[
  {
    "id": "diag-01",
    "input": "We've seen a spike in complaints from our warehouse team this month compared to last month.",
    "expectedPrimaryTypes": [
      "unexplained_change"
    ],
    "expectedReasoningModes": [
      "establish_baseline",
      "identify_difference"
    ],
    "shouldIdentify": [
      "complaints",
      "warehouse",
      "baseline comparison"
    ],
    "shouldNotInfer": [
      "quality issue",
      "staff turnover",
      "training gap"
    ],
    "description": "Baseline comparison — change without context. Should NOT jump to conclusions about quality or staff issues.",
    "expectedBehaviours": [
      {
        "id": "b-baseline",
        "description": "Identifies prior state or baseline period",
        "type": "baseline_recognition",
        "acceptedSignals": [
          "baseline",
          "previous period",
          "before comparison",
          "pre-change",
          "prior state"
        ],
        "required": true
      },
      {
        "id": "b-nosub",
        "description": "Does NOT assert warehouse quality/staff issues as cause",
        "type": "unsupported_justification",
        "prohibitedSignals": [
          "quality issue",
          "staff turnover",
          "training gap"
        ],
        "required": true
      },
      {
        "id": "b-nq1",
        "description": "Asks about baseline detail (absolute numbers, time frame)",
        "type": "next_question_target",
        "acceptedSignals": [
          "baseline",
          "number",
          "period",
          "volume",
          "count",
          "over what period"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-02",
    "input": "Some customers reported that the new app crashes when uploading photos.",
    "expectedPrimaryTypes": [
      "observed_problem"
    ],
    "expectedReasoningModes": [
      "identify_difference",
      "establish_baseline"
    ],
    "shouldIdentify": [
      "app crashes",
      "photo upload",
      "some customers"
    ],
    "shouldNotInfer": [
      "all users affected",
      "server-side bug",
      "Android only"
    ],
    "description": "Subset modifier — 'some customers' means not universal. Should distinguish from blanket claims.",
    "expectedBehaviours": [
      {
        "id": "b-subset",
        "description": "Recognises subset scope rather than universal claim",
        "type": "subset_recognition",
        "acceptedSignals": [
          "some",
          "subset",
          "partial",
          "not universal",
          "certain users",
          "limited to"
        ],
        "required": true
      },
      {
        "id": "b-obv",
        "description": "Acknowledges photo-upload context from the scenario",
        "type": "observation_recognition",
        "acceptedSignals": [
          "photo",
          "upload",
          "crash",
          "app"
        ],
        "required": false
      },
      {
        "id": "b-nq2",
        "description": "Asks about which user groups are affected vs unaffected",
        "type": "next_question_target",
        "acceptedSignals": [
          "who",
          "which users",
          "affected group",
          "distinguish",
          "proportion"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-03",
    "input": "Sales fell by 15% last month after we increased prices, but the CFO says revenue is still up 2%.",
    "expectedPrimaryTypes": [
      "contradiction"
    ],
    "expectedReasoningModes": [
      "investigate_contradiction",
      "establish_baseline"
    ],
    "shouldIdentify": [
      "sales decline",
      "price increase",
      "revenue increase",
      "CFO report"
    ],
    "shouldNotInfer": [
      "price was set too high",
      "competitors gained market share",
      "revenue data is wrong"
    ],
    "description": "Apparent contradiction — sales down but revenue up after price change. Distinguishes volume vs value.",
    "expectedBehaviours": [
      {
        "id": "b-metric",
        "description": "Recognises revenue/sales as different metric dimensions",
        "type": "metric_relationship",
        "acceptedSignals": [
          "rate",
          "denominator",
          "comparable scale",
          "volume vs value",
          "per unit",
          "absolute vs relative"
        ],
        "required": true
      },
      {
        "id": "b-contra",
        "description": "Identifies the apparent contradiction between sales and revenue signals",
        "type": "contradiction_recognition",
        "acceptedSignals": [
          "contradiction",
          "divergent",
          "opposing",
          "conflicting",
          "conversely",
          "but"
        ],
        "required": true
      },
      {
        "id": "b-trans",
        "description": "Acknowledges temporal caution in cause-effect timing",
        "type": "transition_recognition",
        "acceptedSignals": [
          "transition",
          "before to",
          "moved from",
          "after",
          "since"
        ],
        "required": false
      },
      {
        "id": "b-nq3",
        "description": "Asks about sales volume and revenue composition breakdown",
        "type": "next_question_target",
        "acceptedSignals": [
          "sales volume",
          "revenue composition",
          "unit price",
          "average",
          "breakdown"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-04",
