Gym: Skill for Codex

.agents/skills/nemo-gym-debugging/SKILL.md

nemo-gym-debugging is a skill for Codex from NVIDIA-NeMo/Gym. It costs 68 tokens per session (781 once invoked), scanned A, original, Apache-2.0.

A troubleshooting guide for NeMo Gym, a system for running model-training and evaluation jobs. It groups failures by areas such as servers, configuration, data, verification, caching, and performance.

In plain words
What is it for?
Use it to diagnose missing or incomplete JSONL results, schema and verifier errors, Slurm or Ray failures, vLLM readiness problems, tool and sandbox issues, stale inputs, cache problems, and slow jobs.
Why use it?
It helps you investigate failed or suspicious runs in a consistent order instead of guessing which component is broken.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: installed under .agents/ (shared by several agents).

This is NVIDIA-NeMo/Gym's own configuration. It tells 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 →

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 · 1,320 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/nemo-gym-debugging/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/NVIDIA-NeMo/Gym

Made for: 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 nemo-gym-debugging

README.md
[![agentmods](https://agentmods.dev/badge/skills/nvidia-nemo/gym/nemo-gym-debugging/github.svg)](https://agentmods.dev/skills/nvidia-nemo/gym/nemo-gym-debugging)
Your own site
<a href="https://agentmods.dev/skills/nvidia-nemo/gym/nemo-gym-debugging"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/gym/nemo-gym-debugging/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for nemo-gym-debugging

Your own site · 80×15
<a href="https://agentmods.dev/skills/nvidia-nemo/gym/nemo-gym-debugging"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/gym/nemo-gym-debugging.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 781 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 $0.00068 $0.00781
Opus 5.5 $0.00027 $0.00312
Sonnet 5.5 $0.00014 $0.00156
Haiku 4.5 $0.00007 $0.00078

Measured 23d ago against content hash e3dfc4829f10, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

nemo-gym-debugging 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 23d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/check_tool_call_jsonl.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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/nemo-gym-debugging/SKILL.md · 62 lines

How it starts

The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Nemo Gym Debugging

Invocation Check

Use this skill when something failed or looks suspicious in a Nemo Gym run. If the task is adding a new env, use the add-benchmark skill; if it is changing profiling behavior, use the nemo-gym-reward-profiling skill.

Debug by classification, not by guessing. The first goal is to decide whether the issue is:

  • infra: Slurm, Ray, container, filesystem, network, ports
  • model serving: vLLM startup/readiness/throughput
  • config: wrong config bundle, missing agent, wrong extra args
  • data/schema: JSONL fields do not match verifier/resource server expectations
  • verifier/runtime: resource server exception or malformed verify response
  • cache/resume: stale materialized inputs or partial rollout output
  • throughput/resources: concurrency too high, judge bottleneck, tool/sandbox latency

Debug Order

  1. Check Slurm/Ray job state and logs.
  2. Check vLLM readiness and /models availability.
  3. Check Gym server readiness: all expected servers started.
  4. Check tool routing if the env uses tools; check sandbox readiness only if a sandbox is configured.
  5. Check materialized inputs and source data timestamps.
  6. Check rollout output and profiling/metrics output counts.
  7. Inspect the first real verifier exception, not shutdown noise.
  8. Compare failing row schema against the resource server request model.

High-Value Suspects

  • If data changed and resume_from_cache was enabled, stale materialized inputs are a first-class suspect.
  • If rollout output has a few rows and profiling is empty, inspect verifier errors and partial-output cache.
  • If all servers are ready but verifier returns 422/500, inspect request body schema before debugging infra.
  • If tool envs hang or partially work, check tool ownership/loading before changing model settings; check sandbox readiness only when a sandbox is actually part of the env.
  • If tool-call rows fail before generation with vLLM grammar/schema errors, read references/vllm-tool-call-schema-checks.md and run a static tool-schema check before changing Gym wrappers.
  • If logs only show nested "inner server" 500s without the real provider/verifier body, first enable existing request-boundary visibility with ++global_aiohttp_client_request_debug=True. Read references/request-boundary-visibility.md before changing code.

Read the full file on GitHub · 62 lines

Files

What ships with it

6 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. 23d ago Changed e3dfc4829f10
  2. 2mo ago First seen · 62 lines · 68 tokens per session scan A 1f005e915095

Subscribe to this mod's changes

nemo-gym-debugging is a skill published in the GitHub repository NVIDIA-NeMo/Gym (1,320 stars, last pushed today), licensed Apache-2.0. It adds 68 tokens to every session and 781 once invoked, about $0.0003 per session on Opus 5.5. 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.

Related

nemo-gym-debugging compared with skills from other repositories

nemo-gym-debugging next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
nemo-gym-debuggingNVIDIA-NeMo/GymSkill1.3ktodayApache-2.0A68Codex
adk-debuggoogle/adk-pythonSkill22kyesterdayApache-2.0A191Claude Code, Codex
resolve-handoffomnigent-ai/omnigentSkill11ktodayApache-2.0A23Claude Code, Codex
iterate-implementationhumanlayer/humanlayerSkill13kyesterdayApache-2.0A16Claude Code, Codex
typescript-testingComposioHQ/composioSkill31kyesterdayMITA92Claude Code, Codex
cli-e2eComposioHQ/composioSkill31kyesterdayMITA62Claude Code, Codex
rubber-duckkirodotdev/KiroCrewSkill4.8ktodayApache-2.0A161Claude Code, Codex