moai-artifacts-builder
About
The moai-artifacts-builder skill provides enterprise artifact management with governance, lifecycle control, and supply chain security aligned with November 2025 standards. It handles multiple artifact formats while ensuring SBOM generation, compliance scanning, and security verification. Use this skill when you need to manage production artifacts with automated governance and security controls.
Quick Install
Claude Code
Recommended/plugin add https://github.com/modu-ai/moai-adkgit clone https://github.com/modu-ai/moai-adk.git ~/.claude/skills/moai-artifacts-builderCopy and paste this command in Claude Code to install this skill
Documentation
Enterprise Artifact Management & Governance - v4.1.0
Skill Overview
November 2025 Enterprise Standards: Production artifact management with governance, SBOM, and supply chain security
| Feature | Coverage |
|---|---|
| Artifact Types | 7 standard formats (80% enterprise coverage) |
| Security | SBOM, provenance, immutability, SOC 2/ISO 27001 |
| Context7 MCP | ✅ Metadata lookup and artifact index search |
| Compliance | Automated scanning and signature verification |
Core Responsibilities
- Artifact Classification: 7 enterprise-standard formats
- Lifecycle Management: Creation → Validation → Storage → Deployment → Retirement
- Governance: RBAC, monitoring, audit trails
- Security: SBOM, provenance, supply chain security
- Context7 Integration: Metadata and vulnerability correlation
Level 1: Quick Reference (50-150 lines)
Essential Artifact Patterns
Docker Container Artifact:
artifact:
id: "[email protected]"
type: "Container Image"
format: "Docker OCI"
registry: "docker.io"
repository: "myorg/api-gateway"
tag: "2.5.1"
metadata:
created: 2025-11-13T14:30:00Z
source_repo: "https://github.com/myorg/api-gateway"
source_commit: "abc123def456789"
creator: "github-actions"
security:
vulnerability_scan:
tool: "Trivy v0.54.0"
status: "passed"
critical_vulnerabilities: 0
high_vulnerabilities: 0
Python Package Artifact:
artifact:
id: "[email protected]"
type: "Language Package"
format: "Python Wheel"
filename: "data_pipeline-1.3.2-py3-none-any.whl"
pypi_url: "https://pypi.org/project/data-pipeline/1.3.2/"
metadata:
created: 2025-11-13T13:45:00Z
python_version: "3.8+"
checksum:
sha256: "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855"
dependencies:
- name: "pandas"
version: "2.1.0"
requirement: "pandas>=2.1.0,<3.0"
SBOM Requirement (CycloneDX 1.6):
sbom:
version: "CycloneDX 1.6"
spec_version: "1.6"
metadata:
timestamp: 2025-11-13T14:30:00Z
tools:
- name: "syft"
version: "0.95.0"
components:
- type: "library"
name: "requests"
version: "2.31.0"
purl: "pkg:pypi/[email protected]"
licenses: [{ name: "Apache-2.0" }]
7 Enterprise Artifact Types:
- Container Images (Docker/OCI) - 80-90% enterprise usage
- Language Packages (Maven, npm, PyPI) - Package managers, semantic versioning
- Binary Artifacts (Executable, .so, .dll) - Native code, compiled binaries
- Documentation (API docs, guides) - HTML, PDF, Markdown formats
- Configuration (IaC, templates) - Terraform, CloudFormation, Helm
- Test Reports (Coverage, scans) - JSON, XML, CSV compliance data
- Source Archives (Release tarballs) - .tar.gz, .zip with GPG signatures
Core Governance Rules:
- ✅ Classification First: Every artifact maps to one of 7 types
- ✅ Provenance Tracking: Complete source-to-production traceability
- ✅ Immutability: Published artifacts cannot be modified
- ✅ SBOM Required: All artifacts include Software Bill of Materials
