nemo-relay-instrument-typed-wrappers

nemo-relay-instrument-typed-wrappers is a skill for Claude Code, Codex from NVIDIA/NeMo-Relay. It costs 39 tokens per session (951 once invoked), scanned A, original, Apache-2.0.

A guide to adding typed wrappers around NeMo Relay tool and language-model calls. Typed wrappers translate application-specific values to JSON for Relay and back again for the application.

In plain words
What is it for?
Use it when adding dataclasses, Pydantic models, custom Node.js codecs, or provider-specific request and response codecs.
Why use it?
It lets applications use stable data models while keeping Relay middleware compatible with predictable JSON values.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one.

Good fit Use it when adding dataclasses, Pydantic models, custom Node.js codecs, or provider-specific request and response codecs.

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Install with agentmods
npx agentmods add skills/nvidia/nemo-relay/nemo-relay-instrument-typed-wrappers
About the project

NVIDIA/NeMo-Relay is a runtime and library that gives coding agents and applications shared control over agent execution scopes, policies, plugins, lifecycle events, and observability data. It is for developers who need to inspect or instrument runs from agents such as Codex or Claude Code, or integrate frameworks and export traces and trajectories. The catalogue entries provide Relay plugins, hooks, instructions, skills, and an MCP server for those workflows.

NVIDIA/NeMo-Relay · 169 stars · on GitHub · docs.nvidia.com

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add NVIDIA/NeMo-Relay --skill nemo-relay-instrument-typed-wrappers
Clone the repo
git clone --depth 1 https://github.com/NVIDIA/NeMo-Relay

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

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README.md
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Your own site
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agentmods 80×15 button for nemo-relay-instrument-typed-wrappers

Your own site · 80×15
<a href="https://agentmods.dev/skills/nvidia/nemo-relay/nemo-relay-instrument-typed-wrappers"><img src="https://agentmods.dev/badge/skills/nvidia/nemo-relay/nemo-relay-instrument-typed-wrappers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 951 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00039 $0.00951
Opus 5 $0.00019 $0.00476
Sonnet 5 $0.00008 $0.00190
Haiku 4.5 $0.00004 $0.00095

Measured 11d ago against content hash 81e1c7d38992, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

nemo-relay-instrument-typed-wrappers scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/nemo-relay-instrument-typed-wrappers/SKILL.md · 91 lines

How it starts

The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.
  • Built-in request codecs guarantee JSON-value identity for an unchanged annotation. They compare edits with a decoded baseline and patch only changed fields, preserving native representation details and unknown fields.
  • Use instructions, portable messages and components, and the tagged api_specific request surface for normalized edits. Provider-only union members use explicit { provider, kind, value } native components. Reserve top-level extra for unknown future fields.
  • The nemo-relay gateway always supplies matching request codecs on Anthropic Messages, OpenAI Chat Completions, and OpenAI Responses generation routes. On those routes, treat raw request.content as read-only and return body edits through the annotated request. Header edits still use the raw request.

Read the full file on GitHub · 91 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 91 lines · 39 tokens per session scan A 81e1c7d38992

Subscribe to this mod's changes

nemo-relay-instrument-typed-wrappers is a skill published in the GitHub repository NVIDIA/NeMo-Relay (169 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 951 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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