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    release fleet

    2

    Deterministic containerization, Kubernetes orchestration, and hardened CI/CD pipelines with a reliability ledger.

    $19.90

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    release fleet

    release fleet

    Example session with this skill installed

    I need to containerize my 'auth-service' and set up a hardened CI pipeline with a release checklist.

    • Read your context and instructions
    • Compiled the release fleet

    I have generated the production-ready Docker configuration, Kubernetes manifests, and the GitHub Actions pipeline for auth-service. All containers are configured to run as non-root with pinned base image digests.

    Release Fleet Artifacts

    
# Dockerfile
FROM python:3.11-slim@sha256:83c510... AS builder
WORKDIR /app
COPY requirements.txt .
RUN pip install --user --no-cache-dir -r requirements.txt

FROM python:3.11-slim@sha256:83c510...
RUN groupadd -g 999 harbor && useradd -r -u 999 -g harbor harbor
USER harbor
WORKDIR /app
COPY --from=builder /home/harbor/.local /home/harbor/.local
COPY . .
HEALTHCHECK --interval=30s --timeout=3s CMD curl -f http://localhost:8080/health || exit 1
ENTRYPOINT ["python", "main.py"]

    
# .github/workflows/harbor.yml
name: harbor-release
on: [push]
permissions:
  contents: read
jobs:
  check:
    runs-name: Release Checklist
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Harbor Secrets Scan
        run: harbor scan --path .
      - name: Go/No-Go Checklist
        run: harbor release auth-service --check-only

    The reliability ledger has been initialized at warroom/ledger.jsonl. Your initial reliability score is recorded.

    Next steps

    1. Run harbor chart auth-service to generate the Kubernetes deployment manifests.
    2. Update the HEALTHCHECK path in the Dockerfile if your app uses a different endpoint.
    3. Perform your first harbor drill auth-service to record a successful rollback rehearsal in the ledger.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Manual deployment and infrastructure configuration often lead to inconsistent environments, security holes, and failed releases. This skill automates the generation of production-ready container and orchestration artifacts using a deterministic fleet of specialized agent roles.

    What it does

    • Reproducible Dockerization generates multi-stage Dockerfiles with pinned bases, healthchecks, and non-root user enforcement.
    • Orchestration Scaffolding creates Kubernetes manifests including liveness/readiness probes, resource budgets, and rolling update strategies.
    • Pipeline Hardening builds GitHub Actions workflows with minimal permission scopes, secrets gating, and dependency caching.
    • Release Governance executes a mandatory go/no-go checklist ensuring git hygiene, changelog presence, and passing tests before deployment.
    • Reliability Ledger maintains a monotonic reliability score and records rollback drills and blameless postmortems in a persistent log.

    How it works

    1. Dock run harbor dock <app> to generate the core container files and entrypoints.
    2. Chart execute harbor chart <app> to produce cloud-agnostic Kubernetes Deployment and Service YAMLs.
    3. Pipeline use harbor pipeline <app> to output security-hardened CI/CD workflow configurations.
    4. Release & Drill invoke harbor release for pre-flight checks and harbor drill to rehearse and record recovery procedures.

    Frameworks & tools

    The skill generates standard YAML and HCL compatible with any cloud provider. It requires Python 3.10+ for the local fleet tooling and integrates with Docker and Git.

    Why this beats prompting it yourself

    Generic prompts often ignore security best practices like runAsNonRoot or pinned image digests. This skill enforces operational discipline via a deterministic ledger, ensuring your reliability score never falls and rollback drills are never forgotten.

    Use cases

    • Containerize a legacy Python or Node.js app for production.
    • Generate least-privilege IAM and Kubernetes manifests for a new microservice.
    • Conduct a blameless postmortem and log action items after a production incident.
    • Setup a hardened GitHub Actions pipeline with automated secrets scanning.

    Known limitations

    Requires Python 3.10 or higher. Full functionality depends on a local Git remote and Docker installation to validate artifacts and ledger entries.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    Python

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