Gym: Skill for Claude Code

.agents/skills/add-benchmark/SKILL.md

add-benchmark is a skill for Claude Code, Codex from NVIDIA-NeMo/Gym. It costs 120 tokens per session (2,612 once invoked), scanned A, original, Apache-2.0.

A guide for adding benchmarks or training environments to NeMo-Gym. A benchmark is a repeatable test used to measure how well an AI system performs a task.

In plain words
What is it for?
Use it to prepare benchmark data, scaffold resource or agent servers, add dependencies, implement verification, and reproduce results before and after integration.
Why use it?
It provides the integration steps needed to connect a new evaluation or training task, including wrapping an existing third-party benchmark.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is NVIDIA-NeMo/Gym's own configuration. It tells Claude Code and Codex how to work on Gym itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Gym configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python scripts/print_aggregate_results.py +jsonl_fpath=results/profiled.jsonl.

About the project

NVIDIA-NeMo/Gym is a library and infrastructure for evaluating and improving models and agents inside environments, where each environment defines tasks, agent interaction, verification, and execution state. It is for teams running reproducible evaluations or training at scale across settings such as code execution, tool calling, and sandboxes, and the catalogue entries provide skills and instructions for working with it.

NVIDIA-NeMo/Gym · 1,168 stars · on GitHub · docs.nvidia.com

Reuse

Borrowing it

Nothing to install: this file belongs to NVIDIA-NeMo/Gym. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/NVIDIA-NeMo/Gym/main/.agents/skills/add-benchmark/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/NVIDIA-NeMo/Gym

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.

agentmods badge for add-benchmark

README.md
[![agentmods](https://agentmods.dev/badge/skills/nvidia-nemo/gym/add-benchmark.svg)](https://agentmods.dev/skills/nvidia-nemo/gym/add-benchmark)
Your own site
<a href="https://agentmods.dev/skills/nvidia-nemo/gym/add-benchmark"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/gym/add-benchmark.svg" alt="Measured on agentmods" height="20"></a>
Per session 120 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,612 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.
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 $0.00120 $0.02612
Opus 5 $0.00060 $0.01306
Sonnet 5 $0.00024 $0.00522
Haiku 4.5 $0.00012 $0.00261

Measured 7d ago against content hash d4212ba7d7de, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

add-benchmark 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 7d 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.

.agents/skills/add-benchmark/SKILL.md · 261 lines

How it starts

The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Add Benchmark to NeMo-Gym

Determine Integration Type

Before starting, determine which type of benchmark you're adding:

Native benchmark — verification logic implemented directly in a Gym resources server:

  • Resources server implements verify() with reward logic
  • Agent server orchestrates model calls (use simple_agent for single-turn, or custom agent for multi-turn)
  • Example: code_gen, instruction_following, math_with_judge

External benchmark — wrapping a 3rd-party library that has its own orchestration:

  • Integrate at the agent server level (not resources server)
  • Agent's /run endpoint wraps the external library
  • Pre-process from Gym schema to library input, post-process back to BaseVerifyResponse
  • Reproduce publicly reported numbers with the original repo first, then reproduce again after Gym integration
  • Add the dependency in requirements.txt

Workflow

Step 1: Scaffold the server

Run gym env init to generate the directory structure:

gym env init --resources-server my_benchmark

This creates:

resources_servers/my_benchmark/
├── app.py              # Server template
├── configs/my_benchmark.yaml
├── data/.gitignore
├── tests/test_app.py
├── requirements.txt
└── README.md

For external benchmarks, create the agent server manually under responses_api_agents/my_agent/ with the same structure.

Step 2: Prepare data

Convert your source dataset to Gym JSONL format. Each line must have responses_create_params.input (OpenAI message format). Task-specific verification data goes in verifier_metadata.

{
  "responses_create_params": {
    "input": [
      {"role": "system", "content": "System prompt"},
      {"role": "user", "content": "Problem statement"}
    ]
  },
  "verifier_metadata": {
    "test_cases": [{"input": "...", "expected_output": "..."}],
    "task_id": "unique_id"
  }
}

Data conversion: Write conversion scripts in the source repo (e.g. your dataset repository), not in NeMo-Gym. Prompt files also belong in the source repo. Exception: when there is no external source repo. See references/patterns.md § "Data Conversion Script Pattern".

Read the full file on GitHub · 261 lines

Files

What ships with it

1 file 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. 7d ago First seen · 261 lines · 120 tokens per session scan A d4212ba7d7de

Subscribe to this mod's changes

add-benchmark is a skill published in the GitHub repository NVIDIA-NeMo/Gym (1,168 stars, last pushed today), licensed Apache-2.0. It adds 120 tokens to every session and 2,612 once invoked, about $0.0006 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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