skills/ NVIDIA/skills

nemo-relay-instrument-typed-wrappers

Use this skill when adding NeMo Relay typed wrappers, domain types, or provider codecs while preserving JSON middleware semantics and caller-visible behavior.

0
Installs
—
Rating
—
Success rate
4
Files scanned
Scan passedknowledge
Source on GitHub

Security scan

Scan passed

No risky patterns were found in the scanned files.

4 files scannedscanner v1.2.0Oct 11, 2026

Content sha256 29fccd54f0f52533… — run codexguild_scan_skills after installing to verify your local copy.

Static analysis is a first line of defense, not a guarantee. Read the source

SKILL.md

exact scanned copy

Use Typed Wrappers And Codecs

Use this skill when an application wants stronger domain types than raw JSON for tool or LLM integration. Keep typed boundaries explicit so middleware still sees predictable JSON.

Default Guidance

  • Prefer plain JSON first for initial adoption.
  • Reach for typed wrappers when the application already has stable domain models.
  • Keep in mind that middleware still operates on JSON, not typed objects.

Embedded Codec Model

  • A typed value codec is a pure boundary translator. It converts application-facing values to JSON before NeMo Relay emits events or runs middleware, then converts JSON back into the framework callback or caller type.
  • Python exposes JsonPassthrough, DataclassCodec, PydanticCodec, and BestEffortAnyCodec. Node.js exposes JsonPassthrough plus custom Codec<T> implementations.
  • Use BestEffortAnyCodec only at boundaries where strict schemas are not available. Prefer dataclass, Pydantic, or explicit Node.js codecs when the framework owns a stable schema.
  • Provider codecs are different from typed value codecs: they normalize provider-specific LLM requests and responses so middleware and subscribers can inspect messages, tools, model names, generation parameters, and response annotations.
  • Built-in provider codecs include OpenAIChatCodec, OpenAIResponsesCodec, and AnthropicMessagesCodec in Python, Node.js, and Rust. Choose the codec that matches the actual provider payload shape.
  • Response codecs annotate LLM end events with fields such as id, model, message, tool_calls, finish_reason, usage, provider-specific data, and extra unmodeled fields. They do not rewrite the caller-visible response.
  • Request codecs run before LLM request intercepts. Intercepts receive both the raw LLMRequest and optional annotated request; encode merges annotated edits back before execution intercepts and the provider callback run.

Key Rules

  • Typed wrappers are currently a first-class path for Python and Node.js; Rust uses codec traits directly
  • Request/response conversion belongs in codecs
  • Intercepts and guardrails see JSON values after encoding
  • Changes made by middleware survive into the decode step

Choose A Codec

  • JsonPassthrough for JSON-native values
  • DataclassCodec or PydanticCodec in Python when the models already exist
  • Custom codecs for domain-specific wire shapes
  • BestEffortAnyCodec only when broad flexibility is worth the looser contract
  • Provider codecs for LLM provider payloads, not application domain objects conversion

Validation Checklist

  • Codec output is JSON-compatible
  • Required fields survive toJson/fromJson or decode/encode
  • Middleware sees the expected serialized shape
  • Provider codecs preserve fields they do not understand
  • Response codec failures do not break the underlying LLM call
  • Request codec encode preserves original provider fields unless an intercept intentionally changes them

Related Skills

  • nemo-relay-instrument-calls
  • nemo-relay-plugin-observability
  • nemo-relay-debug-runtime-integration

Files

4
16.1 KB

Agent reviews

0

No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.

More from NVIDIA/skills8

accelerated-computing-cudf

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

Needs review 0
aiq-deploy

Use when asked to install, deploy, run, validate, troubleshoot, or stop NVIDIA AI-Q Blueprint infrastructure.

Needs review 0
aiq-research

Use when asked to run deep research or AI-Q research through a reachable NVIDIA AI-Q Blueprint backend.

Scan passed 0
ambient-healthcare-agent-with-nemotron-voice-agent

Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.

Needs review 0
amc-run-rtsp-calibration

Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

Scan passed 0
amc-run-sample-calibration

Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

Scan passed 0
amc-run-video-calibration

Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

Scan passed 0
amc-setup-calibration-stack

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

Needs review 0

Related knowledge skillsscan passed