Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
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...
skill openclawclawhub Free
Generate comparative market analysis (CMA) and home valuation reports from IDX listing data and selected comparable properties. Use when a user wants to pick...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, agents/openai.yaml, references/cma-input-schema.md, references/gemini-canvas-publish.md, references/valuation-guidelines.md, scripts/build_cma.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- danielfoch
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
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
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs1 Scripts1 Config
- SKILL.md
- references/cma-input-schema.md
- references/gemini-canvas-publish.md
- references/valuation-guidelines.md
- scripts/build_cma.py
- agents/openai.yaml