    "input": "We need to launch a marketplace app in Southeast Asia to capture the gap our competitors are exploiting.",
    "expectedPrimaryTypes": [
      "decision_request"
    ],
    "expectedReasoningModes": [
      "decision_support",
      "identify_missing_information"
    ],
    "shouldIdentify": [
      "marketplace app",
      "Southeast Asia",
      "competitor gap"
    ],
    "shouldNotInfer": [
      "this will definitely succeed",
      "we have the resources",
      "competitors are struggling"
    ],
    "description": "Decision request — forward-looking, needs missing info identification.",
    "expectedBehaviours": [
      {
        "id": "b-action",
        "description": "Recognises forward-looking proposed action",
        "type": "proposed_action_recognition",
        "acceptedSignals": [
          "decision_request",
          "desired_outcome",
          "action plan"
        ],
        "required": true
      },
      {
        "id": "b-nosub2",
        "description": "Does NOT treat competitor gap as quantified fact",
        "type": "unsupported_justification",
        "prohibitedSignals": [
          "competitor gap",
          "gap confirmed",
          "we lack"
        ],
        "required": true
      },
      {
        "id": "b-nq4",
        "description": "Asks about market gap size and scope",
        "type": "next_question_target",
        "acceptedSignals": [
          "gap size",
          "market size",
          "scope",
          "extent",
          "how big"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-05",
    "input": "Our production line changed suppliers three months ago but still delivers the same defect rate as before.",
    "expectedPrimaryTypes": [
      "unexplained_change"
    ],
    "expectedReasoningModes": [
      "establish_baseline",
      "identify_difference"
    ],
    "shouldIdentify": [
      "supplier change",
      "three months ago",
      "same defect rate"
    ],
    "shouldNotInfer": [
      "new supplier is worse",
      "old supplier was better",
      "quality process is broken"
    ],
    "description": "Unexpected continuity — changed context but no outcome change.",
    "expectedBehaviours": [
      {
        "id": "b-mnorm",
        "description": "Recognises unexpected continuity despite change input",
        "type": "measurement_normalisation",
        "acceptedSignals": [
          "normalise",
          "denominator",
          "rate",
          "comparable scale",
          "per unit"
        ],
        "required": true
      },
      {
        "id": "b-timing",
        "description": "Acknowledges timing of the supplier change vs outcome measurement",
        "type": "timing_recognition",
        "acceptedSignals": [
          "after",
          "three months",
          "timeline",
          "time lag",
          "delayed effect"
        ],
        "required": false
      },
      {
        "id": "b-nq5",
        "description": "Asks why input change produced no outcome change",
        "type": "next_question_target",
        "acceptedSignals": [
          "why",
          "same rate",
          "defect rate comparison",
          "baseline",
          "period of measurement"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-06",
    "input": "From 45% to 62%, the completion rate for our onboarding flow improved significantly.",
    "expectedPrimaryTypes": [
      "unexplained_change"
    ],
    "expectedReasoningModes": [
      "establish_baseline",
      "validate_measurement"
    ],
    "shouldIdentify": [
      "completion rate",
      "45%",
      "62%",
      "onboarding"
    ],
    "shouldNotInfer": [
      "all improvements are due to the redesign",
      "the old flow was bad",
      "users prefer the new design"
    ],
    "description": "Quantified improvement — needs context about measurement period and baseline conditions.",
    "expectedBehaviours": [
      {
        "id": "b-baseline2",
        "description": "Recognises quantified improvement needs context for significance",
        "type": "baseline_recognition",
        "acceptedSignals": [
          "baseline",
          "previous period",
          "comparison point",
          "reference",
          "benchmark",
          "pre-change"
        ],
        "required": true
      },
      {
        "id": "b-nq6",
        "description": "Asks about timeframe, cohort, and baseline conditions",
        "type": "next_question_target",
        "acceptedSignals": [
          "timeframe",
          "cohort",
          "baseline condition",
          "measurement period",
          "sample size"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-07",
    "input": "A user claimed that our pricing model is too complex for small businesses.",
    "expectedPrimaryTypes": [
      "reported_claim"
    ],
    "expectedReasoningModes": [
      "validate_claim",
      "identify_difference"
    ],
    "shouldIdentify": [
      "pricing complexity",
      "small business",
      "user claim"
    ],
    "shouldNotInfer": [
      "the pricing is actually complex",
      "other small businesses agree",
      "we should simplify pricing"
    ],
    "description": "Single reported claim — needs validation, not acceptance as fact.",
    "expectedBehaviours": [
      {
        "id": "b-cval",
        "description": "Treats single-user claim as needing corroboration, not acceptance",
        "type": "claim_validation",
        "acceptedSignals": [
          "validate",
          "corroborate",
