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://ekthesage.github.io/prospicio/

Codex / OpenCode

Tell the agent:
Fetch the skill file at https://ekthesage.github.io/prospicio/skill.md and follow the instructions.

Manual — download the skill file:

curl -O https://ekthesage.github.io/prospicio/skill.md

Or browse the SKILL.md file.

SKILL.md

---
name: prospicio
description: >
  Actuarial and risk modeling on a Rust core: reserving, distributions, aggregate loss, reinsurance and pricing. Use when writing Python code that uses the prospicio package.
license: MIT
compatibility: Requires Python >=3.9.
---

# prospicio

Actuarial and risk modeling on a Rust core: reserving, distributions, aggregate loss, reinsurance and pricing.

## Installation

```bash
pip install prospicio
```

## API overview

### Distributions

Parametric, discretized and sampled representations.

- `distributions.Lognormal`
- `distributions.Gamma`
- `distributions.Tweedie`
- `distributions.Weibull`
- `distributions.Loglogistic`
- `distributions.Mixture`
- `distributions.Custom`
- `distributions.to_json`
- `distributions.from_json`
- `distributions.Grid`
- `distributions.DiscretizationReport`
- `distributions.Sampled`

### Large-loss severities

The Pareto family for layer and treaty pricing, with maximum likelihood fits.

- `distributions.Pareto`
- `distributions.PiecewisePareto`
- `distributions.LogAffinePareto`
- `distributions.GeneralizedPareto`
- `distributions.local_pareto_to_piecewise`

### Claim counts

Frequency distributions for frequency-severity aggregation.

- `distributions.Poisson`
- `distributions.NegativeBinomial`
- `distributions.Binomial`
- `distributions.claim_count`

### Predictive distributions

The joint result every model returns.

- `distributions.PredictiveDistribution`

### Aggregate loss

Compound distributions on a grid, and simulated years of losses.

- `aggregate.panjer`
- `aggregate.fft`
- `aggregate.CompoundReport`
- `aggregate.simulate_events`
- `aggregate.EventSet`

### Reinsurance

Excess-of-loss layers and towers, applied to simulated losses or exactly on the grid.

- `reinsurance.Layer`
- `reinsurance.Tower`
- `reinsurance.TowerGrids`

### Models

Terms and design matrices, GLMs and GAMs, metrics, resampling and MCMC diagnostics.

- `models.Terms`
- `models.Coding`
- `models.Design`
- `models.Glm`
- `models.GlmFit`
- `models.BayesGlm`
- `models.BayesGlmFit`
- `models.ElasticNet`
- `models.ElasticNetFit`
- `models.CvPath`
- `models.Gam`
- `models.GamFit`
- `models.deviance`
- `models.gini`
- `models.lift`
- `models.crps`
- `models.log_score`
- `models.pit`
- `models.pit_from_draws`
- `models.pit_histogram`
- `models.ks_uniform`
- `models.k_fold`
- `models.group_k_fold`
- `models.time_ordered`
- `models.mcmc_diagnostics`
- `models.elpd_loo`
- `models.stacking_weights`
- `models.pseudo_bma_weights`
- `models.BayesStacking`
- `models.HierarchicalStacking`
- `models.StackingFit`
- `models.elpd_waic`
- `models.lppd`
- `models.Elpd`
- `models.deviance_score`
- `models.cross_validate`
- `models.grid_search`
- `models.random_search`
- `models.log_uniform`
- `models.SearchResult`
- `models.compare`
- `models.Comparison`
- `models.actual_vs_expected`
- `models.simulate_from_means`

### Gradient boosting

LightGBM and XGBoost behind the model protocol, with joint predictive draws.

- `boosting.Booster`
- `boosting.BoosterFit`

### Pricing

The collective model, layer rating, reinsurance tower matching, and risk-loaded prices from simulated losses.

- `pricing.CollectiveModel`
- `pricing.ilf`
- `pricing.loss_elimination_ratio`
- `pricing.pareto_extrapolation`
- `pricing.alpha_between_layers`
- `pricing.alpha_between_frequency_and_layer`
- `pricing.alpha_between_frequencies`
- `pricing.match_tower`
- `pricing.fit_pml_curve`
- `pricing.fit_references`
- `pricing.TowerModel`
- `pricing.price`
- `pricing.price_portfolio`
- `pricing.Price`
- `pricing.PortfolioPrice`
- `pricing.Mbbefd`
- `pricing.TabulatedCurve`
- `pricing.RiskProfile`
- `pricing.severity_exposure_curve`

### Reserving

Loss triangles, the chain ladder with tail factors, Mack's model with its one-year view, the expected-loss methods, the ODP bootstrap and Clark's growth curves.

- `reserving.Triangle`
- `reserving.ChainLadder`
- `reserving.ChainLadderFit`
- `reserving.TailConstant`
- `reserving.TailCurve`
- `reserving.TailBondy`
- `reserving.TailLogLinear`
- `reserving.Mack`
- `reserving.MackFit`
- `reserving.ClaimsDevelopmentResult`
- `reserving.ExpectedLoss`
- `reserving.BornhuetterFerguson`
- `reserving.Benktander`
- `reserving.CapeCod`
- `reserving.ExpectedLossFit`
- `reserving.CapeCodFit`
- `reserving.OdpBootstrap`
- `reserving.OdpBootstrapFit`
- `reserving.MackBootstrap`
- `reserving.MackBootstrapFit`
- `reserving.OneYearFit`
- `reserving.ClarkLdf`
- `reserving.ClarkCapeCod`
- `reserving.ClarkFit`

### Risk measures

Distortion risk measures, and capital allocation to components.

- `risk.Distortion`
- `risk.allocate`
- `risk.capital`
- `risk.entropic`
- `risk.esscher`
- `risk.marginal_expected_shortfall`
- `risk.covar`
- `risk.esscher_allocation`
- `risk.Allocation`

### Dependence

Copulas, and reordering existing draws to a target correlation.

- `risk.GaussianCopula`
- `risk.StudentTCopula`
- `risk.ArchimedeanCopula`
- `risk.simulate`
- `risk.iman_conover`

### Extreme value tails

Generalized Pareto tails over a threshold, beyond the draws.

- `risk.Gpd`
- `risk.PotTail`
- `risk.mean_excess`
- `risk.hill`

## Resources

- [Full documentation](https://ekthesage.github.io/prospicio/)
- [llms.txt](llms.txt) — Indexed API reference for LLMs
- [llms-full.txt](llms-full.txt) — Comprehensive documentation for LLMs
- [Source code](https://github.com/EKtheSage/prospicio)