netra-custom-metrics
Netra Custom Metrics
Use this skill to instrument business and operational KPIs alongside Netra traces.
When To Use
- You need request, latency, token, and cost metrics.
- You need queue depth or in-flight gauges.
- You want periodic metrics export via OTLP/HTTP JSON.
Procedure
- Initialize Netra with
enable_metrics=True. - Get a meter via
Netra.get_meter("service-name"). - Define instruments: counter, histogram, up-down counter, observable counter.
- Record values with low-cardinality attributes.
- Call
Netra.shutdown()on process exit to flush final export.
Python Pattern
from netra import Netra
More from keyvaluesoftwaresystems/netra-skills
netra-best-practices
Code-first Netra best-practices playbook covering setup, instrumentation, context tracking, custom spans/metrics, integration patterns, evaluation, simulation, and troubleshooting.
34netra-mcp-usage
Netra MCP trace-debugging workflow focused on query_traces, get_trace_by_id, and get_session_details, including exact input parameters, filter schema, operators, sorting, and pagination patterns.
19netra-simulation-setup
Set up Netra multi-turn simulations with scenario definitions, personas, fact checkers, evaluator configuration, and test-run analysis. Use to validate agent behavior before production.
4netra-evaluation-setup
Set up high-quality Netra evaluations with datasets, evaluator design, variable mapping, and repeatable test runs. Use for regression detection and quality benchmarking.
4netra-decorator-instrumentation
Create custom Netra tracing instrumentation using decorators. Use when choosing between auto-instrumentation, decorators, and manual tracing in Python or TypeScript, with clear semantic span design.
3netra-setup
Install and initialize the Netra SDK with environment-safe defaults, instrument selection, and shutdown handling.
2