enterprise
16
总安装量
5
周安装量
#21178
全站排名
安装命令
npx skills add https://github.com/popup-studio-ai/bkit-claude-code --skill enterprise
Agent 安装分布
claude-code
5
opencode
4
gemini-cli
4
github-copilot
3
cursor
3
Skill 文档
Advanced (Enterprise) Skill
Actions
| Action | Description | Example |
|---|---|---|
init |
Project initialization (/init-enterprise feature) | /enterprise init my-platform |
guide |
Display development guide | /enterprise guide |
help |
MSA/Infrastructure help | /enterprise help |
init (Project Initialization)
- Create Turborepo monorepo structure
- apps/, packages/, services/, infra/ folder structure
- Create CLAUDE.md (Level: Enterprise specified)
- docs/ 5-category structure
- infra/terraform/, infra/k8s/ base templates
- Initialize .bkit-memory.json
guide (Development Guide)
- AI Native 10-Day development cycle
- Microservices architecture patterns
- Phase 1-9 full Pipeline (Enterprise version)
help (Infrastructure Help)
- Kubernetes basic concepts
- Terraform IaC patterns
- AWS EKS, RDS configuration guide
Target Audience
- Senior developers
- CTOs / Architects
- Large-scale system operators
Tech Stack
Frontend:
- Next.js 14+ (Turborepo monorepo)
- TypeScript
- Tailwind CSS
- TanStack Query
- Zustand
Backend:
- Python FastAPI (microservices)
- PostgreSQL (schema separation)
- Redis (cache, Pub/Sub)
- RabbitMQ / SQS (message queue)
Infrastructure:
- AWS (EKS, RDS, S3, CloudFront)
- Kubernetes (Kustomize)
- Terraform (IaC)
- ArgoCD (GitOps)
CI/CD:
- GitHub Actions
- Docker
Language Tier Guidance (v1.3.0)
Supported: All Tiers
Enterprise level handles complex requirements including legacy system integration.
| Tier | Usage | Guidance |
|---|---|---|
| Tier 1 | Primary services | New development, core features |
| Tier 2 | System/Cloud | Go (K8s), Rust (performance critical) |
| Tier 3 | Platform native | iOS (Swift), Android (Kotlin), legacy Java |
| Tier 4 | Legacy integration | Migration plan required |
Migration Path:
- PHP â TypeScript (Next.js API routes)
- Ruby â Python (FastAPI)
- Java â Kotlin or Go
Project Structure
project/
âââ apps/ # Frontend apps (Turborepo)
â âââ web/ # Main web app
â âââ admin/ # Admin
â âââ docs/ # Documentation site
â
âââ packages/ # Shared packages
â âââ ui/ # UI components
â âââ api-client/ # API client
â âââ config/ # Shared config
â
âââ services/ # Backend microservices
â âââ auth/ # Auth service
â âââ user/ # User service
â âââ {domain}/ # Domain-specific services
â âââ shared/ # Shared modules
â
âââ infra/ # Infrastructure code
â âââ terraform/
â â âââ modules/ # Reusable modules
â â âââ environments/ # Environment-specific config
â âââ k8s/
â âââ base/ # Common manifests
â âââ overlays/ # Environment-specific patches
â
âââ docs/ # PDCA documents
â âââ 00-requirement/
â âââ 01-development/ # Design documents (multiple)
â âââ 02-scenario/
â âââ 03-refactoring/
â âââ 04-operation/
â
âââ scripts/ # Utility scripts
âââ .github/workflows/ # CI/CD
âââ docker-compose.yml
âââ turbo.json
âââ pnpm-workspace.yaml
Clean Architecture (4-Layer)
âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ
â API Layer â
â - FastAPI routers â
â - Request/Response DTOs â
â - Auth/authz middleware â
âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤
â Application Layer â
â - Service classes â
â - Use Case implementation â
â - Transaction management â
âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤
â Domain Layer â
â - Entity classes (pure Python) â
â - Repository interfaces (ABC) â
â - Business rules â
âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ¤
â Infrastructure Layer â
â - Repository implementations (SQLAlchemy) â
â - External API clients â
â - Cache, messaging â
âââââââââââââââââââââââââââââââââââââââââââââââââââââââââââ
Dependency direction: Top â Bottom
Domain Layer depends on nothing
Core Patterns
Repository Pattern
# domain/repositories/user_repository.py (interface)
from abc import ABC, abstractmethod
class UserRepository(ABC):
@abstractmethod
async def find_by_id(self, id: str) -> User | None:
pass
@abstractmethod
async def save(self, user: User) -> User:
pass
# infrastructure/repositories/user_repository_impl.py (implementation)
class UserRepositoryImpl(UserRepository):
def __init__(self, db: AsyncSession):
self.db = db
async def find_by_id(self, id: str) -> User | None:
result = await self.db.execute(
select(UserModel).where(UserModel.id == id)
)
return result.scalar_one_or_none()
Inter-service Communication
