Agent skill

Enterprise

by ww-w-ai in ww-w-ai/bkit-claude-code

Enterprise-grade systems with microservices, Kubernetes, Terraform, and AI Native methodology.

Apache-2.0Auto-check: notesDevOps & Cloud

Install Enterprise

skills CLI
$ npx skills add ww-w-ai/bkit-claude-code --skill enterprise -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ww-w-ai/bkit-claude-code enterprise --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ww-w-ai/bkit-claude-code.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/enterprise .claude/skills/enterprise && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
enterprise
GitHub stars
601
Token cost
~3.5k tokens
SKILL.md length
479 words
Files
1
Skills in repo
44
Repo updated
First seen
Licence
Apache-2.0

At a glance

Enterprise-grade systems with microservices, Kubernetes, Terraform, and AI Native methodology.

  • Works in 6 steps: Create Turborepo monorepo structure → apps/, packages/, services/, infra/… → Create CLAUDE.md (Level: Enterprise… → …
  • (QUALITYGATEFAIL / ITERATIONEXHAUSTED / BUDGETEXCEEDED / PHASETIMEOUT)
  • SKILL.md covers Actions, Target Audience, Tech Stack and Project Structure, plus 10 more sections
  • Calls argocd and claude; needs INTERNAL_TOKEN

What it does

Enterprise is an agent skill from ww-w-ai/bkit-claude-code. Enterprise-grade systems with microservices, Kubernetes, Terraform, and AI Native methodology. For multi-feature initiatives spanning a release timeline, combine with /sprint master-plan (v2.1.13) to group features into a single 8-phase sprint container with shared scope/budget and 4 auto-pause triggers (QUALITYGATEFAIL / ITERATIONEXHAUSTED / BUDGETEXCEEDED / PHASETIMEOUT). Triggers: microservices, k8s, terraform, monorepo, AI native

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Container orchestration, Infrastructure as code and Microservices. It works with Kubernetes and Terraform. The repository describes itself as: bkit Vibecoding Kit - PDCA methodology + Claude Code mastery for AI-native development. The licence is Apache-2.0.

When your agent uses it

  • (QUALITYGATEFAIL / ITERATIONEXHAUSTED / BUDGETEXCEEDED / PHASETIMEOUT)
  • Tasks that involve Container orchestration
  • Tasks that involve Infrastructure as code

Example prompts

  • “/enterprise”

Requirements

  • Python 3
  • Docker
  • A credential in INTERNAL_TOKEN
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Glob, Grep, Bash, Task, WebSearch

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Create Turborepo monorepo structure
  2. apps/, packages/, services/, infra/ folder structure
  3. Create CLAUDE.md (Level: Enterprise specified)
  4. docs/ 5-category structure
  5. infra/terraform/, infra/k8s/ base templates
  6. Initialize the pipeline store — writeBkitMemory() in lib/pdca/status.js,

What it can do on your machine

Read from SKILL.md and the folder at commit 85b4913. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Glob
    • Grep
    • Bash
    • Task
    • WebSearch

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • argocd
    • claude

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • INTERNAL_TOKEN

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Enterprise loads about 3.5k tokens when it runs. Until then it costs about 113 tokens; SKILL.md has 479 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Write, Edit, Glob, Grep, Bash, Task, WebSearch

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ww-w-ai/bkit-claude-code at commit 85b4913, republished under its Apache-2.0 licence (© ww-w-ai). 479 words, ~3,524 tokens.

Download SKILL.mdSave it as .claude/skills/enterprise/SKILL.md (or your agent's skills folder).
name
enterprise
description
Enterprise-grade systems with microservices, Kubernetes, Terraform, and AI Native methodology. For multi-feature initiatives spanning a release timeline, combine with /sprint master-plan (v2.1.13) to group features into a single 8-phase sprint container with shared scope/budget and 4 auto-pause triggers (QUALITY_GATE_FAIL / ITERATION_EXHAUSTED / BUDGET_EXCEEDED / PHASE_TIMEOUT). Triggers: microservices, k8s, terraform, monorepo, AI native
allowed-tools
Read, Write, Edit, Glob, Grep, Bash, Task, WebSearch
classification
capability
classification-reason
Specialized domain knowledge with limited model overlap
deprecation-risk
low
effort
high
argument-hint
[init|guide|help]
agents.default
bkit:enterprise-expert
agents.infra
bkit:infra-architect
agents.architecture
bkit:enterprise-expert
agents.security
bkit:security-architect
agents.team
bkit:cto-lead

