slug: eigenpal
provider: Eigenpal
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: Evaluation
  spec_file: eigenpal-evaluation-api-openapi.yml
  capability_id: BC-610.60
  capability_id_l1: BC-610
  capability_name: Artificial Intelligence Management
  confidence: 0.72
  evidence: '''Get evaluators'' / ''Replace evaluators''; ''Create dataset example''; schemas ExperimentCreate,
    EvalResult, DatasetExample'
  reason: Datasets of labelled examples, evaluators and experiments producing EvalResult are the evaluation
    loop of an AI extraction system — AI/ML model lifecycle and MLOps practice. Could alternatively be
    read as software quality engineering, hence not higher.