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    "slug": "idx-cma-report",
    "name": "IDX CMA Report",
    "source": "tencent",
    "type": "skill",
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        "Paste one of the prompts below and point your agent at the extracted folder."
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        },
        {
          "label": "Upgrade existing",
          "body": "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."
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        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
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  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "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."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "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."
      },
      {
        "label": "Upgrade existing",
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      }
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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "IDX CMA Report",
        "body": "Use this skill to turn subject-property data and IDX comparables into a defensible CMA package with:\n\nStructured valuation calculations\nA written report for agent/client review\nAn interactive handoff prompt for Google Gemini Canvas / Google AI Studio"
      },
      {
        "title": "1. Gather Data Through IDX MCP/CLI",
        "body": "Use the IDX MCP/CLI skill already available in the environment to pull:\n\nSubject property details\nCandidate comparable listings (closed/pending/active based on user preference)\n\nAsk the user which comps to include when the choice is ambiguous. Keep 3 to 8 comps unless the user requests otherwise.\n\nNormalize data to JSON using the schema in references/cma-input-schema.md."
      },
      {
        "title": "2. Build CMA Outputs",
        "body": "Run:\n\npython3 scripts/build_cma.py \\\n  --subject subject.json \\\n  --comps comps.json \\\n  --output-dir cma-output\n\nThe script produces:\n\ncma-output/cma_report.md (summary report)\ncma-output/cma_data.json (calculation payload)\ncma-output/interactive_local.html (local interactive view)\ncma-output/gemini_canvas_prompt.md (prompt for Google tools)"
      },
      {
        "title": "3. Review and Explain Adjustments",
        "body": "Before final delivery:\n\nShow the comp set used\nShow estimated range and central estimate\nExplain assumptions and major adjustments in plain language\nFlag missing/low-quality fields that weaken confidence\n\nUse references/valuation-guidelines.md for adjustment defaults and confidence guidance."
      },
      {
        "title": "4. Publish Interactive Version in Gemini",
        "body": "Use cma-output/gemini_canvas_prompt.md as the base prompt. Then:\n\nOpen Google AI Studio or Gemini Canvas.\nPaste the generated prompt and provide cma_data.json.\nAsk for an interactive CMA web app with:\n\nComp table with sorting/filtering\nMap-ready data fields (if lat/lng present)\nValue-range visualization\nNotes panel explaining adjustments\n\n\nRequest hosted/shareable output if available in the chosen Google tool.\n\nSee references/gemini-canvas-publish.md for a copy-ready checklist."
      },
      {
        "title": "Safety Rules",
        "body": "Treat outputs as broker/agent CMA support, not a licensed appraisal.\nSurface data gaps, outliers, or stale comps before presenting a valuation.\nNever invent listing attributes; mark missing values as unknown.\nKeep a clear boundary between factual listing data and model assumptions."
      },
      {
        "title": "References",
        "body": "references/cma-input-schema.md\nreferences/valuation-guidelines.md\nreferences/gemini-canvas-publish.md"
      }
    ],
    "body": "IDX CMA Report\n\nUse this skill to turn subject-property data and IDX comparables into a defensible CMA package with:\n\nStructured valuation calculations\nA written report for agent/client review\nAn interactive handoff prompt for Google Gemini Canvas / Google AI Studio\nWorkflow\n1. Gather Data Through IDX MCP/CLI\n\nUse the IDX MCP/CLI skill already available in the environment to pull:\n\nSubject property details\nCandidate comparable listings (closed/pending/active based on user preference)\n\nAsk the user which comps to include when the choice is ambiguous. Keep 3 to 8 comps unless the user requests otherwise.\n\nNormalize data to JSON using the schema in references/cma-input-schema.md.\n\n2. Build CMA Outputs\n\nRun:\n\npython3 scripts/build_cma.py \\\n  --subject subject.json \\\n  --comps comps.json \\\n  --output-dir cma-output\n\n\nThe script produces:\n\ncma-output/cma_report.md (summary report)\ncma-output/cma_data.json (calculation payload)\ncma-output/interactive_local.html (local interactive view)\ncma-output/gemini_canvas_prompt.md (prompt for Google tools)\n3. Review and Explain Adjustments\n\nBefore final delivery:\n\nShow the comp set used\nShow estimated range and central estimate\nExplain assumptions and major adjustments in plain language\nFlag missing/low-quality fields that weaken confidence\n\nUse references/valuation-guidelines.md for adjustment defaults and confidence guidance.\n\n4. Publish Interactive Version in Gemini\n\nUse cma-output/gemini_canvas_prompt.md as the base prompt. Then:\n\nOpen Google AI Studio or Gemini Canvas.\nPaste the generated prompt and provide cma_data.json.\nAsk for an interactive CMA web app with:\nComp table with sorting/filtering\nMap-ready data fields (if lat/lng present)\nValue-range visualization\nNotes panel explaining adjustments\nRequest hosted/shareable output if available in the chosen Google tool.\n\nSee references/gemini-canvas-publish.md for a copy-ready checklist.\n\nSafety Rules\nTreat outputs as broker/agent CMA support, not a licensed appraisal.\nSurface data gaps, outliers, or stale comps before presenting a valuation.\nNever invent listing attributes; mark missing values as unknown.\nKeep a clear boundary between factual listing data and model assumptions.\nReferences\nreferences/cma-input-schema.md\nreferences/valuation-guidelines.md\nreferences/gemini-canvas-publish.md"
  },
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    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/danielfoch/idx-cma-report",
    "publisherUrl": "https://clawhub.ai/danielfoch/idx-cma-report",
    "owner": "danielfoch",
    "version": "0.1.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
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