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/trl-setupWrote 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/trl-setup)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/trl-setup"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/trl-setup.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 | $0.00034 | $0.00304 |
| Opus 5 | $0.00017 | $0.00152 |
| Sonnet 5 | $0.00007 | $0.00061 |
| Haiku 4.5 | $0.00003 | $0.00030 |
Grade A, and why
trl-setup 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
TRL Setup
Installation
pip install torch transformers trl accelerate peft datasets --break-system-packages
Compatibility Notes
- TRL imports from
trl.trainer.utils:DPODataCollatorWithPadding,pad_to_length, etc. - Some imports may change across TRL versions (e.g.,
trl_sanitze_kwargs_for_taggingvstrl_sanitize_kwargs_for_tagging) - If running in externally-managed Python (Debian), use
--break-system-packagesflag
Common Import Errors
trl_sanitze_kwargs_for_taggingnot found → check trl version, may be renamed or removed- Missing
CPOTrainer→ upgrade trl:pip install --upgrade trl
Running Tests from Project Root
cd /root/SimPO && python -m pytest unit_test/unit_test_1.py -v
# or
cd /root/SimPO && python unit_test/unit_test_1.py
Key TRL Trainer Utilities
DPODataCollatorWithPadding: handles padding for preference pairspad_to_length: pads tensor to specified lengthdisable_dropout_in_model: disables dropout during eval
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 · 33 lines · 34 tokens per session scan A 407259810013
trl-setup is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 304 once invoked, about $0.0002 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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