Data model artifacts for Prometheus
93
Pending
Does it follow best practices?
Impact
93%
1.13xAverage score across 10 eval scenarios
Pending
The risk profile of this skill
{
"context": "Evaluates whether the solution builds and serializes Prometheus summary metrics using github.com/prometheus/client_model/go types. Checks correct population of summary fields, construction and ordering of quantile entries, wrapping in metric containers, and protobuf marshaling behavior.",
"type": "weighted_checklist",
"checklist": [
{
"name": "Summary fields",
"description": "Uses github.com/prometheus/client_model/go (`dto`) Summary with SampleCount and SampleSum populated via proto helper functions (e.g., proto.Uint64/proto.Float64) instead of bare values.",
"max_score": 30
},
{
"name": "Quantile entries",
"description": "Creates one dto.Quantile per requested phi with Quantile and Value pointers set, keeps entries sorted by phi ascending, and omits placeholders for invalid inputs.",
"max_score": 30
},
{
"name": "Metric wrapping",
"description": "Places the summary inside a dto.Metric (and, if emitting a family, sets MetricType_SUMMARY on dto.MetricFamily) rather than encoding a custom struct.",
"max_score": 20
},
{
"name": "Protobuf marshal",
"description": "Serializes dependency types with proto.Marshal from google.golang.org/protobuf/proto, returning those bytes and propagating marshal errors.",
"max_score": 20
}
]
}docs
evals
scenario-1
scenario-2
scenario-3
scenario-4
scenario-5
scenario-6
scenario-7
scenario-8
scenario-9
scenario-10