{
  "description": "GASP conformance set for Apache Ossie: one case per construct OSSIE_SQL_2026 cannot state (the staged reasons in the GASP export), each with a complete model, a standard SQL reference query, the expected result on the GASP fixture and the rows it reads. Written by npm run conformance in gasp-wiki-mcp.",
  "period": {
    "start": "2026-01-01",
    "end": "2026-06-30"
  },
  "data": {
    "customer": {
      "rows": 365,
      "slice": "snapshot rows on month starts, month ends and the period bounds shifted back by one, three, six and twelve months"
    },
    "invoice": {
      "rows": 328,
      "slice": "snapshot rows on month starts, month ends and the period bounds shifted back by one, three, six and twelve months"
    },
    "opportunity": {
      "rows": 218,
      "slice": "snapshot rows on month starts, month ends and the period bounds shifted back by one, three, six and twelve months"
    },
    "product_event": {
      "rows": 2977,
      "slice": "rows dated from 2025-12-01"
    },
    "revenue_event": {
      "rows": 26,
      "slice": "rows dated from 2025-12-01"
    },
    "service_metrics": {
      "rows": 18,
      "slice": "rows dated from 2025-12-01"
    },
    "subscription": {
      "rows": 341,
      "slice": "snapshot rows on month starts, month ends and the period bounds shifted back by one, three, six and twelve months"
    },
    "ticket": {
      "rows": 31,
      "slice": "rows dated from 2025-12-01"
    },
    "user": {
      "rows": 399,
      "slice": "snapshot rows on month starts, month ends and the period bounds shifted back by one, three, six and twelve months"
    }
  },
  "cases": [
    {
      "id": "c01_cohort_nrr",
      "reason": "cohort",
      "construct": "a cohort fixed at the opening of the period and followed through its events (Net Revenue Retention)",
      "metric": "gasp_nrr",
      "gasp_id": "nrr",
      "formula": "(metric.mrr at period_start + metric.expansion_mrr - metric.contraction_mrr - metric.churned_mrr) / metric.mrr at period_start over cohort(field.customer_id, period_start)",
      "unit": "percent",
      "datasets": [
        "revenue_event",
        "customer",
        "subscription"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 0.9867549668874173
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/core/net-revenue-retention-nrr"
    },
    {
      "id": "c02_lag_mrr_growth_rate",
      "reason": "lag",
      "construct": "the same aggregate one period earlier (period-over-period growth)",
      "metric": "gasp_mrr_growth_rate",
      "gasp_id": "mrr_growth_rate",
      "formula": "(metric.mrr - lag(metric.mrr, 1 month)) / lag(metric.mrr, 1 month)",
      "unit": "percent",
      "datasets": [
        "subscription"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 0.11009174311926606
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/core/mrr-growth-rate"
    },
    {
      "id": "c03_at_period_start_logo_churn_rate",
      "reason": "at period_start",
      "construct": "a snapshot read at the opening of the period beside events inside it",
      "metric": "gasp_logo_churn_rate",
      "gasp_id": "logo_churn_rate",
      "formula": "metric.churned_customers / metric.total_customers at period_start",
      "unit": "percent",
      "datasets": [
        "revenue_event",
        "customer"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 0.25
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/core/logo-churn-rate-customer-churn"
    },
    {
      "id": "c04_total_ticket_distribution_by_type",
      "reason": "total",
      "construct": "a share of the ungrouped total inside a breakdown",
      "metric": "gasp_ticket_distribution_by_type",
      "gasp_id": "ticket_distribution_by_type",
      "formula": "count_distinct(field.ticket_id) / total(metric.ticket_volume)",
      "unit": "percent",
      "datasets": [
        "ticket"
      ],
      "columns": [
        "ticket_type",
        "value"
      ],
      "expected": [
        {
          "ticket_type": "account",
          "value": 0.0967741935483871
        },
        {
          "ticket_type": "billing",
          "value": 0.12903225806451613
        },
        {
          "ticket_type": "bug",
          "value": 0.25806451612903225
        },
        {
          "ticket_type": "feature_request",
          "value": 0.0967741935483871
        },
        {
          "ticket_type": "how_to",
          "value": 0.3225806451612903
        },
        {
          "ticket_type": "other",
          "value": 0.0967741935483871
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/support/ticket-distribution-by-type"
    },
