train-with-environments
Helps configure and troubleshoot RL training runs using prime-rl and native v1 tasksets.
Install
mkdir -p .claude/skills/train-with-environments && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/4067" && unzip -o skill.zip -d .claude/skills/train-with-environments && rm skill.zipInstalls to .claude/skills/train-with-environments
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Configure and diagnose prime-rl training with native verifiers.v1 tasksets, harnesses, runtimes, and trace branches. Use for RL experiment setup, environment-server configuration, group sizing, difficulty filtering, renderer selection, periodic eval, or rollout/training failure diagnosis.Key capabilities
- →Configure `prime-rl` training with native verifiers.v1 tasksets
- →Diagnose rollout failures separately from training instability
- →Set up difficulty filtering for training environments
- →Configure renderers and handle trace branches
- →Manage multi-environment training with sampling ratios
How it works
This skill configures `prime-rl` to train models using native v1 tasksets, allowing for detailed control over environment shape, group sizing, and difficulty filtering.
Inputs & outputs
When to use train-with-environments
- →Configure reinforcement learning training runs
- →Diagnose rollout and training failures
- →Tune hyperparameters for RL agents
About train-with-environments
Assists developers in setting up training loops, tuning hyperparameters, and diagnosing instability in RL experiments. It provides a standardized way to integrate native tasksets into training workflows.
Train models with verifiers environments using hosted RL or prime-rl. Use when asked to configure RL runs, tune key hyperparameters, diagnose instability, set up difficulty filtering and oversampling, or create practical train and eval loops for new environments.
When not to use it
- →When the taskset has not been validated for correct prompt/tool/user traces and reward
- →When the hosted training's public CLI schema does not support native v1 taskset/harness fields
- →When the project does not use `prime-rl` for reinforcement learning
Limitations
- →The skill requires `prime-rl` to be installed and configured.
- →The skill assumes the use of `vf.Trace` over the v1 environment-server protocol.
- →The skill does not support native v1 hosted commands until the public CLI schema changes.
How it compares
This skill provides a structured approach to integrating and diagnosing v1 tasksets within `prime-rl`, unlike a generic RL setup that might not differentiate between rollout and training instability.
Compared to similar skills
train-with-environments side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| train-with-environments (this skill) | 1 | 26d | Review | Advanced |
| senior-data-scientist | 9 | 7mo | Review | Advanced |
| llama-cpp | 21 | 8mo | Review | Intermediate |
| langchain | 26 | 8mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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