mcpField
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cloud-native-readiness

cloud-native-readiness

github.com/zjy365Cloud & DevOps49 installs
Repository

About cloud-native-readiness

cloud-native-readiness assesses a repository's suitability for cloud-native microservice deployment and inventories existing Docker and Kubernetes artifacts. It reports findings and routes projects with sufficient readiness but no container artifacts to dockerfile-skill.

Key features

  1. 01Six-dimension readiness assessment
  2. 02Scored readiness report
  3. 03Docker and Kubernetes artifact detection
  4. 04Existing setup quality review
  5. 05Routing based on readiness and artifacts
  6. 06Context handoff to dockerfile-skill

Use cases

  1. 01Evaluate a repository before containerization
  2. 02Find existing Docker and Kubernetes deployment files
  3. 03Identify readiness gaps and remediation steps
  4. 04Pass assessment findings to dockerfile-skill

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/zjy365/seakills --skill cloud-native-readiness

After installing, type/cloud-native-readinessin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

examples/
sample-report.md
knowledge/
anti-patterns.md
criteria.md
scoring-model.md
sealos-patterns.md
modules/
assess.md
detect.md
route.md

Details

From the project README

Cloud Native Readiness Assessment Skill

Overview

This skill evaluates a repository's readiness for cloud-native microservice deployment through a 3-phase workflow:

  1. Assess - Analyze the project against cloud-native criteria and produce a readiness report
  2. Detect - Check if Docker artifacts already exist (Dockerfile, docker-compose, container images)
  3. Route - If artifacts exist, return the result directly; if not, invoke dockerfile-skill to containerize

Workflow

cloud-native-readiness
  │
  ├─ Phase 1: Cloud-Native Assessment
  │    ├─ NOT suitable → Report reasons, suggest remediation, END
  │    └─ Suitable → Continue
  │
  ├─ Phase 2: Existing Artifacts Detection
  │    ├─ Found Dockerfile/docker-compose/image → Report existing setup, END
  │    └─ Not found → Continue
  │
  └─ Phase 3: Route to dockerfile-skill
       └─ Invoke /dockerfile to generate Docker configuration

Usage

/cloud-native-readiness              # Assess current directory
/cloud-native-readiness <path>       # Assess specific path
/cloud-native-readiness <github-url> # Clone and assess

Quick Start

When invoked, ALWAYS follow this sequence:

  1. Read and execute modules/assess.md — Cloud-native readiness evaluation
  2. Read and execute modules/detect.md — Existing Docker artifacts detection
  3. Read and execute modules/route.md — Decision routing

Phase 1: Cloud-Native Readiness Assessment

Load and execute: modules/assess.md

Evaluates 6 dimensions (each scored 0-2):

DimensionWhat to check
StatelessnessDoes the app store state locally (sessions in memory, local file writes)?
Config ExternalizationAre configs hardcoded or driven by env vars / config files?
Horizontal ScalabilityCan multiple instances run without conflicts?
Startup/ShutdownDoes the app start fast and handle SIGTERM gracefully?
ObservabilityDoes it have health checks, structured logging, metrics?
Service BoundariesIs it a focused service or a tightly-coupled monolith?

Scoring:

  • 10-12: Excellent — fully cloud-native ready
  • 7-9: Good — ready with minor adjustments
  • 4-6: Fair — needs some refactoring before containerization
  • 0-3: Poor — significant rework needed, not recommended for containerization now

Output: Structured readiness report with score, findings, and recommendations.

Phase 2: Existing Artifacts Detection

Load and execute: modules/detect.md

Checks for:

  • Dockerfile / Dockerfile.* (multi-stage, multi-service)
  • docker-compose.yml / docker-compose.yaml / compose.yml
  • .dockerignore
  • DOCKER.md or docker-related documentation
  • Container registry references (ghcr.io, docker.io, ECR, GCR, ACR)
  • Kubernetes manifests (k8s/, kubernetes/, deploy/, helm/, charts/)
  • CI/CD pipeline with Docker build steps (.github/workflows/, .gitlab-ci.yml)

Output: Inventory of existing Docker/K8s artifacts with quality assessment.

Phase 3: Routing Decision

Load and execute: modules/route.md

Decision Matrix:

Readiness ScoreArtifacts ExistAction
≥ 7Yes, completeReport existing setup. Done.
≥ 7Yes, partialReport gaps, suggest improvements. Done.
≥ 7NoInvoke dockerfile-skill to generate.
4-6AnyReport issues + remediation steps. Optionally proceed with dockerfile-skill.
0-3AnyReport blockers. Do NOT invoke dockerfile-skill.

Readiness Report Format

The final output MUST use this format:

# Cloud-Native Readiness Report

## Summary
- **Project**: {name}
- **Score**: {score}/12 ({rating})
- **Verdict**: {Ready | Ready with caveats | Needs work | Not recommended}

## Assessment Details

### ✅ Strengths
- {what's already cloud-native friendly}

### ⚠️ Concerns
- {issues that need attention}

### ❌ Blockers (if any)
- {critical issues preventing containerization}

## Dimension Scores

| Dimension | Score | Notes |
|-----------|-------|-------|
| Statelessness | {0-2} | {detail} |
| Config Externalization | {0-2} | {detail} |
| Horizontal Scalability | {0-2} | {detail} |
| Startup/Shutdown | {0-2} | {detail} |
| Observability | {0-2} | {detail} |
| Service Boundaries | {0-2} | {detail} |

## Existing Docker Artifacts
- {inventory or "None found"}

## Recommendation
- {next steps}

Supporting Resources

Integration with dockerfile-skill

When routing to dockerfile-skill, pass the assessment context:

  1. The readiness report findings inform Dockerfile generation decisions
  2. Detected external services map directly to docker-compose.yml services
  3. Identified concerns become Dockerfile comments / DOCKER.md caveats
  4. The assessment's config externalization findings drive ENV/ARG setup

Handoff: When invoking dockerfile-skill, include a summary of:

  • Detected language/framework/package manager
  • External service dependencies
  • Config externalization status
  • Any special concerns (stateful components, long startup, etc.)

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