keyvaluesoftwaresystems Netra Best Practices
Use this skill as the end-to-end guide for integrating, operating, and improving AI systems with Netra.
Step 1 — Detect Project Language
Before doing anything else, determine whether the project is Python or TypeScript/JavaScript. Check the project root in this order:
| Signal file | Language |
|---|---|
pyproject.toml, setup.py, requirements.txt, Pipfile |
Python |
package.json, tsconfig.json, bun.lockb |
TypeScript / JavaScript |
If both are present (monorepo), ask the user which sub-project they are working on. If neither is found, ask the user.
From this point forward, use ONLY the references for the detected language. Never mix Python and TypeScript patterns.
Step 2 — Installation
Detect the package manager before installing netra-sdk. Check in priority order:
| Priority | Signal file | Command |
|---|---|---|
| 1 | uv.lock |
uv add netra-sdk |
| 2 | poetry.lock |
poetry add netra-sdk |
| 3 | pyproject.toml (no lock file above) |
pip install netra-sdk |
| 4 | requirements.txt (no Python indicators above) |
pip install netra-sdk |
| 5 | yarn.lock |
yarn add netra-sdk |
| 6 | pnpm-lock.yaml |
pnpm add netra-sdk |
| 7 | package-lock.json |
npm install netra-sdk |
| 8 | bun.lockb |
bun add netra-sdk |
| 9 | None found | Ask the user before proceeding |
Do NOT run multiple install commands or install globally.
Step 3 — Load the Right References
Based on the detected language and the user's use case, read the appropriate reference files:
Python references (under references/python/)
| Use case | Reference file |
|---|---|
| Instrumenting an LLM application | python/instrumentation.md |
| Running evaluations / test suites | python/evaluation.md |
| Running multi-turn simulations | python/simulation.md |
| Custom metrics (counters, histograms, gauges) | python/custom-metrics.md |
TypeScript references (under references/typescript/)
| Use case | Reference file |
|---|---|
| Instrumenting an LLM application | typescript/instrumentation.md |
| Running evaluations / test suites | typescript/evaluation.md |
| Running multi-turn simulations | typescript/simulation.md |
| Custom metrics (Currently not supported for TS) | typescript/custom-metrics.md |
Step 4 — Anti-Hallucination Rules
Follow these rules strictly when generating Netra code:
NEVER use enum values not listed in the Netra's official documentation. Do not guess.
NEVER mix Python and TypeScript conventions. Specifically:
- Python uses
snake_caseparameters (as_type,module_name,app_name) - TypeScript uses
camelCaseparameters (asType,moduleName,appName) - Python instrument sets:
{InstrumentSet.OPENAI}(plain set literal) - TypeScript instrument sets:
new Set([NetraInstruments.OPENAI])(Set constructor)
- Python uses
Respect lifecycle differences:
- Python:
Netra.init(...)is synchronous - TypeScript:
await Netra.init(...)is asynchronous — always await it - Python:
with Netra.start_span(...) as span:auto-closes - TypeScript:
span.end()must be called manually infinally
- Python:
When unsure about an API, follow the doc-fetching protocol below rather than guessing.
Step 5 — Doc-Fetching Fallback
If a use case is not covered by the reference files:
- Fetch the documentation index:
https://docs.getnetra.ai/llms.txt - Identify the relevant documentation page from the index.
- Fetch that page for the detailed guide.
Feedback
If the user is unhappy with the results, ask them to open an issue at https://github.com/KeyValueSoftwareSystems/netra-skills/issues/new.