Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
76
63%
Does it follow best practices?
Impact
100%
1.17xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./data/skills-md/0xdarkmatter/claude-mods/python-observability-patterns/SKILL.mdStructured logging with structlog
Uses structlog
100%
100%
merge_contextvars processor
100%
100%
add_log_level processor
50%
100%
TimeStamper ISO format
100%
100%
JSONRenderer processor
100%
100%
wrapper_class setting
50%
100%
PrintLoggerFactory setting
100%
100%
get_logger usage
100%
100%
Keyword-argument log calls
100%
100%
bind_contextvars for request ID
80%
100%
ContextVar for request_id
0%
100%
UUID4 fallback request_id
100%
100%
clear_contextvars after request
50%
100%
Prometheus metrics instrumentation
Uses prometheus_client
100%
100%
Counter for request totals
100%
100%
Histogram for latency
100%
100%
Gauge for active connections
100%
100%
Counter metric name
100%
100%
Histogram metric name
100%
100%
Histogram buckets
0%
100%
Request labels
42%
100%
perf_counter timing
100%
100%
Active connections tracking
100%
100%
/metrics endpoint
100%
100%
generate_latest with text/plain
80%
100%
OpenTelemetry distributed tracing
Uses opentelemetry
100%
100%
TracerProvider setup
100%
100%
BatchSpanProcessor
100%
100%
OTLPSpanExporter
100%
100%
OTLP endpoint
100%
100%
set_tracer_provider
100%
100%
get_tracer with __name__
50%
100%
start_as_current_span
100%
100%
span.set_attribute
100%
100%
Nested child spans
100%
100%
Span naming
100%
100%
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