          "verify",
          "confirm",
          "evidence needed",
          "single user",
          "unverified"
        ],
        "required": true
      },
      {
        "id": "b-nq7",
        "description": "Asks for examples or corroboration from other users",
        "type": "next_question_target",
        "acceptedSignals": [
          "examples",
          "corroborate",
          "other users",
          "more examples",
          "survey",
          "feedback"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-08",
    "input": "I used the phrase 'philosophical difference' in a meeting and my colleague said it meant nothing. Is that fair?",
    "expectedPrimaryTypes": [
      "ambiguous_statement"
    ],
    "expectedReasoningModes": [
      "clarify_meaning"
    ],
    "shouldIdentify": [
      "philosophical",
      "ambiguous",
      "meaning clarification"
    ],
    "shouldNotInfer": [
      "the phrase was wrong",
      "the colleague is hostile",
      "we should avoid philosophical language"
    ],
    "description": "Meta-test — self-referential ambiguous statement. Should trigger clarification mode.",
    "expectedBehaviours": [
      {
        "id": "b-ambig",
        "description": "Recognises the phrase as ambiguous and requiring clarification",
        "type": "ambiguity_recognition",
        "acceptedSignals": [
          "ambiguous",
          "unclear meaning",
          "clarify",
          "interpretation varies",
          "phrase intent"
        ],
        "required": true
      },
      {
        "id": "b-nq8",
        "description": "Asks about the phrase intent in meeting context",
        "type": "next_question_target",
        "acceptedSignals": [
          "intent",
          "meaning",
          "context",
          "why said",
          "what meant",
          "phrase intent"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-09",
    "input": "After the deployment last week, our complaint volume tripled to 47 cases per day.",
    "expectedPrimaryTypes": [
      "causal_claim"
    ],
    "expectedReasoningModes": [
      "investigate_contradiction",
      "establish_baseline"
    ],
    "shouldIdentify": [
      "deployment",
      "complaint volume increase",
      "tripled",
      "47 cases"
    ],
    "shouldNotInfer": [
      "the deployment caused the complaints",
      "the bug report was insufficient",
      "rollback is needed"
    ],
    "description": "Post-event spike — presents correlation as potential causation. Must resist jumping to causal conclusion.",
    "expectedBehaviours": [
      {
        "id": "b-trans2",
        "description": "Distinguishes temporal sequence from causal proof",
        "type": "transition_recognition",
        "acceptedSignals": [
          "transition",
          "before to",
          "after",
          "temporal sequence",
          "coincidence vs cause"
        ],
        "required": true
      },
      {
        "id": "b-baseline3",
        "description": "Recognises need for pre-deployment complaint baseline",
        "type": "baseline_recognition",
        "acceptedSignals": [
          "baseline",
          "previous level",
          "before deployment",
          "pre-change",
          "historical"
        ],
        "required": true
      },
      {
        "id": "b-nq9",
        "description": "Asks about evidence distinguishing deployment effect from coincidence",
        "type": "next_question_target",
        "acceptedSignals": [
          "coincidence",
          "deployment timing",
          "baseline comparison",
          "other factors",
          "confounders"
        ],
        "required": false
      }
    ]
  },
  {
    "id": "diag-10",
    "input": "Some complaints involve production issues, but others say the delivery team is slow.",
    "expectedPrimaryTypes": [
      "observed_problem"
    ],
    "expectedReasoningModes": [
      "identify_difference",
      "decompose_aggregate"
    ],
    "shouldIdentify": [
      "production issues",
      "delivery speed",
      "complaint types"
    ],
    "shouldNotInfer": [
      "production is worse than delivery",
      "the delivery team needs training",
      "both teams are underperforming equally"
    ],
    "description": "Paired with diag-01 — distinguishes subset complaints from aggregate claims.",
    "expectedBehaviours": [
      {
        "id": "b-obs2",
        "description": "Decomposes complaints into distinct categories rather than merging",
        "type": "observation_recognition",
        "acceptedSignals": [
          "complaint",
          "production",
          "delivery",
          "categories",
          "types of complaint",
          "decompose"
        ],
        "required": true
      },
      {
        "id": "b-metric2",
        "description": "Avoids merging complaint types without quantification",
        "type": "metric_relationship",
        "acceptedSignals": [
          "rate",
          "comparable scale",
          "proportion",
          "percentage",
          "volume vs value"
        ],
        "required": false
      },
      {
        "id": "b-nq10",
        "description": "Asks about complaint category proportions (production vs delivery)",
        "type": "next_question_target",
        "acceptedSignals": [
          "proportion",
          "percentage",
          "ratio",
          "how many",
          "which is worse",
          "split"
        ],
        "required": false
      }
    ]
  }
]