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GitHub 저장소

SWE-Skills-Bench

SWE-Skills-Bench에는 GeniusHTX에서 수집한 skills 9개가 있으며, 저장소 수준 직업 범위와 사이트 내 skill 상세 페이지를 제공합니다.

수집된 skills
9
Stars
59
업데이트
2026-04-14
Forks
11
직업 범위
직업 카테고리 2개 · 100% 분류됨
저장소 탐색

이 저장소의 skills

add-admin-api-endpoint
소프트웨어 개발자

Add a new endpoint or endpoints to Ghost's Admin API at `ghost/api/admin/**`.

2026-04-14
turborepo
소프트웨어 개발자

Turborepo monorepo build system guidance. Triggers on: turbo.json, task pipelines, dependsOn, caching, remote cache, the "turbo" CLI, --filter, --affected, CI optimization, environment variables, internal packages, monorepo structure/best practices, and boundaries. Use when user: configures tasks/workflows/pipelines, creates packages, sets up monorepo, shares code between apps, runs changed/affected packages, debugs cache, or has apps/packages directories.

2026-04-14
add-uint-support
소프트웨어 개발자

Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.

2026-03-23
analytics-events
소프트웨어 개발자

Add product analytics events to track user interactions in the Metabase frontend

2026-03-23
analyze-ci
소프트웨어 개발자

Analyze failed GitHub Action jobs for a pull request.

2026-03-23
clojure-write
소프트웨어 개발자

Guide Clojure and ClojureScript development using REPL-driven workflow, coding conventions, and best practices. Use when writing, developing, or refactoring Clojure/ClojureScript code.

2026-03-23
implementing-jsc-classes-zig
소프트웨어 개발자

Creates JavaScript classes using Bun's Zig bindings generator (.classes.ts). Use when implementing new JS APIs in Zig with JSC integration.

2026-03-23
llm-evaluation
데이터 과학자

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

2026-03-23
rag-implementation
소프트웨어 개발자

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

2026-03-23