Advanced (Enterprise) Skill

Actions

ActionDescriptionExample
initProject initialization (/init-enterprise feature)/enterprise init my-platform
guideDisplay development guide/enterprise guide
helpMSA/Infrastructure help/enterprise help
init (Project Initialization)
  1. Create Turborepo monorepo structure
  2. apps/, packages/, services/, infra/ folder structure
  3. Create CLAUDE.md (Level: Enterprise specified)
  4. docs/ 5-category structure
  5. infra/terraform/, infra/k8s/ base templates
  6. Initialize the pipeline store — writeBkitMemory() in lib/pdca/status.js, which writes .bkit/state/memory.json (the migrated path of .bkit-memory.json). This holds the project level and the 9-phase pipelineStatus, read by the pipeline Stop hooks for phases 5, 6 and 9. It is NOT where the PDCA phase lives — that is .bkit/state/pdca-status.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
- Sentry Browser SDK (@sentry/nextjs) — Error tracking + Session Replay

Backend:
- Python FastAPI (microservices) — default
- PostgreSQL (schema separation)
- Redis (cache, Pub/Sub)
- RabbitMQ / SQS (message queue)
- Sentry Server SDK (sentry-sdk[fastapi]) — Error tracking + APM

Infrastructure:
- AWS (EKS, RDS, S3, CloudFront)
- Kubernetes (Kustomize)
- Terraform (IaC)
- ArgoCD (GitOps)
- ALB + NGINX Ingress Controller (L7 load balancing)
  - CORS: Ingress annotation으로 처리
    nginx.ingress.kubernetes.io/enable-cors: "true"
  - NLB(L4)는 gRPC/WebSocket 전용 서비스에만 사용

CI/CD:
- GitHub Actions
- Docker
- Semgrep (SAST) + Trivy (Container Scan)

Monitoring & Error Tracking:
- Sentry — Error tracking, grouping, regression detection
- Prometheus + Grafana — Metrics & dashboards
- Loki + Promtail — Log aggregation
- Tempo + OpenTelemetry — Distributed tracing
- Alertmanager → PagerDuty (critical) / Slack (warning)

Self-Healing Pipeline:
- Sentry Webhook → Self-Healing Agent trigger
- 4-Layer Living Context (Scenarios, Invariants, Impact, Incidents)
- Auto-fix (max 5 iterations) → Auto PR → Canary Deploy
- Auto-Rollback on error rate spike
Language Tier Guidance (v1.3.0)

Supported: All Tiers

Enterprise level handles complex requirements including legacy system integration.

TierUsageGuidance
Tier 1Primary servicesNew development, core features
Tier 2System/CloudGo (K8s), Rust (performance critical)
Tier 3Platform nativeiOS (Swift), Android (Kotlin), legacy Java
Tier 4Legacy integrationMigration plan required

Migration Path:

  • PHP → TypeScript (Next.js API routes)
  • Ruby → Python (FastAPI)
  • Java → Kotlin or Go

Project Structure

project/
├── apps/                        # Frontend apps (Turborepo)
│   ├── web/                    # Main web app
│   ├── admin/                  # Admin
│   └── docs/                   # Documentation site
│
├── packages/                    # Shared packages
│   ├── ui/                     # UI components
│   ├── api-client/             # API client
│   └── config/                 # Shared config
│
├── services/                    # Backend microservices
│   ├── auth/                   # Auth service
│   ├── user/                   # User service
│   ├── {domain}/               # Domain-specific services
│   └── shared/                 # Shared modules
│
├── infra/                       # Infrastructure code
│   ├── terraform/
│   │   ├── modules/            # Reusable modules
│   │   └── environments/       # Environment-specific config
│   └── k8s/
│       ├── base/               # Common manifests
│       └── overlays/           # Environment-specific patches
│
├── docs/                        # PDCA documents
│   ├── 00-requirement/
│   ├── 01-development/         # Design documents (multiple)
│   ├── 02-scenario/
│   ├── 03-refactoring/
│   └── 04-operation/
│
├── scripts/                     # Utility scripts
├── .github/workflows/           # CI/CD
├── docker-compose.yml
├── turbo.json
└── pnpm-workspace.yaml

