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Data & AI AI Skills

Discover Data & AI skills, compare source repositories and read the instructions for each workflow.

Categories: Data & AI

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GitHub

ddd

mgreenly

Domain-Driven Design (DDD) skill for the ikigai project

GitHub

harness

mgreenly

Automated quality check loops with escalation and fix sub-agents

GitHub

lcov

mgreenly

LCOV tooling - finding gaps, parsing coverage files, marker syntax

GitHub

jj

mgreenly

Jujutsu (jj) skill for the ikigai project

GitHub

meta

mgreenly

Meta - Agent System infrastructure for the ikigai project

GitHub

route

kevinreber

Create Remix routes with loaders and actions. Use when creating new pages, implementing route loaders/actions, handling navigation, or when the user mentions route, page, loader, action, or navigation.

GitHub

testing

kevinreber

Write unit tests with Vitest and E2E tests with Playwright. Use when creating tests, writing test cases, setting up test fixtures, or when the user mentions test, testing, vitest, playwright, or coverage.

GitHub

effect-ts-patterns

nuclom

Patterns and best practices for Effect-TS in the Nuclom codebase. Use when writing API routes, services, or any code using Effect-TS. Covers Effect.gen, error handling, services, layers, and common pitfalls.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.

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