gaspatchio-model-review

Reviews gaspatchio actuarial models for code quality and ASOP 56 methodology compliance.

4|Updated Jul 4, 2026
One-click install
npx skills add https://github.com/gaspatchio/gaspatchio --skill gaspatchio-model-review-gaspatchio
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gaspatchio-model-review
Source: https://github.com/gaspatchio/gaspatchio/tree/main/skills/gaspatchio-model-review
Command: npx skills add https://github.com/gaspatchio/gaspatchio --skill gaspatchio-model-review-gaspatchio

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Actuarial models built with gaspatchio can contain hidden anti-patterns (like map_elements or Python loops) that destroy performance, and methodology errors (wrong decrement timing, misaligned lookup tables) that silently produce wrong numbers. This Skill provides a structured, evidence-based review process that catches both code quality and actuarial methodology issues before models reach production. ## Core Features & Use Cases - Two-layer review: Layer 1 checks gaspatchio code quality against a catalog of 10 anti-patterns; Layer 2 applies an ASOP 56-informed checklist covering correctness, assumption integrity, change impact, and documentation. - Severity-gated completion: Issues are classified as Critical, Important, or Minor, and the review is blocked until all Critical and Important findings are resolved with file:line references. - Executable verification: Runs the model via gspio run-single-policy and inspects parquet output with gspio describe --json rather than trusting stdout or claims that results "look reasonable". - Use Case: After building a term life projection model, invoke this Skill to verify formulas, lookup table mappings, and decrement ordering, then receive a structured review report with actionable fixes before merging to production. ## Quick Start Review my gaspatchio model in model.py for code quality and actuarial methodology issues, then give me a severity-classified report.

Frequently Asked Questions about gaspatchio-model-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review a gaspatchio actuarial model before production?

Run the model with `uv run gspio run-single-policy model.py data.parquet 1 --output-file result.parquet`, inspect output with `gspio describe --json`, then check code against the anti-pattern catalog and the ASOP 56 checklist. All Critical and Important issues must be fixed before the review passes.

What are common gaspatchio anti-patterns to avoid?

The most severe are `map_elements`/`apply` calls and Python for-loops over data rows, which bypass Polars vectorization and cause major slowdowns. Other issues include inline Polars joins instead of `Table.lookup()`, hardcoded assumption values, and raw list operations instead of `.projection.*` accessors.

What is ASOP 56 and how does it apply to model review?

ASOP 56 is the Actuarial Standard of Practice on Modeling, effective October 2020, governing how US actuaries design, use, and review models. The checklist translates it into concrete checks for formula correctness, assumption sourcing, change impact analysis, and documentation sufficiency.

Why should I use gspio describe instead of reading stdout output?

Parsing stdout is fragile and loses type information. Writing results to parquet with `--output-file` and inspecting with `gspio describe --json` preserves column types, reveals null/NaN values, and provides verifiable evidence for the review.

When should model review happen in the development workflow?

Review after completing model building, when reviewing changes to an existing model, before merging to production, or before regulatory sign-off. It does not require any other skill to run first and can be invoked at any point in the model lifecycle.

What severity levels block a model review from completing?

Critical issues (wrong numbers or runtime crashes) and Important issues (methodology deviations, maintainability risks) both block completion. Minor issues like missing section headers or documentation gaps are logged but do not block the review.