Read the source. Install what you trust.
Each skill bundle packages a reusable agent behavior — a prompt, supporting files, and evaluation criteria. Browse the public catalog, review the full source, then install a private copy you can edit and experiment with.
Browse bundles
108 published bundles ready to inspect and install
Workflow Audit
Map an enterprise workflow end-to-end: inputs, decisions, tools, outputs, success criteria
Model Versioning For RL
Track and switch between reference model, current policy, and reward model versions during training
VLLM For RL
Configure vLLM or similar engines for RL workloads (batched generation, multiple completions)
High Throughput Rollout Serving
Serve models at high throughput for RL rollout collection (not just user-facing latency)
Compute Budgeting For RL
Estimate and optimize GPU hours needed for RL training runs
Checkpoint Selection
Choose the best model checkpoint based on eval performance, not just training metrics
Training Stability Debugging
Diagnose and fix common RL training failures: reward collapse, mode collapse, KL explosion
Kl Divergence Management
Control how far the policy drifts from the reference model during training
Reward Model Training
Train reward models from human preference data, handle label noise and distribution shift
RL Hyperparameter Tuning
Tune learning rates, KL penalties, reward scaling, batch sizes for RL stability
Distributed RL Training
Shard training across multiple GPUs/nodes with proper gradient synchronization
Manage RL Rollouts
Orchestrate parallel agent rollouts across environments at scale