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.
npx skills add liortesta/ClawdAgent --skill nemo-evaluatorgit clone --depth 1 https://github.com/liortesta/ClawdAgentWrote 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.
[](https://agentmods.dev/skills/liortesta/clawdagent/nemo-evaluator)<a href="https://agentmods.dev/skills/liortesta/clawdagent/nemo-evaluator"><img src="https://agentmods.dev/badge/skills/liortesta/clawdagent/nemo-evaluator.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00076 | $0.03310 |
| Opus 5 | $0.00038 | $0.01655 |
| Sonnet 5 | $0.00015 | $0.00662 |
| Haiku 4.5 | $0.00008 | $0.00331 |
Grade A, and why
nemo-evaluator-sdk 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 4d 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.
This is a copy
100% identical to nemo-evaluator-sdk — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 495 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeMo Evaluator SDK - Enterprise LLM Benchmarking
Quick Start
NeMo Evaluator SDK evaluates LLMs across 100+ benchmarks from 18+ harnesses using containerized, reproducible evaluation with multi-backend execution (local Docker, Slurm HPC, Lepton cloud).
Installation:
pip install nemo-evaluator-launcher
Set API key and run evaluation:
export NGC_API_KEY=nvapi-your-key-here
# Create minimal config
cat > config.yaml << 'EOF'
defaults:
- execution: local
- deployment: none
- _self_
execution:
output_dir: ./results
target:
api_endpoint:
model_id: meta/llama-3.1-8b-instruct
url: https://integrate.api.nvidia.com/v1/chat/completions
api_key_name: NGC_API_KEY
evaluation:
tasks:
- name: ifeval
EOF
# Run evaluation
nemo-evaluator-launcher run --config-dir . --config-name config
View available tasks:
nemo-evaluator-launcher ls tasks
Common Workflows
Workflow 1: Evaluate Model on Standard Benchmarks
Run core academic benchmarks (MMLU, GSM8K, IFEval) on any OpenAI-compatible endpoint.
Checklist:
Standard Evaluation:
- [ ] Step 1: Configure API endpoint
- [ ] Step 2: Select benchmarks
- [ ] Step 3: Run evaluation
- [ ] Step 4: Check results
Step 1: Configure API endpoint
# config.yaml
defaults:
- execution: local
- deployment: none
- _self_
execution:
output_dir: ./results
target:
api_endpoint:
model_id: meta/llama-3.1-8b-instruct
url: https://integrate.api.nvidia.com/v1/chat/completions
api_key_name: NGC_API_KEY
For self-hosted endpoints (vLLM, TRT-LLM):
target:
api_endpoint:
model_id: my-model
url: http://localhost:8000/v1/chat/completions
api_key_name: "" # No key needed for local
Step 2: Select benchmarks
Add tasks to your config:
evaluation:
tasks:
- name: ifeval # Instruction following
- name: gpqa_diamond # Graduate-level QA
env_vars:
HF_TOKEN: HF_TOKEN # Some tasks need HF token
- name: gsm8k_cot_instruct # Math reasoning
- name: humaneval # Code generation
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.
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.
- 4d ago First seen · 495 lines · 76 tokens per session scan A 97bc3483974c
nemo-evaluator-sdk is a skill published in the GitHub repository liortesta/ClawdAgent (11 stars, last pushed 11d ago), licensed Apache-2.0. It adds 76 tokens to every session and 3,310 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to nemo-evaluator-sdk, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
nemo-evaluator-sdk
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
nemo-evaluator-sdk
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
nemo-evaluator-sdk
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
nemo-evaluator-sdk
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
nemo-evaluator-sdk
Evaluates LLMs across 100+ benchmarks from 18+ harnesses (MMLU, HumanEval, GSM8K, safety, VLM) with multi-backend execution. Use when needing scalable evaluation on local Docker, Slurm HPC, or cloud platforms. NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.