Clean Architecture (4-Layer)

┌─────────────────────────────────────────────────────────┐
│                    API Layer                             │
│  - FastAPI routers                                       │
│  - Request/Response DTOs                                 │
│  - Auth/authz middleware                                 │
├─────────────────────────────────────────────────────────┤
│                  Application Layer                       │
│  - Service classes                                       │
│  - Use Case implementation                               │
│  - Transaction management                                │
├─────────────────────────────────────────────────────────┤
│                    Domain Layer                          │
│  - Entity classes (pure Python)                          │
│  - Repository interfaces (ABC)                           │
│  - Business rules                                        │
├─────────────────────────────────────────────────────────┤
│                 Infrastructure Layer                     │
│  - Repository implementations (SQLAlchemy)               │
│  - External API clients                                  │
│  - Cache, messaging                                      │
│  - Sentry SDK integration (error capture)                │
└─────────────────────────────────────────────────────────┘

Dependency direction: Top → Bottom
Domain Layer depends on nothing

Error Handling & Self-Healing Pipeline

Exception 발생 (Frontend/Backend)
  ↓
Sentry SDK 자동 캡처 (stack trace + breadcrumbs + user context)
  ↓
Sentry Alert Rule (new issue / regression / spike)
  ↓
Webhook → Self-Healing Agent trigger
  ↓
Living Context 4-Layer 로딩
  ├── Scenario Matrix: 테스트 시나리오
  ├── Invariants: 불변 조건 (critical = 수정 차단)
  ├── Impact Map: blast radius 계산
  └── Incident Memory: 과거 장애 교훈
  ↓
Claude Code Fix (max 5 iterations)
  ↓
4중 검증 (scenarios + invariants + impact + anti-patterns)
  ↓
Pass → Auto PR → Human Review → Canary Deploy (10%→25%→50%→100%)
Fail → Escalation → PagerDuty + Slack + Auto-Rollback
  ↓
Post-deploy: Sentry에서 issue resolved 확인 + error_rate 모니터링
Load Balancer Strategy
ALB + NGINX Ingress Controller (기본, 권장)
─────────────────────────────────────
- L7 로드밸런싱 (HTTP/HTTPS/gRPC)
- CORS: Ingress annotation으로 처리 (앱 코드 불필요)
- Path-based routing (/api/auth/*, /api/users/*)
- AWS Certificate Manager (ACM) TLS 연동
- WAF 연동 가능

NLB (특수 케이스만)
─────────────────────────────────────
- L4 로드밸런싱 (TCP/UDP)
- 극도의 저지연 필요 시 (< 1ms)
- WebSocket/gRPC 전용 서비스
- CORS 처리 불가 → 앱단에서 직접 처리 필요

Core Patterns

Repository Pattern
python
# 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
python
# 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
hcl
# 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
yaml
# 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

EnvironmentInfrastructureDeployment Method
LocalDocker ComposeManual
StagingEKSArgoCD Auto Sync
ProductionEKSArgoCD Manual Sync

Security Rules

✅ Allowed
- Retrieve secrets from Secrets Manager
- IAM role-based access
- VPC internal communication
- mTLS (inter-service)

❌ Prohibited
- Hardcoded secrets
- DB in public subnet
- Using root account
- Excessive IAM permissions

CI/CD Pipeline

Push to feature/*
    ↓
GitHub Actions (CI)
    - Lint
    - Test
    - Build Docker image
    - Push to ECR
    ↓
PR to staging
    ↓
ArgoCD Auto Sync (Staging)
    ↓
PR to main
    ↓
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
  1. Document-First Design: Write design docs BEFORE code
  2. Monorepo Context Control: All code in one repo for AI context
  3. PR-Based Collaboration: Every change through PR
Show full SKILL.md (186 more words)Show less
10-Day Development Pattern
DayFocusOutput
1ArchitectureMarket analysis + System architecture
2-3CoreAuth, User + Business services
4-5UXPO feedback → Documentation → Implementation
6-7QAZero Script QA + bug fixes
8InfraTerraform + GitOps
9-10ProductionSecurity review + Deployment

