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/ibnr/

Codex / OpenCode

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

Manual — download the skill file:

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

Or browse the SKILL.md file.

SKILL.md

---
name: ibnr
description: >
  Gallery-centric probabilistic loss reserving: Bayesian MCMC and neural network methods with mandatory evaluation, over a long-format triangle data layer. Use when writing Python code that uses the ibnr package.
license: MPL-2.0
compatibility: Requires Python >=3.11,<3.13.
---

# ibnr

Gallery-centric probabilistic loss reserving: Bayesian MCMC and neural network methods with mandatory evaluation, over a long-format triangle data layer.

## Installation

```bash
pip install ibnr
```

## API overview

### Triangle

The long-format triangle data layer - a tidy table of origin_period / dev_lag / eval_date cells over an ibis expression (duckdb or polars backend), carrying grain, cumulative/incremental, and units metadata.


- `Triangle`: A reserving triangle over an ibis table expression
- `TriangleMeta`: Triangle-level metadata. Carried through every transformation

### Triangle transforms & interop

Cumulative/incremental conversion, grain changes, as_of() backtest slicing, and lossless round-trips with chainladder-python and bermuda.


- `Triangle.from_long`
- `Triangle.from_chainladder`
- `Triangle.to_chainladder`
- `Triangle.from_bermuda`
- `Triangle.to_bermuda`
- `Triangle.to_cumulative`
- `Triangle.to_incremental`
- `Triangle.with_dev_grain`
- `Triangle.with_origin_grain`
- `Triangle.as_of`
- `Triangle.latest_diagonal`
- `Triangle.filter`
- `Triangle.select_fields`
- `Triangle.to_wide`
- `Triangle.to_pandas`
- `Triangle.to_polars`
- `Triangle.execute`
- `Triangle.count`
- `Triangle.with_expr`
- `Triangle.validate`

### Gallery

Discover, retrieve, and fit reserving models through the GalleryEntry contract. Every entry - Bayesian, neural, or statistical - produces a PredictiveDistribution.


- `gallery.list`
- `gallery.get`
- `gallery.fit`
- `gallery.GalleryEntry`

### Held-out evaluation

The four steps between a fitted entry and a leaderboard row: which cells a fit at as_of is scored on, one model's arrays at those cells, the cross-model intersection, and the board.


- `gallery.next_diagonal`
- `gallery.CohortForecast`
- `gallery.Absence`
- `gallery.align_panel`
- `gallery.leaderboard`
- `gallery.SCORE_DIRECTION`
- `gallery.stack`

### Chain ladder & reserve risk

The distribution-free chain ladder (Mack 1993) and its run-off MSEP, plus the one-year claims development result - the Solvency II reserve-risk view. cdr_methods() is the option surface: which model generates next year's diagonal, how the reserve is re-estimated once it exists, and what each route has actually been validated against.


- `kernels.MackFit`
- `kernels.fit_mack`
- `kernels.simulate_ultimates`
- `kernels.cdr_methods`
- `kernels.get_cdr_method`
- `kernels.one_year_cdr`
- `kernels.simulate_one_year_cdr`
- `kernels.rereserve`
- `kernels.cdr_risk_measures`
- `kernels.CDRResult`
- `kernels.MackDiagonal`
- `kernels.ODPBootstrapDiagonal`
- `gallery.GalleryDiagonal`

### Evaluation kernels

The unifying predictive-distribution output type and the cross-backend posterior-parity tooling.


- `kernels.PredictiveDistribution`
- `kernels.ParityReport`
- `kernels.compare_posteriors`

### Wire format

The single place an ibnr result becomes bytes - one Arrow IPC stream per artifact, uncompressed by default, plus a one-way JSON summary for callers that cannot take the draws.


- `kernels.to_arrow`
- `kernels.from_arrow`
- `kernels.peek_kind`
- `kernels.to_summary`
- `kernels.CODEC_VERSION`
- `kernels.CONTENT_TYPE_ARROW`
- `kernels.CONTENT_TYPE_JSON`
- `kernels.DEFAULT_QUANTILES`

### Retrospective harness

Parallel company x line backtesting with staged sampler escalation - the compute seam behind the study scripts.


- `kernels.harness.run_retro`
- `kernels.harness.run_task`
- `kernels.harness.RetroTask`
- `kernels.harness.SamplerSettings`
- `kernels.harness.ConvergenceGates`
- `kernels.harness.precompile`
- `kernels.harness.default_max_workers`

## Resources

- [Full documentation](https://ekthesage.github.io/ibnr/)
- [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/ibnr)