{
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  "item": {
    "slug": "adi-decision-engine",
    "name": "ADI Decision Engine",
    "source": "tencent",
    "type": "skill",
    "category": "AI 智能",
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    "prerequisites": [
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      "assets/icon-large.svg",
      "assets/icon-small.svg",
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      "examples/research_methods.json"
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      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
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      "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": [
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          "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",
          "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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      "detail": "Yavira can redirect you to the upstream package for this item.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/adi-decision-engine"
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        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
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      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
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    "agentPageUrl": "https://openagent3.xyz/skills/adi-decision-engine/agent",
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    "briefUrl": "https://openagent3.xyz/skills/adi-decision-engine/agent.md"
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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",
        "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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  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Core promise",
        "body": "Turn a messy tradeoff problem into a structured, auditable multi-criteria decision and return a ranked recommendation with confidence and explanation."
      },
      {
        "title": "When to use this skill",
        "body": "Use this skill when the user needs structured decision support rather than open-ended brainstorming. Typical triggers include:\n\nmulti-criteria decision analysis\nweighted scoring or option ranking\nvendor selection or procurement\nroute planning with explicit tradeoffs\nhiring shortlist ranking\ntool or platform comparison\npolicy-driven or auditable agent decisions"
      },
      {
        "title": "Input modes",
        "body": "This skill supports exactly two input modes."
      },
      {
        "title": "1. Structured mode",
        "body": "The user already has a decision request with:\n\noptions\ncriteria\noptional constraints\noptional policy_name\noptional evidence, confidence, or context\n\nUse scripts/validate_request.py first if request quality is uncertain, then scripts/run_adi.py to execute it."
      },
      {
        "title": "2. Freeform mode",
        "body": "The user provides a natural-language tradeoff problem.\n\nFirst use scripts/normalize_problem.py to produce a request skeleton. Do not pretend the request is complete if important fields are missing. If the skeleton is not ready, ask for the missing inputs instead of inventing scores or constraints."
      },
      {
        "title": "Output contract",
        "body": "If ADI runs successfully, the final answer must contain:\n\nbest_option\na short rationale for why it won\ntop-ranked alternatives\nconfidence summary\nconstraint impact summary\nsensitivity or stability summary when available\nexplicit assumptions\n\nIf the request is not complete enough to run, return a request-completion prompt rather than a fabricated ranking."
      },
      {
        "title": "Workflow",
        "body": "Determine whether the user input is structured or freeform.\nFor freeform input, normalize it into a request skeleton using scripts/normalize_problem.py.\nValidate candidate requests with scripts/validate_request.py.\nRun complete requests with scripts/run_adi.py.\nPresent the ADI result in clear decision-support language:\n\nrecommendation first\nstrongest tradeoff second\ncaveats and sensitivity after that"
      },
      {
        "title": "Decision hygiene rules",
        "body": "Never rank options without explicit criteria.\nNever silently invent hard constraints.\nIf criterion direction is ambiguous, stop and clarify.\nNormalize vague goals into named criteria before scoring.\nPrefer a small, explicit criteria set over many overlapping criteria.\nKeep the policy choice visible: balanced, risk_averse, or exploratory."
      },
      {
        "title": "Output quality rules",
        "body": "Show the top recommendation first.\nExplain why it won.\nMention the strongest tradeoff.\nCall out eliminated or constraint-violating options.\nInclude confidence caveats when evidence is weak.\nUse a compact comparison table or structured bullet list when comparing several options."
      },
      {
        "title": "Safety and honesty rules",
        "body": "No hidden math.\nNo fake scores.\nNo fabricated evidence.\nDo not claim ADI ran if the runtime dependency is missing.\nDo not request API keys.\nDo not require network access for the core workflow.\nDo not tell the user to trust the ranking if the request is under-specified."
      },
      {
        "title": "Runtime requirements",
        "body": "python3\neither an importable adi-decision package or the adi CLI on PATH\n\nIf the ADI runtime is unavailable, stop with a clear error and explain that the dependency must be installed locally."