Monorepo Benefits for AI

Mono-repo:
└─ project/
    ├─ frontend/ ──────┐
    ├─ services/ ──────┤  AI reads completely
    ├─ infra/ ─────────┤  Context unified
    └─ packages/ ──────┘

✅ AI understands full context
✅ Single source of truth for types
✅ Atomic commits across layers
✅ Consistent patterns enforced
CLAUDE.md Hierarchy
project/
├── CLAUDE.md           # Project-wide context
├── frontend/CLAUDE.md  # Frontend conventions
├── services/CLAUDE.md  # Backend conventions
└── infra/CLAUDE.md     # Infra conventions

Rule: Area-specific CLAUDE.md overrides project-level rules


bkit Features for Enterprise Level (v1.5.1)

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:

RoleAgentsPDCA Phases
architectenterprise-expert, infra-architectDesign
developerbkend-expertDo, Act
qaqa-monitor, gap-detectorCheck
reviewercode-analyzer, design-validatorCheck, Act

To enable:

  1. Set environment: CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1
  2. Start team mode: /pdca team {feature}
  3. 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.

© ww-w-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/enterprise of ww-w-ai/bkit-claude-code.

Open the folder on GitHubat commit 85b4913

Compare with similar skills

Enterprise next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Enterprise compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Enterprise this skillww-w-ai/bkit-claude-code601—~3.5kAutomated safety check: NotesApache-2.0
Eks Best Practicesaws-samples/appmod-blueprints115—~5kAutomated safety check: PassMIT-0
Asdfjjmartres/opencode133—~2.1kAutomated safety check: NotesMIT
Supercheck Infrastructure Deploymentsupercheck-io/supercheck215—~1.4kAutomated safety check: NotesAGPL-3.0
Cloud Devopsdavila7/claude-code-templates33k4 repos~1.4kAutomated safety check: PassMIT
Infrastructuremicrosoft/physical-ai-toolchain126—~1.6kAutomated safety check: PassMIT

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Questions about Enterprise

What does Enterprise do?

Enterprise-grade systems with microservices, Kubernetes, Terraform, and AI Native methodology. Enterprise is an agent skill from ww-w-ai/bkit-claude-code. Enterprise-grade systems with microservices, Kubernetes, Terraform, and AI Native methodology.

When should I use Enterprise?

Enterprise fits situations like: (QUALITYGATEFAIL / ITERATIONEXHAUSTED / BUDGETEXCEEDED / PHASETIMEOUT); tasks that involve Container orchestration; tasks that involve Infrastructure as code.

How do I install Enterprise in Claude Code?

Run `npx skills add ww-w-ai/bkit-claude-code --skill enterprise -a claude-code`. Or copy the skill folder (skills/enterprise in ww-w-ai/bkit-claude-code) into .claude/skills/enterprise in your project. Claude Code loads it when a task matches its description.

How do I install Enterprise in Codex?

Run `npx skills add ww-w-ai/bkit-claude-code --skill enterprise -a codex`. Or copy the skill folder (skills/enterprise in ww-w-ai/bkit-claude-code) into .agents/skills/enterprise in your project. Codex loads it when a task matches its description.

Can I use Enterprise in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ww-w-ai/bkit-claude-code --skill enterprise -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/enterprise, .gemini/skills/enterprise, .github/skills/enterprise and .opencode/skills/enterprise in your project.

What does Enterprise need to run?

Going by SKILL.md and its folder, Enterprise needs the command-line tools its instructions call (argocd and claude) and credentials named INTERNAL_TOKEN. Our summary lists: Python 3; Docker; A credential in INTERNAL_TOKEN. Its frontmatter pre-approves these tools: Read, Write, Edit, Glob, Grep, Bash, Task, WebSearch.

Does Enterprise access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Enterprise safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Enterprise use?

Enterprise is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Enterprise use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Enterprise?

Skills that share tags, products or a category with Enterprise: Eks Best Practices (aws-samples/appmod-blueprints, 115 stars), Asdf (jjmartres/opencode, 133 stars), Supercheck Infrastructure Deployment (supercheck-io/supercheck, 215 stars) and Cloud Devops (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Enterprise?

ww-w-ai (a GitHub organization) maintains it in ww-w-ai/bkit-claude-code, which has 601 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on September 27, 2026.

Source: ww-w-ai/bkit-claude-code on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.