idx-cma-report

# 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`