      },
      {
        "title": "References",
        "body": "Request schema: references/request_schema.md\nResult interpretation: references/result_interpretation.md\nPolicy guide: references/policy_guide.md\nUse cases: references/use_cases.md"
      },
      {
        "title": "Examples",
        "body": "examples/vendor_selection.json\nexamples/route_planning.json\nexamples/hiring_shortlist.json\nexamples/research_methods.json\nexamples/tool_selection.json"
      }
    ],
    "body": "ADI Decision Engine\nCore promise\n\nTurn a messy tradeoff problem into a structured, auditable multi-criteria decision and return a ranked recommendation with confidence and explanation.\n\nWhen to use this skill\n\nUse this skill when the user needs structured decision support rather than open-ended brainstorming. Typical triggers include:\n\nmulti-criteria decision analysis\nweighted scoring or option ranking\nvendor selection or procurement\nroute planning with explicit tradeoffs\nhiring shortlist ranking\ntool or platform comparison\npolicy-driven or auditable agent decisions\nInput modes\n\nThis skill supports exactly two input modes.\n\n1. Structured mode\n\nThe user already has a decision request with:\n\noptions\ncriteria\noptional constraints\noptional policy_name\noptional evidence, confidence, or context\n\nUse scripts/validate_request.py first if request quality is uncertain, then scripts/run_adi.py to execute it.\n\n2. Freeform mode\n\nThe user provides a natural-language tradeoff problem.\n\nFirst use scripts/normalize_problem.py to produce a request skeleton. Do not pretend the request is complete if important fields are missing. If the skeleton is not ready, ask for the missing inputs instead of inventing scores or constraints.\n\nOutput contract\n\nIf ADI runs successfully, the final answer must contain:\n\nbest_option\na short rationale for why it won\ntop-ranked alternatives\nconfidence summary\nconstraint impact summary\nsensitivity or stability summary when available\nexplicit assumptions\n\nIf the request is not complete enough to run, return a request-completion prompt rather than a fabricated ranking.\n\nWorkflow\nDetermine whether the user input is structured or freeform.\nFor freeform input, normalize it into a request skeleton using scripts/normalize_problem.py.\nValidate candidate requests with scripts/validate_request.py.\nRun complete requests with scripts/run_adi.py.\nPresent the ADI result in clear decision-support language:\nrecommendation first\nstrongest tradeoff second\ncaveats and sensitivity after that\nDecision hygiene rules\nNever rank options without explicit criteria.\nNever silently invent hard constraints.\nIf criterion direction is ambiguous, stop and clarify.\nNormalize vague goals into named criteria before scoring.\nPrefer a small, explicit criteria set over many overlapping criteria.\nKeep the policy choice visible: balanced, risk_averse, or exploratory.\nOutput quality rules\nShow the top recommendation first.\nExplain why it won.\nMention the strongest tradeoff.\nCall out eliminated or constraint-violating options.\nInclude confidence caveats when evidence is weak.\nUse a compact comparison table or structured bullet list when comparing several options.\nSafety and honesty rules\nNo hidden math.\nNo fake scores.\nNo fabricated evidence.\nDo not claim ADI ran if the runtime dependency is missing.\nDo not request API keys.\nDo not require network access for the core workflow.\nDo not tell the user to trust the ranking if the request is under-specified.\nRuntime requirements\npython3\neither an importable adi-decision package or the adi CLI on PATH\n\nIf the ADI runtime is unavailable, stop with a clear error and explain that the dependency must be installed locally.\n\nReferences\nRequest schema: references/request_schema.md\nResult interpretation: references/result_interpretation.md\nPolicy guide: references/policy_guide.md\nUse cases: references/use_cases.md\nExamples\nexamples/vendor_selection.json\nexamples/route_planning.json\nexamples/hiring_shortlist.json\nexamples/research_methods.json\nexamples/tool_selection.json"
  },
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    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/dimgouso/adi-decision-engine",
    "publisherUrl": "https://clawhub.ai/dimgouso/adi-decision-engine",
    "owner": "dimgouso",
    "version": "0.1.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
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