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.
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109 published bundles ready to inspect and install
Overfitting Detection For RL
Detect when RL training narrows capability (great on trained tasks, worse on everything else)
Domain Transfer Measurement
Quantify how much RL training on coding transfers to (say) data analysis or writing
Transfer Eval Design
Build evals that test whether RL training on task A improved performance on related task B
Risk Tier Classification
Classify agent skills by risk level (read-only vs. write vs. financial vs. external-facing) and apply appropriate controls
Audit Trail For RL Decisions
Log every decision an RL agent makes in production with sufficient context for post-hoc review
Deployment Gating Pipeline
Build eval-gated deployment pipelines where RL-trained models must pass benchmarks before production
Data Exfiltration Prevention
Monitor and prevent agents from leaking sensitive data through tool calls
Skill Security Audit
Static and dynamic analysis of agent skill code for security vulnerabilities
Deceptive Alignment Detection
Test whether agents behave differently when they believe they're being evaluated vs. not
RL Alignment Auditing
Verify that the policy optimizes for the intended objective, not a proxy
Action Space Sandboxing
Restrict agent actions to prevent irreversible or harmful operations
Safe Exploration Constraints
Define and enforce hard constraints on what agents can do during training rollouts