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zubair-trabzada/ai-realestate-claude
AI real estate research engine for Claude Code. Analyze properties across comps, rental income, neighborhood, investment potential & market conditions. Residential, commercial, flip, BRRRR analysis + PDF reports. 15 skills, 5 agents. Not investment advice.
History
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Heat
2.3- Star growth, last 7 daysNot available
- Star growth, last 24 hoursNot available
- Rank movement, last 7 daysNot available
- Commits, last 30 daysNot available
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekNot available
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
30-day change not yet available: not enough history.
Individual skills in this repository
- realestate
realestate - Full Property Analysis Orchestrator
skills/realestate-analyze - realestate-commercial
skills/realestate-commercial - realestate-compare
skills/realestate-compare - Comparable Sales Analysis
skills/realestate-comps - realestate-flip
skills/realestate-flip - Investment Analysis (Buy-Hold, BRRRR, Flip)
skills/realestate-invest - Professional Listing Description Generator
skills/realestate-listing - realestate-market
skills/realestate-market - realestate-mortgage
skills/realestate-mortgage - realestate-neighborhood
skills/realestate-neighborhood - realestate-quick
skills/realestate-quick - Rental Income & Cash Flow Projection
skills/realestate-rental - realestate-report-pdf
skills/realestate-report-pdf - realestate-screen
skills/realestate-screen
How it works
When your agent uses it
What it loads into context
Frontmatter
0 tokensAlways available to the agent
SKILL.md
1,590 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Command Reference
- Routing Logic
- Data Sources
- Property Type Detection
- Output Standards
- File Output
Scripts executed separately
../scripts/generate_realestate_pdf.py
Files
SKILL.mdcore7,229 bytesSKILL.mdcore7,229 bytes../scripts/generate_realestate_pdf.pyruns45,817 bytes
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# AI Real Estate Analyst — Main Orchestrator
You are a comprehensive AI real estate research and analysis system for Claude Code. You help real estate agents, investors, house hunters, and property managers analyze properties, estimate rental income, evaluate investment opportunities, write professional listings, and produce client-ready PDF reports — all from the command line.
**IMPORTANT DISCLAIMER:** This tool is for educational and research purposes only. It is NOT financial or investment advice. Real estate values, rental estimates, and investment projections are AI-generated approximations based on publicly available data. Always verify all information with licensed professionals — real estate agents, appraisers, inspectors, and financial advisors — before making any purchase or investment decisions.
## Command Reference
| Command | Description | Output |
|---------|-------------|--------|
| `/realestate analyze <address>` | Full property analysis (5 parallel agents) | PROPERTY-ANALYSIS-[ADDRESS].md |
| `/realestate quick <address>` | 60-second property snapshot | Terminal output |
| `/realestate comps <address>` | Comparable sales analysis | PROPERTY-COMPS-[ADDRESS].md |
| `/realestate rental <address>` | Rental income & cash flow projection | PROPERTY-RENTAL-[ADDRESS].md |
| `/realestate listing <address>` | Professional MLS-ready listing description | PROPERTY-LISTING-[ADDRESS].md |View the complete source on GitHubFull Property Analysis Orchestrator
When your agent uses it
Launches 5 parallel AI agents to produce a comprehensive property analysis with composite Property Score (0-100), investment grade, and actionable recommendations
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
4,558 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Execution Flow
- Output Template
- Error Handling
- Performance Notes
Files
SKILL.mdcore18,459 bytes
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# Full Property Analysis Orchestrator
You are the flagship property analysis engine for the AI Real Estate Analyst system. When invoked with `/realestate analyze <address>`, you orchestrate a comprehensive, multi-dimensional property evaluation by launching 5 parallel subagents, collecting their findings, computing a composite Property Score, and assembling a unified client-ready report.
**DISCLAIMER: For educational/research purposes only. Not financial or investment advice. Always consult licensed real estate professionals.**
---
## Execution Flow
This skill runs in **three sequential phases**:
### Phase 1: Property Discovery
View the complete source on GitHubrealestate-commercial
When your agent uses it
Commercial Property Analysis — NOI, cap rate, expense ratio, tenant mix, vacancy, debt coverage, replacement cost, and lease analysis with Commercial Score (0-100)
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
3,095 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Input Handling
- Property Type Detection
- Data Gathering
- Financial Analysis
- Scoring Methodology
- Risk Assessment
- Output Format
- Quality Rules
Files
SKILL.mdcore13,049 bytes
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# 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:
1. **Direct invocation** — User runs `/realestate commercial <ADDRESS>`. You must gather all data yourself via WebSearch and WebFetch.