    {
      "id": "c05_period_bound_in_filter_win_rate",
      "reason": "period bound in filter",
      "construct": "the reporting period bound to a field other than the dataset's time field (opportunities by close date on a snapshot)",
      "metric": "gasp_win_rate",
      "gasp_id": "win_rate",
      "formula": "count(field.opportunity_id where field.opportunity_stage = 'closed_won' and in_period(field.close_date)) / count(field.opportunity_id where field.opportunity_stage in ('closed_won', 'closed_lost') and in_period(field.close_date))",
      "unit": "percent",
      "datasets": [
        "opportunity"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 0.5
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/sales/win-rate"
    },
    {
      "id": "c06_to_date_ticket_backlog",
      "reason": "to_date",
      "construct": "an aggregate cumulative from the start of history to the period end",
      "metric": "gasp_ticket_backlog",
      "gasp_id": "ticket_backlog",
      "formula": "count_distinct(field.ticket_id where field.ticket_status in ('open', 'pending')) at period_end",
      "unit": "count",
      "datasets": [
        "ticket"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 7
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/support/ticket-backlog"
    },
    {
      "id": "c07_period_days_uptime",
      "reason": "period_days",
      "construct": "the number of days in the reporting period as a value",
      "metric": "gasp_uptime",
      "gasp_id": "uptime",
      "formula": "1 - sum(field.downtime_minutes) / (period_days * 1440)",
      "unit": "percent",
      "datasets": [
        "service_metrics"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 0.9997582872928177
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/engineering/uptime-availability"
    },
    {
      "id": "c08_bucket_aging_buckets",
      "reason": "bucket",
      "construct": "a dimension defined by ranges over a per-row expression",
      "metric": "gasp_aging_buckets",
      "gasp_id": "aging_buckets",
      "formula": "sum(field.outstanding_amount) / total(sum(field.outstanding_amount))",
      "unit": "percent",
      "datasets": [
        "invoice"
      ],
      "columns": [
        "bucket",
        "value"
      ],
      "expected": [
        {
          "bucket": "current",
          "value": 1
        },
        {
          "bucket": "days_31_60",
          "value": 0
        },
        {
          "bucket": "days_61_90",
          "value": 0
        },
        {
          "bucket": "days_90_plus",
          "value": 0
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/revops/aging-buckets"
    },
    {
      "id": "c09_window_dau_mau",
      "reason": "window",
      "construct": "trailing windows of different lengths ending at the period end (one day over thirty)",
      "metric": "gasp_dau_mau",
      "gasp_id": "dau_mau",
      "formula": "metric.daily_active_users_dau / metric.monthly_active_users_mau",
      "unit": "percent",
      "datasets": [
        "product_event"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 0.23809523809523808
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/product/daumau-ratio-stickiness"
    },
    {
      "id": "c10_distinct_pair_breadth_of_adoption",
      "reason": "distinct pair",
      "construct": "a count of distinct tuples (account and feature pairs)",
      "metric": "gasp_breadth_of_adoption",
      "gasp_id": "breadth_of_adoption",
      "formula": "count_distinct(field.customer_id, field.feature_id) / metric.active_accounts",
      "unit": "ratio",
      "datasets": [
        "product_event"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 4.785714285714286
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/product/breadth-of-adoption"
    },
    {
      "id": "c11_all_time_user_retention_day_n",
      "reason": "all_time",
      "construct": "an unbounded range beside a bounded one (users who signed up in the period, active any time after)",
      "metric": "gasp_user_retention_day_n",
      "gasp_id": "user_retention_day_n",
      "formula": "all_time(count_distinct(field.user_id where field.product_event_type = 'qualifying' and in_period(field.signup_date) and (field.event_date - field.signup_date) = 30)) / count_distinct(field.user_id where in_period(field.signup_date))",
      "unit": "percent",
      "datasets": [
        "user",
        "product_event"
      ],
      "columns": [
        "value"
      ],
      "expected": [
        {
          "value": 0.7142857142857143
        }
      ],
      "canonical": "https://koinedata.com/v1/metrics/product/user-retention-day-n"
    }
  ]
}