- ✅ Security Scanning: Pre-deployment vulnerability verification
Level 2: Practical Implementation (200-300 lines)
Enterprise Artifact Lifecycle
Phase 1: Creation:
phase: "creation"
activities:
- Build artifact from source (CI/CD)
- Generate metadata (timestamp, commit SHA, builder)
- Create initial SBOM (syft, cyclonedx)
- Assign semantic version (SemVer)
artifacts:
- Raw binary/container
- SBOM (CycloneDX or SPDX)
- Build log
- Metadata JSON
Phase 2: Validation:
phase: "validation"
gates:
- ✅ Vulnerability scan (Trivy, Grype)
- ✅ License compliance (FOSSA, Black Duck)
- ✅ SBOM validation (cyclonedx-python)
- ✅ Signature verification (GPG/RSA)
- ✅ Artifact integrity (checksum match)
failure_behavior:
critical: "block_deployment" # CRITICAL/HIGH vulnerabilities
high: "require_approval" # Manual approval required
medium_low: "log_warning" # Warning only
Multi-Format Repository Setup:
repositories:
- name: "container-registry"
type: "Container (Docker/OCI)"
upstream: "docker.io"
proxy_cache:
enabled: true
retention_days: 30
security:
scan_on_push: true
signature_required: true
sbom_required: true
- name: "python-packages"
type: "Python (PyPI)"
upstream: "pypi.org"
retention_policy:
strategy: "semantic_versioning"
keep_release_versions: true
keep_prerelease: 3
- name: "binary-artifacts"
type: "Generic Binary"
retention_policy:
max_size_gb: 500
cleanup_strategy: "lru"
governance:
rbac:
admin_group: "release-engineering"
publish_group: "ci-automation"
read_group: "developers"
approval_workflow:
require_approval: true
approvers: ["security-team", "release-manager"]
timeout_hours: 24
Context7 MCP Integration:
# Context7 artifact metadata lookup
context7_query = {
"operation": "artifact_metadata_lookup",
"artifact_id": "[email protected]",
"fields": [
"sbom",
"provenance",
"signatures",
"vulnerability_scan",
"compliance_status"
]
}
# Response structure
response = {
"artifact": {
"id": "[email protected]",
"registry": "docker.io",
"location": "docker.io/myorg/app-service:1.0.0",
"sbom_url": "context7://sbom-index/[email protected]",
"provenance_verified": True,
"vulnerabilities_critical": 0
}
}
SBOM Index Search:
# Context7 vulnerability correlation
context7_query = {
"operation": "vulnerability_correlation",
"cve_id": "CVE-2024-5678",
"affected_components": ["requests", "urllib3"],
"action": "find_artifacts"
}
# Response
response = {
"cve": "CVE-2024-5678",
"severity": "HIGH",
"affected_artifacts": [
{
"artifact_id": "[email protected]",
"component": "[email protected]",
"status": "vulnerable",
"patch_available": True,
"patched_version": "[email protected]",
"recommended_action": "upgrade_component_rebuild"
}
]
}
Binary Artifact Example:
artifact:
id: "[email protected]"
type: "Binary Artifact"
format: "Native Executable (.so)"
files:
- name: "libperformance_optimizer.so"
arch: "x86_64"
os: "linux"
size_bytes: 512000
checksum_sha256: "abc123..."
signature: "RSA-4096"
- name: "libperformance_optimizer.dylib"
arch: "arm64"
os: "macos"
size_bytes: 480000
checksum_sha256: "def456..."
signature: "RSA-4096"
metadata:
created: 2025-11-13T12:00:00Z
compiler: "gcc-13"
optimization_flags: "-O3 -march=native"
source_commit: "release/3.1.0"
distribution:
storage: "artifactory.myorg.com"
bucket: "native-binaries"
access: "restricted"
approval_required: true
Terraform/IaC Artifact:
artifact:
id: "[email protected]"
type: "Configuration/IaC"
format: "Terraform Module"
files:
- path: "main.tf"
size_bytes: 2048
checksum_sha256: "ijk789..."
- path: "variables.tf"
size_bytes: 1024
checksum_sha256: "lmn012..."