# Synchronous (Internal API)
async def get_user_info(user_id: str) -> dict:
async with httpx.AsyncClient() as client:
response = await client.get(
f"{USER_SERVICE_URL}/internal/users/{user_id}",
headers={"X-Internal-Token": INTERNAL_TOKEN}
)
return response.json()
# Asynchronous (message queue)
await message_queue.publish(
topic="user.created",
message={"user_id": user.id, "email": user.email}
)
Terraform Module
# modules/eks/main.tf
resource "aws_eks_cluster" "this" {
name = "${var.environment}-${var.project_name}-eks"
role_arn = aws_iam_role.cluster.arn
version = var.kubernetes_version
vpc_config {
subnet_ids = var.subnet_ids
}
tags = merge(var.tags, {
Environment = var.environment
})
}
Kubernetes Deployment
# k8s/base/backend/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: user-service
spec:
replicas: 2
template:
spec:
containers:
- name: user-service
image: ${ECR_REGISTRY}/user-service:${TAG}
resources:
requests:
cpu: "100m"
memory: "256Mi"
limits:
cpu: "500m"
memory: "512Mi"
livenessProbe:
httpGet:
path: /health
port: 8000
Environment Configuration
| Environment | Infrastructure | Deployment Method |
|---|---|---|
| Local | Docker Compose | Manual |
| Staging | EKS | ArgoCD Auto Sync |
| Production | EKS | ArgoCD Manual Sync |
Security Rules
â
Allowed
- Retrieve secrets from Secrets Manager
- IAM role-based access
- VPC internal communication
- mTLS (inter-service)
â Prohibited
- Hardcoded secrets
- DB in public subnet
- Using root account
- Excessive IAM permissions
CI/CD Pipeline
Push to feature/*
â
GitHub Actions (CI)
- Lint
- Test
- Build Docker image
- Push to ECR
â
PR to staging
â
ArgoCD Auto Sync (Staging)
â
PR to main
â
ArgoCD Manual Sync (Production)
SoR Priority
1st Priority: Codebase
- scripts/init-db.sql (source of truth for DB schema)
- services/{service}/app/ (each service implementation)
2nd Priority: CLAUDE.md / Convention docs
- services/CLAUDE.md
- frontend/CLAUDE.md
- infra/CLAUDE.md
3rd Priority: docs/ design documents
- For understanding design intent
- If different from code, code is correct
AI Native Development
3 Core Principles
- Document-First Design: Write design docs BEFORE code
- Monorepo Context Control: All code in one repo for AI context
- PR-Based Collaboration: Every change through PR
10-Day Development Pattern
| Day | Focus | Output |
|---|---|---|
| 1 | Architecture | Market analysis + System architecture |
| 2-3 | Core | Auth, User + Business services |
| 4-5 | UX | PO feedback â Documentation â Implementation |
| 6-7 | QA | Zero Script QA + bug fixes |
| 8 | Infra | Terraform + GitOps |
| 9-10 | Production | Security review + Deployment |
Monorepo Benefits for AI
Mono-repo:
ââ project/
ââ frontend/ âââââââ
ââ services/ ââââââ⤠AI reads completely
ââ infra/ âââââââââ⤠Context unified
ââ packages/ âââââââ
â
AI understands full context
â
Single source of truth for types
â
Atomic commits across layers
â
Consistent patterns enforced
CLAUDE.md Hierarchy
project/
âââ CLAUDE.md # Project-wide context
âââ frontend/CLAUDE.md # Frontend conventions
âââ services/CLAUDE.md # Backend conventions
âââ infra/CLAUDE.md # Infra conventions
Rule: Area-specific CLAUDE.md overrides project-level rules
bkit Features for Enterprise Level (v1.5.1)
Output Style: bkit-enterprise (Recommended)
For CTO-level architecture perspectives, activate the enterprise style:
/output-style bkit-enterprise
This provides:
- Architecture tradeoff analysis tables (Option/Pros/Cons/Recommendation)
- Performance, security, and scalability perspectives for every decision
- Cost impact estimates for infrastructure changes
- Deployment strategy recommendations (Blue/Green, Canary, Rolling)
- SOLID principles and Clean Architecture compliance checks
Agent Teams (4 Teammates)
Enterprise projects support full Agent Teams for parallel PDCA execution:
| Role | Agents | PDCA Phases |
|---|---|---|
| architect | enterprise-expert, infra-architect | Design |
| developer | bkend-expert | Do, Act |
| qa | qa-monitor, gap-detector | Check |
| reviewer | code-analyzer, design-validator | Check, Act |
To enable:
- Set environment:
CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1 - Start team mode:
/pdca team {feature} - Monitor progress:
/pdca team status
Agent Memory (Auto-Active)
All bkit agents automatically remember project context across sessions.
Enterprise agents use project scope memory, ensuring architecture decisions
and infrastructure patterns persist across development sessions.