Skills

A skill is a package of structured files that teaches an AI coding agent how to work with a specific tool or framework. The skill below was generated by Great Docs from this project’s documentation. Install it in your agent and it will be able to run commands, edit configuration, write content, and troubleshoot problems without step-by-step guidance from you.

Any agent — install with npx:

npx skills add https://sim-tools.github.io/sim-tools/

Codex / OpenCode

Tell the agent:
Fetch the skill file at https://sim-tools.github.io/sim-tools/skill.md and follow the instructions.

Manual — download the skill file:

curl -O https://sim-tools.github.io/sim-tools/skill.md

Or browse the SKILL.md file.

SKILL.md

---
name: sim-tools
description: >
  Simulation Tools for Education and Practice. Use when writing Python code that uses the sim_tools package.
license: MIT
compatibility: Requires Python >=3.10.
---

# sim_tools

Simulation Tools for Education and Practice

## Installation

```bash
pip install sim-tools
```

## API overview

### Classes

Main classes provided by the package

- `distributions.Bernoulli`
- `distributions.Beta`
- `distributions.CombinationDistribution`
- `distributions.DiscreteEmpirical`
- `distributions.DistributionRegistry`
- `distributions.Erlang`
- `distributions.ErlangK`
- `distributions.Exponential`
- `distributions.FixedDistribution`
- `distributions.Gamma`
- `distributions.GroupedContinuousEmpirical`
- `distributions.Hyperexponential`
- `distributions.Lognormal`
- `distributions.Normal`
- `distributions.PearsonV`
- `distributions.PearsonVI`
- `distributions.Poisson`
- `distributions.RawContinuousEmpirical`
- `distributions.RawDiscreteEmpirical`
- `distributions.Triangular`
- `distributions.TruncatedDistribution`
- `distributions.Uniform`
- `distributions.Weibull`
- `output_analysis.OnlineStatistics`
- `output_analysis.ReplicationTabulizer`
- `output_analysis.ReplicationsAlgorithm`
- `time_dependent.DistributionRegistry`
- `time_dependent.NSPPThinning`

### Abstract Classes

Abstract base classes

- `trace.Traceable`

### Protocols

Protocol / structural-typing interfaces

- `distributions.Distribution`
- `output_analysis.AlgorithmObserver`
- `output_analysis.ReplicationObserver`
- `output_analysis.ReplicationsAlgorithmModelAdapter`

### Functions

Utility functions

- `datasets.load_banks_et_al_nspp`
- `distributions.is_integer`
- `distributions.is_non_negative`
- `distributions.is_numeric`
- `distributions.is_ordered_pair`
- `distributions.is_ordered_triplet`
- `distributions.is_positive`
- `distributions.is_positive_array`
- `distributions.is_probability`
- `distributions.is_probability_vector`
- `distributions.spawn_seeds`
- `distributions.validate`
- `output_analysis.confidence_interval_method`
- `output_analysis.plotly_confidence_interval_method`
- `time_dependent.nspp_plot`
- `time_dependent.nspp_simulation`

### Constants

Module-level constants and data

- `datasets.FILE_NAME_NSPP_1`
- `datasets.PATH_NSPP_1`
- `distributions.T`
- `output_analysis.ALG_INTERFACE_ERROR`
- `output_analysis.OBSERVER_INTERFACE_ERROR`
- `trace.CONFIG_ERROR`
- `trace.DEFAULT_DEBUG`

### Other

Additional exports

- `ovs`

## Resources

- [Full documentation](https://sim-tools.github.io/sim-tools/)
- [llms.txt](llms.txt) — Indexed API reference for LLMs
- [llms-full.txt](llms-full.txt) — Comprehensive documentation for LLMs