---

name: sbi_simulator_prior_protocol description: Prepare simulator, prior, observation, and simulated pair contracts for simulation-based inference workflows.

SBI Simulator Prior Protocol

Use this skill when a recovery or implementation task needs to turn a prior and black-box simulator into auditable (theta, x) simulation pairs for neural SBI.

Do not use it to evaluate analytic likelihoods or to train the posterior estimator itself.

Inputs

• Prior sampler or bounds plus random seed.
• Simulator callable or simulator specification.
• Number of simulations.
• Optional observation x_o.

Outputs

• Two-dimensional theta and x arrays with matching row counts.
• Shape metadata and provenance.
• Failure count and finite-value status.

Workflow

1. Normalize scalar parameter and observation values to row-major two-dimensional arrays.
2. Sample parameters from the prior with a deterministic seed.
3. Run the simulator without requiring likelihood evaluation.
4. Check row alignment, finite values, and failure counts.
5. Return provenance for downstream training and recovery logs.

Validation

Run:

python scripts/sim_protocol.py --smoke
python tests/test_sim_protocol.py

Limitations

• This skill defines the data protocol only; it does not choose SNPE/SNLE/SNRE.
• Failed simulations must be reported, not silently hidden.