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Netra MCP solves this by providing a standardized interface for integrating observability into your workflows. Netra Skills bridge this gap by giving agents and developers ready-to-use patterns and primitives for reliable instrumentation. Instead of figuring everything out from scratch, you get a proven approach that works in real production environments.

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Quick Start

You can add these skills to your local environment in seconds: 
npx skills add KeyValueSoftwareSystems/netra-skills
The CLI will detect which agentic interfaces you have installed and drop the instructions in the right place. Agents that support skills should start to use them automatically.

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Available skills

SkillBest ForInstall command
netra-best-practices
End-to-end instrumentation, observability, and evaluation.
npx skills add KeyValueSoftwareSystems/netra-skills --skill netra-best-practices
netra-mcp-usage
MCP workflows for trace debugging, dataset setup, evaluator configuration, and test run management.
npx skills add KeyValueSoftwareSystems/netra-skills --skill netra-mcp-usage

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When To Use

  • You want one workflow that replaces separate setup and instrumentation skills.
  • You need traceability from request context to span-level debugging.
  • You need precise, schema-correct inputs for Netra MCP observability, dataset, evaluator, and test run tools.
  • You need repeatable quality validation with evaluations and simulations.
  • You need production-safe troubleshooting guidance.

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Coverage

  • Setup and Baseline: Initialize the SDK correctly for Python (FastAPI/OpenAI) and TypeScript (Express/OpenAI).
  • Context Tracking: Implement request identity (user_id, session_id, tenant_id) and conversation logging.
  • Trace Debugging: Execute precise netra_query_traces, netra_get_trace_by_id, and netra_get_session_details calls with correct schemas, sorting, and pagination.
  • Evaluation Setup: Create datasets, map evaluators, and manage test runs using MCP tools with schema-correct inputs.
  • Instrumentation Strategy: Choose between auto-instrumentation, decorators (@workflow, @agent, @task), and manual spans.
  • Advanced Observability: Implement usage/cost tracking, action records, and OpenTelemetry custom metrics.
  • Evaluation: Set up single-turn evaluations and multi-turn simulations with representative datasets.
  • Trace Analysis: Use Netra MCP observability tools to debug regressions and inspect session-level cost and error totals.
  • Test Run Review: Use netra_get_test_run_summary and netra_get_test_run_details to interpret evaluation results from your IDE.
Last modified on July 10, 2026