> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getnetra.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Netra Skills

> Use Netra Skills to equip your AI coding assistant with proven instrumentation patterns, best practices, and automated evaluation workflows for your apps.

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.

## Quick Start

You can add these skills to your local environment in seconds:

```bash theme={null}
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.

***

## Available skills

| Skill                                                          | Best For                                                                                                                                  | Install command                                                                                                          |
| :------------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------- |
| <div style={{ width: '180px' }}>**netra-best-practices**</div> | <div style={{ width: '280px' }}>End-to-end instrumentation, observability, and evaluation.</div>                                          | <div style={{ width: '600px' }}>`npx skills add KeyValueSoftwareSystems/netra-skills --skill netra-best-practices`</div> |
| <div style={{ width: '180px' }}>**netra-mcp-usage**</div>      | <div style={{ width: '280px' }}>MCP workflows for trace debugging, dataset setup, evaluator configuration, and test run management.</div> | <div style={{ width: '600px' }}>`npx skills add KeyValueSoftwareSystems/netra-skills --skill netra-mcp-usage`</div>      |

#### 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.

#### 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.

***

## Related Resources

* [Netra MCP](/mcp/overview) - Connect Netra's remote MCP server to your editor
* [Tracing Quick Start](/quick-start/QuickStart_Tracing) - Fastest path to visible traces
* [Evaluation Quick Start](/quick-start/QuickStart_Evals) - Build datasets and evaluators
* [Simulation Quick Start](/quick-start/QuickStart_Simulation) - Test multi-turn behavior
* [SDK Overview](/sdk-reference/sdk/overview) - Core SDK concepts