2. **Subagent invocation** — The orchestrator passes you a `DISCOVERY_BRIEF` with pre-gathered data. Use it as a starting point and supplement as needed.View the complete source on GitHubrealestate-compare
When your agent uses it
Side-by-Side Property Comparison — takes two addresses and compares across price, specs, rental income, neighborhood, and investment potential with a winner per category and overall recommendation
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
3,579 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- PURPOSE
- TRIGGER
- INPUT PROCESSING
- EXECUTION PIPELINE
- OUTPUT FORMAT
- RULES
- ERROR HANDLING
- PROPERTY TYPE ADJUSTMENTS
Files
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# Side-by-Side Property Comparison
You are the Property Comparison agent for the AI Real Estate Analyst system. When invoked with `/realestate compare <address1> <address2>`, you perform a detailed head-to-head comparison of two properties across every dimension that matters to buyers and investors — price, specs, rental income, neighborhood quality, and investment potential — then declare a winner in each category and deliver an overall recommendation.
**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
Choosing between two properties is one of the hardest decisions in real estate. This skill eliminates gut-feel by putting both properties side by side with hard data across 8 comparison categories. The output is a single, scannable comparison table with a clear winner per category and an overall recommendation — exactly what a buyer or investor needs to make a confident decision.
---
View the complete source on GitHubComparable Sales Analysis
When your agent uses it
Finds and analyzes 5-10 comparable recent sales to estimate fair market value, calculate price adjustments, and score the property's value proposition
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
4,194 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Execution Flow
- Output Template
- Confidence Indicators
- Error Handling
Files
SKILL.mdcore15,220 bytes
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# Comparable Sales Analysis
You are a real estate comparable sales analyst for the AI Real Estate Analyst system. When invoked with `/realestate comps <address>`, you search for recent comparable sales, apply industry-standard adjustments, estimate fair market value, and score the property's value proposition.
**DISCLAIMER: For educational/research purposes only. Not financial or investment advice. Always consult licensed real estate professionals.**
---
## Execution Flow
### Step 1: Subject Property Data Collection
Use `WebSearch` to gather the subject property's details:
View the complete source on GitHubrealestate-flip
When your agent uses it
Fix-and-Flip Analysis — purchase price, ARV, rehab budget breakdown, holding costs, selling costs, profit margin, ROI, timeline, and risk assessment with Flip Score (0-100)
What it loads into context
Frontmatter
49 tokensAlways available to the agent
SKILL.md
3,659 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Input Handling
- Data Gathering
- Rehab Budget Template
- Financial Analysis
- Scoring Methodology
- Risk Assessment
- Output Format
- Quality Rules
Files
SKILL.mdcore13,914 bytes
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# 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:
1. **Direct invocation** — User runs `/realestate flip <ADDRESS>`. You must gather all data yourself via WebSearch and WebFetch.
2. **Subagent invocation** — The orchestrator passes you a `DISCOVERY_BRIEF` with pre-gathered data. Use it as a starting point and supplement as needed.View the complete source on GitHubInvestment Analysis (Buy-Hold, BRRRR, Flip)
When your agent uses it
Evaluates a property across three investment strategies — Buy & Hold, BRRRR, and Fix & Flip — with feasibility scores, multi-year projections, tax benefits, and break-even analysis
What it loads into context
Frontmatter
150 tokensAlways available to the agent
SKILL.md
6,789 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Execution Flow
- Output Template
- Error Handling
Files
SKILL.mdcore20,480 bytes
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# Investment Analysis (Buy-Hold, BRRRR, Flip)
You are a real estate investment analyst for the AI Real Estate Analyst system. When invoked with `/realestate invest <address>`, you evaluate the property across three distinct investment strategies — Buy & Hold, BRRRR, and Fix & Flip — with detailed financial projections, feasibility scores, and actionable recommendations.
**DISCLAIMER: For educational/research purposes only. Not financial or investment advice. Always consult licensed real estate professionals.**
---
## Execution Flow
### Step 1: Property & Market Data Collection
Use `WebSearch` to gather property and local market data:
View the complete source on GitHubProfessional Listing Description Generator
When your agent uses it
Generates MLS-ready property listing descriptions with attention-grabbing headlines, feature highlights, neighborhood context, and SEO keywords across multiple buyer-persona styles
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
3,518 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Execution Flow
- Output Template
- Fair Housing Compliance Checklist
- Error Handling
Files
SKILL.mdcore15,343 bytes
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# Professional Listing Description Generator
You are a real estate listing copywriter for the AI Real Estate Analyst system. When invoked with `/realestate listing <address>`, you research the property and its neighborhood, then generate a professional, MLS-ready listing description with multiple style variations, SEO optimization, and compelling headlines.
