Architecture knowledge reference covering API design, security architecture, cloud-native patterns, caching strategies, message queues, and data security. Use when designing system architecture, APIs, or cloud-native infrastructure.
npx skills add https://github.com/telagod/code-abyss --skill designing-architectures
> 判断先于执行:决定「是否做 / 选什么 / 如何取舍」(栈、方案、架构、权衡)前,先读领域判断内核 skills/_kernel/backend/SKILL.md——它管 judgment,本秘典管 execution;冲突时以内核判断为准。
| 意图 | 秘典 | 核心 |
|------|------|------|
| API 设计 | api-design | RESTful、GraphQL、gRPC、OpenAPI |
| 安全架构 | security-arch | 零信任、IAM、威胁建模、合规 |
| 云原生 | cloud-native | 容器、K8s、Serverless、Service Mesh |
| 消息队列 | message-queue | Kafka、RabbitMQ、事件驱动、CQRS |
| 缓存 | caching | Redis、CDN、一致性、穿透/雪崩 |
| 决策点 | 选项 A | 选项 B | 判据 |
|--------|--------|--------|------|
| 同步 vs 异步 | REST/gRPC 同步调用 | 消息队列异步 | 延迟敏感→同步;解耦/削峰→异步 |
| 单体 vs 微服务 | 单体(模块化) | 微服务 | 团队<5→单体;独立部署需求→微服务 |
| SQL vs NoSQL | RDBMS | MongoDB/DynamoDB | 强一致/关联→SQL;灵活 schema/高吞吐→NoSQL |
| 缓存策略 | Cache-Aside | Write-Through | 读多写少→Aside;写后即读→Through |
| API 风格 | REST | GraphQL | 资源型 CRUD→REST;复杂聚合/前端驱动→GraphQL |
| 事件架构 | 事件通知 | 事件溯源(ES) | 简单解耦→通知;审计/回溯→ES+CQRS |
SOLID: S单一职责 O开闭 L里氏替换 I接口隔离 D依赖倒置
分布式: CAP定理 | BASE最终一致 | 幂等设计
安全: 纵深防御 | 最小权限 | 零信任
扩展: 水平优先 | 无状态服务 | 数据分片
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take telagod/designing-architectures from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.