How do I install the Idx Cma Report skill?

Run npx skillmds@latest add modbender/idx-cma-report in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Idx Cma Report safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Idx Cma Report?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Idx Cma Report free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Idx Cma Report?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Idx Cma Report by modbender — Agent Skill

Name: Idx Cma Report
Rating: 4.2 (5 reviews)
Author: modbender

Idx Cma Report

Generate comparative market analysis (CMA) and home valuation reports from IDX listing data and selected comparable properties. Use when a user wants to pick comps, estimate a market value range, produce seller-facing home evaluation reports, or publish an interactive CMA experience via Google Gemini Canvas or Google AI Studio.

modbender Updated Sep 9, 2026 12 repo stars

File contents

IDX CMA Report

Use this skill to turn subject-property data and IDX comparables into a defensible CMA package with:

Structured valuation calculations
A written report for agent/client review
An interactive handoff prompt for Google Gemini Canvas / Google AI Studio

Workflow

1. Gather Data Through IDX MCP/CLI

Use the IDX MCP/CLI skill already available in the environment to pull:

Subject property details
Candidate comparable listings (closed/pending/active based on user preference)

Ask the user which comps to include when the choice is ambiguous. Keep 3 to 8 comps unless the user requests otherwise.

Normalize data to JSON using the schema in references/cma-input-schema.md.

2. Build CMA Outputs

Run:

python3 scripts/build_cma.py \
  --subject subject.json \
  --comps comps.json \
  --output-dir cma-output

The script produces:

cma-output/cma_report.md (summary report)
cma-output/cma_data.json (calculation payload)
cma-output/interactive_local.html (local interactive view)
cma-output/gemini_canvas_prompt.md (prompt for Google tools)

3. Review and Explain Adjustments

Before final delivery:

Show the comp set used
Show estimated range and central estimate
Explain assumptions and major adjustments in plain language
Flag missing/low-quality fields that weaken confidence

Use references/valuation-guidelines.md for adjustment defaults and confidence guidance.

4. Publish Interactive Version in Gemini

Use cma-output/gemini_canvas_prompt.md as the base prompt. Then:

Open Google AI Studio or Gemini Canvas.
Paste the generated prompt and provide cma_data.json.
Ask for an interactive CMA web app with:
- Comp table with sorting/filtering
- Map-ready data fields (if lat/lng present)
- Value-range visualization
- Notes panel explaining adjustments
Request hosted/shareable output if available in the chosen Google tool.

See references/gemini-canvas-publish.md for a copy-ready checklist.

Safety Rules

Treat outputs as broker/agent CMA support, not a licensed appraisal.
Surface data gaps, outliers, or stale comps before presenting a valuation.
Never invent listing attributes; mark missing values as unknown.
Keep a clear boundary between factual listing data and model assumptions.

References

references/cma-input-schema.md
references/valuation-guidelines.md
references/gemini-canvas-publish.md

---name: idx-cma-reportdescription: Generate comparative market analysis (CMA) and home valuation reports from IDX listing data and selected comparable properties. Use when a user wants to pick comps, estimate a market value range, produce seller-facing home evaluation reports, or publish an interactive CMA experience via Google Gemini Canvas or Google AI Studio.--- # IDX CMA Report Use this skill to turn subject-property data and IDX comparables into a defensible CMA package with: - Structured valuation calculations - A written report for agent/client review - An interactive handoff prompt for Google Gemini Canvas / Google AI Studio ## Workflow ### 1. Gather Data Through IDX MCP/CLI Use the IDX MCP/CLI skill already available in the environment to pull: - Subject property details - Candidate comparable listings (closed/pending/active based on user preference) Ask the user which comps to include when the choice is ambiguous. Keep 3 to 8 comps unless the user requests otherwise. Normalize data to JSON using the schema in `references/cma-input-schema.md`. ### 2. Build CMA Outputs Run: ```bash python3 scripts/build_cma.py \ --subject subject.json \ --comps comps.json \ --output-dir cma-output ``` The script produces: - `cma-output/cma_report.md` (summary report) - `cma-output/cma_data.json` (calculation payload) - `cma-output/interactive_local.html` (local interactive view) - `cma-output/gemini_canvas_prompt.md` (prompt for Google tools) ### 3. Review and Explain Adjustments Before final delivery: - Show the comp set used - Show estimated range and central estimate - Explain assumptions and major adjustments in plain language - Flag missing/low-quality fields that weaken confidence Use `references/valuation-guidelines.md` for adjustment defaults and confidence guidance. ### 4. Publish Interactive Version in Gemini Use `cma-output/gemini_canvas_prompt.md` as the base prompt. Then: 1. Open [Google AI Studio](https://aistudio.google.com/) or Gemini Canvas. 2. Paste the generated prompt and provide `cma_data.json`. 3. Ask for an interactive CMA web app with: - Comp table with sorting/filtering - Map-ready data fields (if lat/lng present) - Value-range visualization - Notes panel explaining adjustments 4. Request hosted/shareable output if available in the chosen Google tool. See `references/gemini-canvas-publish.md` for a copy-ready checklist. ## Safety Rules - Treat outputs as broker/agent CMA support, not a licensed appraisal. - Surface data gaps, outliers, or stale comps before presenting a valuation. - Never invent listing attributes; mark missing values as unknown. - Keep a clear boundary between factual listing data and model assumptions. ## References - `references/cma-input-schema.md` - `references/valuation-guidelines.md` - `references/gemini-canvas-publish.md`

Frequently asked questions