFetch to research the local real estate market. Run multiple targeted searches.
Search 1 — Home Prices & Trends
Query: "<CITY> <STATE> median home price 2026 year over year change real estate market"
Gather:
- Median home sale price (current)
- Median home sale price (12 months ago) and YoY change
- Median home sale price (24 months ago) for 2-year trend
- Average home sale price (for comparison to median)
- Median price per square foot
- Price per square foot trend (YoY)
- Price tier breakdown: entry-level, mid-range, luxury
- Condo/townhouse median price (if applicable)
- Foreclosure and distressed sale percentage
Search 2 — Inventory & Supply
Query: "<CITY> <STATE> housing inventory months of supply active listings 2026"
Gather:
- Active listings count (current)
- Active listings (same month prior year) and YoY change
- Months of supply (active listings / monthly sales rate)
- New listings count (monthly)
- Pending sales count
- Absorption rate (homes sold per month)
- Inventory trend: increasing, decreasing, or stable
- Shadow inventory (pre-foreclosure, bank-owned not yet listed)
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 · 366 lines · 37 tokens per session scan A c7b9e55e1f26
realestate-market is a skill published in the GitHub repository zubair-trabzada/ai-realestate-claude (160 stars, last pushed 4mo ago), licensed MIT. It adds 37 tokens to every session and 3,236 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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