slug: datafold
provider: Datafold
generated_by: planning/capability-mapping/scripts/classify_capabilities.py
model: claude-opus-5
frame:
- Software & Technology
min_confidence: 0.7
capability_model:
  source: https://github.com/vincentmakes/turbo-ea-capabilities
  license: CC-BY-4.0
  attribution: Turbo EA Capabilities by Vincent Verdet — Turbo EA, https://github.com/vincentmakes/turbo-ea-capabilities,
    CC BY 4.0
  notice: NOTICE
edge_count: 3
edges:
- tag: CI
  spec_file: datafold-ci-api-openapi.yml
  capability_id: BC-4210.10
  capability_id_l1: BC-4210
  capability_name: Continuous Integration Management
  confidence: 0.85
  evidence: '"List CI runs", "Trigger a PR/MR run", "Upload PR/MR changes" with schema "SubmitCiJob"'
  reason: Operations drive continuous-integration jobs against pull/merge requests, which is squarely
    Continuous Integration Management within software release and deployment. Confidence below 0.95 only
    because the CI runs are data-diff validations, a specialised pipeline check.
- tag: Monitors
  spec_file: datafold-monitors-api-openapi.yml
  capability_id: BC-610.30
  capability_id_l1: BC-610
  capability_name: Data Quality Management
  confidence: 0.85
  evidence: '"Create a Schema Change Monitor"; "Create a Data Test Monitor"; schema AnomalyDetectionThreshold'
  reason: Scheduled monitors running data tests, metric anomaly detection and schema-change alerting are
    classic data quality monitoring and remediation triggers.
- tag: Data diffs
  spec_file: datafold-data-diffs-api-openapi.yml
  capability_id: BC-610.30
  capability_id_l1: BC-610
  capability_name: Data Quality Management
  confidence: 0.8
  evidence: '"Create a data diff"; "Get a human-readable summary of a DataDiff comparison"; schemas ApiColumnDiffStat,
    ApiColumnProfileStats'
  reason: Diffing datasets across databases to surface row/column discrepancies and regressions is data
    quality monitoring/profiling and remediation support.