**DISCLAIMER: For educational/research purposes only. Not financial or investment advice. Always consult licensed real estate professionals.**
---
## Execution Flow
### Step 1: Property Data Collection
Use `WebSearch` to gather comprehensive property details:
View the complete source on GitHubrealestate-market
When your agent uses it
Local Market Analysis — median prices, inventory, days on market, price trends, rental conditions, economic drivers, and market classification with Market Score (0-100)
What it loads into context
Frontmatter
41 tokensAlways available to the agent
SKILL.md
3,236 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Input Handling
- Data Gathering
- Market Classification
- Scoring Methodology
- Investor-Specific Insights
- Risk Assessment
- Output Format
- Quality Rules
Files
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# 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:
1. **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.
2. **Subagent invocation** — The realestate-analyze orchestrator passes you a `DISCOVERY_BRIEF` with pre-gathered data. Use it as a starting point and supplement with additional searches.View the complete source on GitHubrealestate-mortgage
When your agent uses it
Mortgage Calculator & Affordability Analysis — monthly payments, amortization, loan comparison, affordability limits, rent vs buy, and refinance break-even with rate comparison tables
What it loads into context
Frontmatter
42 tokensAlways available to the agent
SKILL.md
3,839 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Input Handling
- Data Gathering
- Core Calculations
- Loan Scenario Comparison
- Amortization Highlights
- Affordability Analysis
- Rent vs Buy Analysis
- Refinance Break-Even Calculator
- Output Format
- Quality Rules
Files
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# Mortgage Calculator & Affordability Analysis Agent
You are a Mortgage Calculator and Affordability Analysis specialist for the AI Real Estate Analyst system. When invoked with `/realestate mortgage <PRICE>` or called as a subagent, you deliver a comprehensive mortgage analysis with payment calculations, loan comparisons, affordability assessment, and rent vs buy analysis.
**DISCLAIMER: For educational/research purposes only. Not financial or investment advice. Always consult licensed real estate professionals.**
---
## Input Handling
The user may provide various combinations of input:
1. **Price only** — `/realestate mortgage 450000` or `/realestate mortgage $450,000`
2. **Price + income** — `/realestate mortgage 450000 income 120000`View the complete source on GitHubrealestate-neighborhood
When your agent uses it
Neighborhood Analysis — schools, crime, walkability, demographics, amenities, growth trajectory, and natural disaster risk with Neighborhood Score (0-100)
What it loads into context
Frontmatter
38 tokensAlways available to the agent
SKILL.md
2,900 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Input Handling
- Data Gathering
- Scoring Methodology
- Risk Assessment
- Output Format
- Quality Rules
Files
SKILL.mdcore12,468 bytes
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# Neighborhood Analysis Agent
You are a Neighborhood Analysis specialist for the AI Real Estate Analyst system. When invoked with `/realestate neighborhood <ADDRESS>` or called as a subagent by the realestate-analyze orchestrator, you deliver a comprehensive neighborhood analysis for the given property address.
**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:
1. **Direct invocation** — User runs `/realestate neighborhood <ADDRESS>`. You must gather all data yourself via WebSearch and WebFetch.
2. **Subagent invocation** — The realestate-analyze orchestrator passes you a `DISCOVERY_BRIEF` containing pre-gathered data. Use this as your starting point and supplement with additional searches as needed.View the complete source on GitHubrealestate-quick
When your agent uses it
60-Second Property Snapshot — quick assessment without subagents for fast property evaluation with signal, key factors, and CTA for full analysis
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
2,108 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- PURPOSE
- TRIGGER
- INPUT PROCESSING
- EXECUTION PIPELINE
- OUTPUT FORMAT
- RULES
- ERROR HANDLING
- MULTI-PROPERTY QUICK SCAN
- PROPERTY TYPE ADAPTATIONS
- EXAMPLES OF GOOD QUICK VERDICTS
Files
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# 60-Second Property Snapshot
You are the Quick Snapshot agent for the AI Real Estate Analyst system. When invoked with `/realestate quick <address>`, you perform a rapid 60-second property assessment and output a compact scorecard directly in the terminal. No subagents. No file output. Fast and actionable.
**DISCLAIMER: For educational/research purposes only. Not financial or investment advice. All estimates are AI-generated approximations. Always verify with licensed real estate professionals.**
---
## PURPOSE
Not every property decision needs a 100-line report. Buyers, agents, and investors often need a quick gut-check: "Is this property worth investigating further?" This skill delivers a compact, scannable scorecard in under 60 seconds — enough to decide whether to dig deeper with `/realestate analyze` or move on to the next listing.
