real-estate-investment

real-estate-investment is a skill for Claude Code from Aznatkoiny/zAI-Skills. It costs 197 tokens per session (2,458 once invoked), scanned A, original, MIT.

A complete workflow for analyzing real estate investments, from collecting property data to building cash-flow forecasts and reporting results.

In plain words
What is it for?
It helps evaluate deals and markets using metrics such as net operating income, cap rate, cash-on-cash return, debt-service coverage, IRR, and equity multiple.
Why use it?
It turns common investment measures into a repeatable analysis process and includes checks for changing assumptions.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: positional $N argument.

Part of the real-estate-investment plugin — 1 skill, 9 commands, 4 agents shipped together

Good fit It helps evaluate deals and markets using metrics such as net operating income, cap rate, cash-on-cash return, debt-service coverage, IRR, and equity multiple.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aznatkoiny/zai-skills/real-estate-investment
Install

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.

Any agent
npx skills add Aznatkoiny/zAI-Skills --skill real-estate-investment
Clone the repo
git clone --depth 1 https://github.com/Aznatkoiny/zAI-Skills

Made for: Claude Code.

Or install real-estate-investment, the plugin that ships this one along with the rest of its 1 skill, 9 commands, 4 agents.

Wrote 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.

agentmods badge for real-estate-investment

README.md
[![agentmods](https://agentmods.dev/badge/skills/aznatkoiny/zai-skills/real-estate-investment/github.svg)](https://agentmods.dev/skills/aznatkoiny/zai-skills/real-estate-investment)
Your own site
<a href="https://agentmods.dev/skills/aznatkoiny/zai-skills/real-estate-investment"><img src="https://agentmods.dev/badge/skills/aznatkoiny/zai-skills/real-estate-investment/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.

agentmods 80×15 button for real-estate-investment

Your own site · 80×15
<a href="https://agentmods.dev/skills/aznatkoiny/zai-skills/real-estate-investment"><img src="https://agentmods.dev/badge/skills/aznatkoiny/zai-skills/real-estate-investment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 197 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,458 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00197 $0.02458
Opus 5 $0.00098 $0.01229
Sonnet 5 $0.00039 $0.00492
Haiku 4.5 $0.00020 $0.00246

Measured 9d ago against content hash 99f4296ef2e5, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

real-estate-investment 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 9d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/monte_carlo.py, scripts/pro_forma.py, scripts/sensitivity.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

real-estate-investment/skills/real-estate-investment/SKILL.md · 159 lines

How it starts

The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Real Estate Investment Analysis

Comprehensive real estate investment analysis — from deal screening through financial modeling to investor-ready output. Covers all property types, standard and advanced metrics, code generation, and tax-aware structuring.

Analysis Workflow

Follow this 6-step process for any deal or market analysis:

  1. Define scope — Identify property type, investment strategy, and target output format
  2. Gather data — Collect property financials, market data, and comps (use API reference if automating)
  3. Build pro forma — Construct income statement: Gross Rent → Vacancy → EGI → OpEx → NOI → Debt Service → Cash Flow
  4. Calculate returns — Apply appropriate metrics (see Quick Reference below)
  5. Stress test — Run sensitivity analysis, scenarios, or Monte Carlo simulation
  6. Report — Generate investor-ready output with recommendations

Quick Reference — Core Metrics

Metric Formula Typical Range
NOI Effective Gross Income - Operating Expenses Varies by asset
Cap Rate NOI / Property Value 4-10% (market-dependent)
Cash-on-Cash Annual Pre-Tax Cash Flow / Total Cash Invested 8-12% target
DSCR NOI / Annual Debt Service 1.2x+ (lender minimum)
IRR Discount rate zeroing NPV of all cash flows 15-20% target
Equity Multiple Total Distributions / Total Capital Invested 2.0x+ over hold
GRM Property Price / Annual Gross Rent 8-15 (lower = better)
Break-even Occ. (OpEx + Debt Service) / Potential Gross Income <85% preferred

For complete formulas, Python code, and Excel equivalents → load references/financial-metrics.md

Property Type Router

Select the analysis framework based on property type:

Property Type Key Metrics Rules of Thumb Reference
SFR / Small Multi (1-4) CoC, Cap Rate, DSCR 1% rule, 50% rule, 70% rule references/property-types.md §Residential
BRRRR ARV, Rehab ROI, Refi LTV 70% rule: Max buy = 70% ARV - repairs references/property-types.md §Residential
House Hack Effective housing cost, FHA terms 3.5% down FHA, self-sufficiency test references/property-types.md §Residential
Large Multifamily (5+) Per-unit metrics, NOI, Cap Rate OpEx ratio 35-45% references/property-types.md §Commercial
Commercial (Office/Retail) Per-SF metrics, lease analysis NNN vs Gross lease impact references/property-types.md §Commercial
Short-Term Rental RevPAR, ADR, Occupancy Revenue = ADR x Occ x 365 - fees references/property-types.md §STR
Land / Development Absorption rate, dev pro forma Total cost vs projected value references/property-types.md §Land

Read the full file on GitHub · 159 lines

Changes

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.

  1. 9d ago First seen · 159 lines · 197 tokens per session scan A 99f4296ef2e5

Subscribe to this mod's changes

real-estate-investment is a skill published in the GitHub repository Aznatkoiny/zAI-Skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 197 tokens to every session and 2,458 once invoked, about $0.0010 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 2026-09-03.

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