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 cxcscmu/SkillLearnBench --skill nlp-environment-managementgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/nlp-environment-management)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/nlp-environment-management"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/nlp-environment-management.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00269 |
| Opus 5 | $0.00013 | $0.00134 |
| Sonnet 5 | $0.00005 | $0.00054 |
| Haiku 4.5 | $0.00003 | $0.00027 |
Grade A, and why
nlp-environment-management scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
NLP Environment Management
Setting up an environment for NLP research requires specific versions of deep learning libraries and often custom local modules.
Installation via Conda
If an environment.yml is provided:
# Update existing environment
conda env update -n base --file environment.yml
Or create a new one:
conda env create -f environment.yml
Troubleshooting Common Conflicts
-
Flash Attention: Requires
flash-attnand often specific CUDA versions. Install using:pip install flash-attn --no-build-isolation -
Transformers/TRL Versions: Ensure
transformersandtrlversions match the codebase's expectations. -
Local Modules: If a project uses local modules, ensure they are in the
PYTHONPATH:export PYTHONPATH=$PYTHONPATH:$(pwd)
Logging Environment Info
Always log the environment for reproducibility:
python -VV > python_info.txt
python -m pip freeze >> python_info.txt
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 · 46 lines · 25 tokens per session scan A 0bff180a2982
nlp-environment-management is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 2mo ago), licensed MIT. It adds 25 tokens to every session and 269 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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