metadata:
created: 2025-11-13T10:30:00Z
terraform_version: ">= 1.5"
cloud_provider: "AWS"
modules_included: 5
validation:
terraform_fmt: "passed"
terraform_validate: "passed"
security_scan: "passed"
Level 3: Advanced Integration (50-150 lines)
Advanced Governance & Security
Supply Chain Security Framework:
artifact_governance:
supply_chain_security:
provenance_tracking: true
source_commit_required: true
builder_attestation: true
sbom_requirements:
format: ["CycloneDX-1.6", "SPDX-2.3"]
components_scanned: true
license_compliance: true
vulnerability_threshold:
critical: "block"
high: "require_approval"
medium: "log_only"
signature_verification:
algorithms: ["RSA-4096", "ECDSA-P256"]
trusted_signers: ["[email protected]"]
timestamp_authority: "https://timestamp.comodoca.com"
context7_mcp:
enabled: true
operations:
- artifact_metadata_lookup
- sbom_index_search
- vulnerability_correlation
- compliance_status_check
Attestation Framework (SLSA):
artifact:
id: "[email protected]"
type: "Attestation"
format: "in-toto/SLSA"
attestation:
version: "0.3"
statement:
_type: "https://in-toto.io/Statement/v0.1"
subject:
- name: "docker.io/myorg/app-service"
digest:
sha256: "abc123def456..."
predicateType: "https://slsa.dev/provenance/v1"
predicate:
buildDefinition:
buildType: "https://github.com/slsa-framework/slsa-github-generator@v1"
externalParameters:
source: "https://github.com/myorg/app-service"
ref: "refs/tags/1.0.0"
runDetails:
builder:
id: "https://github.com/slsa-framework/slsa-github-generator"
completion:
finishTime: "2025-11-13T14:35:00Z"
Advanced Automation:
automation:
auto_scan_on_push: true
auto_sbom_generation: true
auto_compliance_report: true
auto_deprecation_warnings: true
monitoring:
sla_targets:
- artifact_availability: "99.99%"
- scan_completion: "< 5 minutes"
- deployment_success: "> 95%"
alerts:
- high_vulnerability_detected: "notify_security_team"
- signature_verification_failed: "block_deployment"
- unauthorized_access: "incident_response"
Enterprise Compliance:
compliance:
sbom_required: true
sbom_format: "CycloneDX"
signature_required: true
audit_retention_years: 7
frameworks:
- SOC 2 Type II
- ISO 27001
- NIST CSF
- GDPR (for EU deployments)
reporting:
automated_reports: true
vulnerability_reports: "daily"
compliance_reports: "weekly"
audit_trail: "7 years"
Release Bundle Example:
artifact:
id: "[email protected]"
type: "Release Bundle"
format: "GitHub Release"
release:
tag: "v2.0.0"
name: "Version 2.0.0 - Production Release"
created: 2025-11-13T14:40:00Z
assets:
- name: "app-service-2.0.0.tar.gz"
size_bytes: 5242880
checksum_sha256: "abc123def456..."
- name: "SBOM.json"
size_bytes: 123456
- name: "SBOM.json.sig"
size_bytes: 256
changelog: |
## What's New
- Feature A
- Feature B
- Bug fixes
Best Practices Checklist
Artifact Creation
- Classification: Choose from 7 standard types
- Metadata: Include creator, timestamp, source commit
- Provenance: Source repo, commit SHA, build log links
- SBOM: CycloneDX or SPDX format
- Signature: RSA-4096 or ECDSA-P256
- Scanning: Trivy/Grype vulnerability detection
- Immutability: No post-publication modifications
Repository Design
- Multi-format Support: Container, Python, Binary, IaC, Docs
- Registry Configuration: Official (upstream), proxy cache (local)
- RBAC: Admin, publisher, read permissions
- Approval Workflow: Security team and release manager approval
- Auto-scanning: Push-time vulnerability scanning
- SBOM Required: All artifacts must include SBOM
- Audit Trail: 7-year retention for compliance
Version: 4.1.0 Enterprise
Last Updated: 2025-11-13
Status: Production Ready
Standards: November 2025 Enterprise Standards
Compliance: SOC 2, ISO 27001, NIST CSF Ready
GitHub Repository
Related Skills
sglang
MetaSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
content-collections
MetaThis skill provides a production-tested setup for Content Collections, a TypeScript-first tool that transforms Markdown/MDX files into type-safe data collections with Zod validation. Use it when building blogs, documentation sites, or content-heavy Vite + React applications to ensure type safety and automatic content validation. It covers everything from Vite plugin configuration and MDX compilation to deployment optimization and schema validation.
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
llamaguard
OtherLlamaGuard is Meta's 7-8B parameter model for moderating LLM inputs and outputs across six safety categories like violence and hate speech. It offers 94-95% accuracy and can be deployed using vLLM, Hugging Face, or Amazon SageMaker. Use this skill to easily integrate content filtering and safety guardrails into your AI applications.
