Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchnpx agentmods add skills/cxcscmu/skilllearnbench/model-verification-unit-testsWrote 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/cxcscmu/skilllearnbench/model-verification-unit-tests)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/model-verification-unit-tests"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/model-verification-unit-tests.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 | $0.00025 | $0.00211 |
| Opus 5 | $0.00013 | $0.00105 |
| Sonnet 5 | $0.00005 | $0.00042 |
| Haiku 4.5 | $0.00003 | $0.00021 |
Grade A, and why
model-verification-unit-tests 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 3d 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.
What it actually says
Model Verification with Unit Tests
Verifying individual components of a training pipeline, such as a loss function, ensures mathematical correctness before full-scale training.
Running Tests
Use the unittest framework or pytest. For a script like unit_test_1.py:
python unit_test/unit_test_1.py
Data Persistence
To allow external verification of results, save computed tensors to a .npz file:
import numpy as np
# In the test code:
np.savez(
"/path/to/loss.npz",
losses=losses.detach().cpu().numpy(),
)
Fixed Tensor Inputs
When verifying a loss function, use fixed tensors to ensure deterministic output:
# Loading pre-computed tensors
policy_chosen_logps = torch.load("path/to/tensor.pt")
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.
- 3d ago First seen · 40 lines · 25 tokens per session scan A 2e0693864e75
model-verification-unit-tests is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 211 once invoked, about $0.0001 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 2026-09-03.
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