Agent skill · data analytics · sickn33
fp-data-transforms
Everyday data transformations using functional patterns - arrays, objects, grouping, aggregation, and null-safe access
Why this skill is useful
Provides domain-specific knowledge on functional programming patterns for data transformations in TypeScript that the AI wouldn't reliably generate on its own.
What it needs
About 5k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Practical Data Transformations This skill covers the data transformations you do every day: working with arrays, reshaping objects, normalizing API responses, grouping data, and safely accessing nested values. Each section shows the imperative approach first, then the functional equivalent, with honest assessments of when each approach shines. Detailed Guide Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely. When to Use You need to transform arrays, objects, grouped data, or nested values in TypeScript. The task involves reshaping API responses, null-safe access, aggregation, or normalization. You want practical functional patterns for everyday data work instead of low-level loops. --- 6. Real-World Examples Example 1: Transform API Response to UI-Ready Data Example 2: Merge User Settings with Defaults Example 3: Group Orders by Customer with Totals Example 4: Safely Access Deeply Nested Config --- 7. When to Use What Use Native Methods When: Simple transformations: .map(), .filter(), .reduce() are perfectly good No composition needed: You're doing a one-off transformation Team familiarity: Everyone knows native methods Optional chaining suffices: obj?.prop?.value ?? default handles your null-safety needs Use fp-ts When: Chaining operations that might fail: Multiple steps where each can return nothing Composing transformations: Building reusable transformation pipelines Type-safe error handling: You want the compiler to track potential failures Complex data pipelines: Many steps that benefit from explicit composition Use Custom Utilities When: Domain-specific operations: groupBy, countBy, sumBy for your data Repeated patterns: You find yourself writing the same transformation many times Team conventions: Establishing consistent patterns acro …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/fp-data-transforms--f002b5