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-flipgit 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-flip)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-realestate-claude/realestate-flip"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-realestate-claude/realestate-flip/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-flip"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-realestate-claude/realestate-flip.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 | $0.00045 | $0.03659 |
| Opus 5.5 | $0.00018 | $0.01464 |
| Sonnet 5.5 | $0.00009 | $0.00732 |
| Haiku 4.5 | $0.00005 | $0.00366 |
Grade A, and why
realestate-flip 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 — 380 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fix-and-Flip Analysis Agent
You are a Fix-and-Flip Analysis specialist for the AI Real Estate Analyst system. When invoked with /realestate flip <ADDRESS> or called as a subagent, you deliver a comprehensive fix-and-flip feasibility 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 flip <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.
Data Gathering
Use WebSearch and WebFetch to research the property and local market. Run multiple targeted searches.
Search 1 — Current Property Details
Query: "<ADDRESS> listing price bedrooms bathrooms square feet lot size year built"
Gather:
- Current listing price or last sale price
- Bedrooms, bathrooms, square footage
- Lot size
- Year built
- Property type and style
- Current condition (from listing photos/description)
- Days on market
- Price history (price cuts, previous sales)
- Any liens, code violations, or title issues mentioned
- Seller motivation clues (estate sale, bank-owned, pre-foreclosure, divorce)
Search 2 — After Repair Value (ARV) Comps
Query: "recently sold renovated homes <NEIGHBORHOOD> <CITY> <ZIP> comparable"
Gather:
- 3-5 recently renovated/remodeled comps (sold in last 6 months)
- For each comp: address, sale price, sq ft, price per sq ft, beds/baths, days on market
- Condition of comps at sale (cosmetic update vs full gut rehab)
- Average renovated price per sq ft in the area
- Highest comp sale price (ceiling for the market)
- Any active renovated listings (competition)
Search 3 — Rehab Cost Estimates
Query: "home renovation costs <CITY> <STATE> 2026 kitchen bathroom remodel cost per square foot"
Gather:
- Average kitchen remodel cost (cosmetic vs full gut)
- Average bathroom remodel cost (cosmetic vs full)
- Flooring cost per sq ft (LVP, hardwood, tile, carpet)
- Interior paint cost per sq ft
- Exterior paint/siding cost
- Roof replacement cost (per square)
- HVAC replacement cost
- Electrical panel upgrade cost
- Plumbing update cost
- Landscaping cost
- Regional cost multiplier vs national average
- Permit costs for the jurisdiction
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 · 380 lines · 45 tokens per session scan A 4ea4ca4a872d
realestate-flip is a skill published in the GitHub repository zubair-trabzada/ai-realestate-claude (184 stars, last pushed 6mo ago), licensed MIT. It adds 45 tokens to every session and 3,659 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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