effect-foundations
Core Effect foundations and style for a coding agent. Use when starting an Effect task, choosing operators, or structuring a small pipeline.
Browse published AI skills maintained by mepuka. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
15Repository stars across indexed skills
75Repository forks
0Average repository stars per indexed skill
5Repository stars may repeat across skills from the same repository.
Core Effect foundations and style for a coding agent. Use when starting an Effect task, choosing operators, or structuring a small pipeline.
Effect patterns for SQL databases (Drizzle/Kysely/Prisma): services, transactions, retries, streaming, and observability.
Concurrency with Effect.all, forEach concurrency, Fiber lifecycle, race and timeouts. Use for parallelizing tasks safely.
Time with Clock/Duration, tracing spans, and structured logging. Use for time-based logic, deadlines, and observability.
Complete catalog of 130+ Effect-TS patterns from EffectPatterns repository. Use when looking for specific implementation patterns, best practices, or real-world examples. Complements other skills with concrete,...
Error modeling and recovery with TaggedError, catchTag(s), mapError, and retry schedules. Use when defining errors or adding resilience.
Define services, provide layers, compose dependencies, and switch live/test. Use for DI boundaries and app composition.
Resource safety with acquireRelease, Effect.scoped, and finalizers. Use when opening files, sockets, servers, or external handles.
Skill index and decision guide. Use to pick the right Effect Skill quickly and follow a minimal decision tree.
HTTP server, routing, JSON responses and dependencies via layers. Use when wiring endpoints and services.
Testing patterns with layers, mocks, and deterministic time. Use when preparing testable services and small smoke tests.
Queue and PubSub patterns, background fibers, and graceful shutdown. Use for decoupling producers/consumers.
Stream creation, transformation, sinks, batching, and resilience. Use when building data pipelines with concurrency and backpressure.
Config loading and Schema validation/transform. Use when defining configuration or validating inputs.
Value-based data structures (Data.struct, tuple, array) and high-performance collections (Chunk, HashSet). Use for safe comparisons and pipelines.
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.