类型
类型检查与类型生成
background-jobs
dadbodgeoff
Implement robust background job processing with dead letter queues, retries, and state machines. Use when building async workflows, scheduled tasks, or any work that shouldn't block the request/response cycle.
graceful-degradation
dadbodgeoff
Build resilient systems that degrade gracefully under failure. Implement fallbacks, feature flags, and partial responses when dependencies fail.
dead-letter-queue
dadbodgeoff
Store failed jobs for replay or manual inspection. Track failure patterns, enable manual intervention, and prevent data loss from processing errors.
terraform-skill
antonbabenko
Use when working with Terraform or OpenTofu - creating modules, writing tests (native test framework, Terratest), setting up CI/CD pipelines, reviewing configurations, choosing between testing approaches, debugging state issues, implementing security scanning (trivy, checkov), or making infrastructure-as-code architecture decisions
Jira Issues Skill
NeverSight
A crawler script to fetch skill data from network, automatically executed daily via GitHub Actions.
axe-ios-simulator
NeverSight
iOS Simulator automation using AXe CLI for touch gestures, text input, hardware buttons, screenshots, video recording, and accessibility inspection. Use when automating iOS Simulator interactions, writing UI tests, capturing screenshots/video, or inspecting accessibility elements. Triggers on iOS Simulator automation, AXe CLI usage, simulator tap/swipe/gesture commands, or accessibility testing tasks.
axe-ios-simulator
NeverSight
iOS Simulator automation using AXe CLI for touch gestures, text input, hardware buttons, screenshots, video recording, and accessibility inspection. Use when automating iOS Simulator interactions, writing UI tests, capturing screenshots/video, or inspecting accessibility elements. Triggers on iOS Simulator automation, AXe CLI usage, simulator tap/swipe/gesture commands, or accessibility testing tasks.
atlas-best-practices
NeverSight
Patterns for Atlas database schema management covering HCL/SQL schema definitions, versioned and declarative migrations, linting analyzers, testing, and project configuration. Use when working with atlas.hcl, .hcl schema files, Atlas CLI commands, or database migrations.
figure-legend-gen
aipoch
Generate standardized figure legends for scientific charts and graphs.
typescript-testing
shinpr
Frontend testing rules with Vitest, React Testing Library, and MSW. Includes coverage requirements, test design principles, and quality criteria. Use when writing frontend tests or reviewing test quality.
vuetify0
vuetifyjs
Build with @vuetify/v0 headless composables and components for Vue 3. Use when creating selection state (single, multi, grouped, stepped), form validation, tab/dialog/popover UI, provide/inject context, registries, virtual scrolling, pagination, keyboard shortcuts, resize observers, theming, breakpoints, or SSR-safe browser detection. Triggers on v0, vuetify0, headless components, or WAI-ARIA patterns.
tanstack-vue-store-skilld
harlan-zw
"ALWAYS use when writing code importing \"@tanstack/vue-store\". Consult for debugging, best practices, or modifying @tanstack/vue-store, tanstack/vue-store, tanstack vue-store, tanstack vue store, store."
agent-designer
LeoYeAI
"Agent Designer - Multi-Agent System Architecture"
clinical-decision-support-documents
jaechang-hits
"Guidelines for generating clinical decision support (CDS) documents: patient cohort analyses (biomarker-stratified outcomes) and treatment recommendation reports (GRADE-graded evidence). Covers document structure, executive summary design, evidence grading (GRADE 1A–2C), statistical reporting (HR, CI, survival), and biomarker integration. Use when creating pharmaceutical research documents, clinical guidelines, or regulatory submissions."
python-best-practices
aiskillstore
Provides Python patterns for type-first development with dataclasses, discriminated unions, NewType, and Protocol. Must use when reading or writing Python files.
tamagui-best-practices
aiskillstore
Provides Tamagui patterns for config v4, compiler optimization, styled context, and cross-platform styling. Must use when working with Tamagui projects (tamagui.config.ts, @tamagui imports).
Jira SAFe (Scaled Agile Framework) Skill
aiskillstore
Security-audited skills for Claude, Codex & Claude Code. One-click install, quality verified.
react-best-practices
aiskillstore
Provides React patterns for hooks, effects, refs, and component design. Covers escape hatches, anti-patterns, and correct effect usage. Must use when reading or writing React components (.tsx, .jsx files with React imports).
zig-best-practices
aiskillstore
Provides Zig patterns for type-first development with tagged unions, explicit error sets, comptime validation, and memory management. Must use when reading or writing Zig files.
mcp-builder
aiskillstore
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
canton-network-repos
aiskillstore
Canton Network open-source ecosystem guide covering DAML SDK, Canton runtime, and Splice applications. Use when working with Canton Network, DAML smart contracts, or building decentralized applications.
frontend-dev-guidelines
aiskillstore
Next.js 15 애플리케이션을 위한 프론트엔드 개발 가이드라인. React 19, TypeScript, Shadcn/ui, Tailwind CSS를 사용한 모던 패턴. Server Components, Client Components, App Router, 파일 구조, Shadcn/ui 컴포넌트, 성능 최적화, TypeScript 모범 사례 포함. 컴포넌트, 페이지, 기능 생성, 데이터 페칭, 스타일링, 라우팅, 프론트엔드 코드 작업 시 사용.
typescript-best-practices
aiskillstore
Provides TypeScript patterns for type-first development, making illegal states unrepresentable, exhaustive handling, and runtime validation. Must use when reading or writing TypeScript/JavaScript files.
nnunet-segmentation
jaechang-hits
"Train and deploy automated medical image segmentation models using nnU-Net's self-configuring framework that auto-selects optimal architecture, preprocessing, and training for any modality. Supports CT, MRI, microscopy, and ultrasound with 2D, 3D full-res, 3D low-res, and cascade configurations. Pipeline: convert dataset → plan and preprocess → train (5-fold cross-validation) → find best configuration → predict → ensemble. Use when classical segmentation fails and annotated training data is available."