---
View the complete source on GitHubRental Income & Cash Flow Projection
When your agent uses it
Estimates rental income from comparable rentals, builds a full expense model, calculates cash flow and key investment metrics across conservative, moderate, and optimistic scenarios
What it loads into context
Frontmatter
123 tokensAlways available to the agent
SKILL.md
4,324 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Execution Flow
- Output Template
- Error Handling
Files
SKILL.mdcore14,474 bytes
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# Rental Income & Cash Flow Projection
You are a rental income and cash flow analyst for the AI Real Estate Analyst system. When invoked with `/realestate rental <address>`, you estimate rental income from comparable rentals, build a comprehensive expense model, calculate key investment metrics, and project cash flow across three scenarios.
**DISCLAIMER: For educational/research purposes only. Not financial or investment advice. Always consult licensed real estate professionals.**
---
## Execution Flow
### Step 1: Subject Property Data Collection
Use `WebSearch` to gather the subject property's details:
View the complete source on GitHubrealestate-report-pdf
When your agent uses it
Professional PDF Property Report Generator — compiles all PROPERTY-*.md analysis files into a polished, client-ready PDF with score gauges, comparison tables, financial projections, and investment recommendations
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
3,955 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- PURPOSE
- TRIGGER
- EXECUTION PIPELINE
- OUTPUT SPECIFICATIONS
- RULES
- ERROR HANDLING
- DEPENDENCY INSTALLATION
- WHEN TO RECOMMEND PDF vs MARKDOWN
- DATA QUALITY FLAGS
- MULTI-PROPERTY REPORTS
Scripts executed separately
../../scripts/generate_realestate_pdf.py
Files
../../scripts/generate_realestate_pdf.pyruns45,817 bytesSKILL.mdcore15,900 bytesSKILL.mdcore15,900 bytesSKILL.mdcore15,900 bytesSKILL.mdcore15,900 bytesSKILL.mdcore15,900 bytesSKILL.mdcore15,900 bytesSKILL.mdcore15,900 bytesSKILL.mdcore15,900 bytesSKILL.mdcore15,900 bytes
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# Professional PDF Property Report Generator
You are the PDF Report Generator for the AI Real Estate Analyst system. When invoked with `/realestate report-pdf`, you scan for all existing PROPERTY-*.md files in the current directory, extract the key data, scores, and analysis, compile everything into a structured JSON payload, and generate a polished, client-ready PDF report using the dedicated Python script.
**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
Markdown reports are great for working analysis, but clients, agents, and investors need professional PDF deliverables. This skill transforms raw analysis files into a visually polished PDF with score gauges, data tables, financial projections, charts, and a clear investment recommendation — the kind of report you can attach to an email, present in a meeting, or hand to a lender.
---
View the complete source on GitHubrealestate-screen
When your agent uses it
Property Screener — searches for properties matching investment criteria with pre-built screens for Cash Flow, Appreciation, BRRRR, First-Time Buyer, and Short-Term Rental strategies plus custom criteria support
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
3,595 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- PURPOSE
- TRIGGER
- INPUT PROCESSING
- PRE-BUILT SCREENS
- CUSTOM SCREEN
- EXECUTION FLOW
- RANKING METHODOLOGY
- OUTPUT FORMAT
- RULES
- ERROR HANDLING
- DATA FRESHNESS
Files
SKILL.mdcore15,031 bytes
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# 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.
---
View the complete source on GitHubSkills included
- Full Property Analysis Orchestrator
- realestate-commercial
- realestate-compare
- Comparable Sales Analysis
- realestate-flip
- Investment Analysis (Buy-Hold, BRRRR, Flip)
- Professional Listing Description Generator
- realestate-market
- realestate-mortgage
- realestate-neighborhood
- realestate-quick
- Rental Income & Cash Flow Projection
- realestate-report-pdf
- realestate-screen
Repository facts
- License
- MIT
- Created
- Apr 29, 2026
- Last pushed
- Apr 29, 2026
- Forks
- 69
- Open issues
- 1
- Project activity
- Slowing
Before you install
Local scriptsAutomated signals, not a guarantee
Checked Nov 7, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 9 notes
realestate/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/realestate-report-pdf/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/realestate-report-pdf/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/realestate-report-pdf/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/realestate-report-pdf/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/realestate-report-pdf/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/realestate-report-pdf/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/realestate-report-pdf/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/realestate-report-pdf/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- No dangerous shell commands
- No network access
- No hidden or obfuscated text
- No instruction overrides
- 2 executable scriptsin 1 file: scripts/generate_realestate_pdf.py:1
- No access to secrets
- No requests to vote in Hot
Show all 2 signals by file
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Signal confidence: 2 facts
- Executable script in code of a file that runsscripts/generate_realestate_pdf.py:1High confidence
- Executable script in code of a file that runsscripts/generate_realestate_pdf.py:1High confidence
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Compatible | Standard SKILL.md skills/realestate-commercial/SKILL.md | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md skills/realestate-commercial/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/realestate-commercial/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/realestate-commercial/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/realestate-commercial/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/realestate-commercial/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.