nemo-rl-auto-research
An autonomous research agent for NeMo-RL and NeMo-gym that manages iterative experiment campaigns, tracks hypotheses via git branches, and logs results in a TSV ledger.
Is nemo-rl-auto-research safe to install?
Review the source first: our audit of nemo-rl-auto-research's source files found 2 shell commands, 0 external URLs, file reads and writes (high risk). Every command and URL listed appears verbatim in the skill's source. The skill executes shell commands, including training jobs that consume GPU resources, and performs file system operations such as creating git branches and writing logs.
How we audit skills: our security review methodology.
Who is this skill for?
Researchers and engineers conducting iterative NeMo-RL or NeMo-gym experiments who require automated experiment tracking, baseline management, and reproducible campaign execution.
What can you do with it?
- Running iterative NeMo-RL experiments against specific metrics like accuracy, reward, or latency.
- Managing long-running research campaigns with automated stop conditions.
- Launching reproducible baselines and iterations for hypothesis testing.
- Maintaining a research ledger using git and TSV logs.
How good is this skill?
Quality score: 5/10. The skill documentation is comprehensive, providing clear operational workflows, safety constraints, and specific instructions for experiment management.
What does the skill file contain?
# Auto Research Run iterative NeMo-RL experiments in this repository against the user's stated objective, such as accuracy, reward, throughput, latency, stability, or another recipe-specific metric, with git as the research ledger. Treat dependencies as ready, but choose the runtime deliberately. Use the recipe's authoritative metric as the source of truth. Keep changes small, reproducible, and simple. Preserve unrelated user work. **Safety:** This skill creates git branches, writes files to disk, and executes shell commands including training jobs that may consume GPU resources. Always con...
Frequently asked questions
What should I do if I do not provide explicit stopping conditions?
The agent runs a baseline plus up to three low-risk experiments, then summarizes the best result and asks for further instructions.
How does the agent handle experiment logging?
It creates an untracked TSV log and a per-experiment directory to store logs and metadata for every hypothesis.
Can this skill be used for code refactoring or bug fixes?
No. The skill is strictly for autonomous research and experiment campaigns.
How does the agent manage hardware resources?
It selects execution environments based on the task, using local GPUs, Kubernetes via launch-nemo-rl, or native Slurm launchers, while checking for Brev instance configurations.
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