slug: hypertune
provider: Hypertune
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: 1
edges:
- tag: Graph QL
  spec_file: hypertune-graph-ql-api-openapi.yml
  capability_id: BC-4210.50
  capability_id_l1: BC-4210
  capability_name: Feature Flag Management
  confidence: 0.78
  evidence: 'GET /graphql evaluateFlags — "Evaluate flags, experiments, and config via the GraphQL Edge
    API"; description: "edge service that delivers feature-flag logic"'
  reason: The two operations are the runtime evaluation surface of a feature-flag platform — flag logic
    fetched from the edge and evaluated for a given context. That is runtime control/targeting of feature
    flags (BC-4210.50) rather than a generic GraphQL gateway. Some ambiguity remains because the same
    endpoint also serves experiment assignment (A/B test execution), so confidence is held below 0.9.