Cloud native readiness
AI agent skills for Sealos — deploy any project, provision databases, object storage & more with one command. Works with Claude Code, Gemini CLI, Codex.
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Determine whether a repository contains a supported cloud workload, then assess eligible targets for cloud-native readiness with a 0-12 score. Use for containerization readiness, Docker/Kubernetes compatibility, deployment feasibility, workload eligibility, or pre-deployment assessment. Also triggers on "/cloud-native-readiness".
SKILL.md
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Cloud Native Readiness Assessment Skill
Overview
This skill evaluates a repository's readiness for cloud-native microservice deployment through a 3-phase workflow:
- Assess - Reject unsupported workload types, then score eligible targets
- Detect - Check if Docker artifacts already exist (Dockerfile, docker-compose, container images)
- Route - If artifacts exist, return the result directly; if not, invoke
dockerfile-skillto containerize
Workflow
cloud-native-readiness
│
├─ Phase 1: Cloud-Native Assessment
│ ├─ Eligibility fails or needs review → Report evidence, END
│ └─ Eligible → Calculate readiness score
│
├─ 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:
- Read and execute modules/assess.md — Cloud-native readiness evaluation
- Read and execute modules/detect.md — Existing Docker artifacts detection
- Read and execute modules/route.md — Decision routing
Phase 1: Cloud-Native Readiness Assessment
Load and execute: modules/assess.md
Apply knowledge/deployment-eligibility.md
before assigning a readiness score. Continue only when the requested root is
classified eligible.
Evaluates 6 dimensions (each scored 0-2):
| Dimension | What to check |
|---|---|
| Statelessness | Does the app store state locally (sessions in memory, local file writes)? |
| Config Externalization | Are configs hardcoded or driven by env vars / config files? |
| Horizontal Scalability | Can multiple instances run without conflicts? |
| Startup/Shutdown | Does the app start fast and handle SIGTERM gracefully? |
| Observability | Does it have health checks, structured logging, metrics? |
| Service Boundaries | Is 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.dockerignoreDOCKER.mdor 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:
An ineligible or unresolved needs_review result always stops before artifact
detection or Dockerfile generation. Apply the score matrix only to eligible targets.
| Readiness Score | Artifacts Exist | Action |
|---|---|---|
| ≥ 7 | Yes, complete | Report existing setup. Done. |
| ≥ 7 | Yes, partial | Report gaps, suggest improvements. Done. |
| ≥ 7 | No | Invoke dockerfile-skill to generate. |
| 4-6 | Any | Report issues + remediation steps. Optionally proceed with dockerfile-skill. |
| 0-3 | Any | Report blockers. Do NOT invoke dockerfile-skill. |
Readiness Report Format
The final output MUST use this format:
For a stopped eligibility result, report its workload type, reason codes, evidence, and next action without inventing a readiness score.
# Cloud-Native Readiness Report
## Summary
- **Project**: {name}
- **Eligibility**: {eligible | ineligible | needs_review} — {workload type}
- **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
- Deployment Eligibility: knowledge/deployment-eligibility.md — Supported workload types and fail-closed routing
- Assessment Criteria: knowledge/criteria.md — Detailed scoring rubrics
- Anti-Patterns: knowledge/anti-patterns.md — Common cloud-native anti-patterns
- Examples: examples/ — Sample readiness reports
Integration with dockerfile-skill
When routing to dockerfile-skill, pass the assessment context:
- The readiness report findings inform Dockerfile generation decisions
- Detected external services map directly to
docker-compose.ymlservices - Identified concerns become Dockerfile comments /
DOCKER.mdcaveats - 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.)