Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/TheBeardedBearSAS/claude-craftWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/thebeardedbearsas/claude-craft/docker-cicd-pipeline)<a href="https://agentmods.dev/commands/thebeardedbearsas/claude-craft/docker-cicd-pipeline"><img src="https://agentmods.dev/badge/commands/thebeardedbearsas/claude-craft/docker-cicd-pipeline/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/thebeardedbearsas/claude-craft/docker-cicd-pipeline"><img src="https://agentmods.dev/badge/commands/thebeardedbearsas/claude-craft/docker-cicd-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00004 | $0.02442 |
| Opus 5 | $0.00002 | $0.01221 |
| Sonnet 5 | $0.00001 | $0.00488 |
| Haiku 4.5 | $0.00000 | $0.00244 |
Grade A, and why
docker-cicd-pipeline scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Docker CI/CD Pipeline
Du bist ein Docker CI/CD Experte. Du musst eine vollständige Pipeline zum Bauen, Testen, Scannen und Deployen von Docker-Images generieren.
Argumente
$ARGUMENTS
Argumente:
- CI-Plattform: github, gitlab, circleci
- Registry: ghcr, ecr, gcr, dockerhub, harbor
- Umgebungen: dev, staging, prod
Beispiel: /docker:cicd-pipeline github ghcr envs:staging,prod
Plan-Modus
Der Plan-Modus ist obligatorisch. Vor der Ausführung aktiviert Claude den Plan-Modus, um betroffenen Code zu analysieren, einen Implementierungsplan vorzuschlagen und auf Ihre Validierung zu warten, bevor Änderungen vorgenommen werden.
MISSION
Schritt 1: Anforderungen analysieren
══════════════════════════════════════════════════════════════
🚀 DOCKER CI/CD KONFIGURATION
══════════════════════════════════════════════════════════════
Plattform: {github|gitlab|circleci}
Registry: {registry}
Umgebungen: {liste}
──────────────────────────────────────────────────────────────
📋 PIPELINE-ARCHITEKTUR
──────────────────────────────────────────────────────────────
┌─────────┬──────────┬──────────┬──────────┬─────────────┐
│ BUILD │ TEST │ SCAN │ PUSH │ DEPLOY │
├─────────┼──────────┼──────────┼──────────┼─────────────┤
│ Lint │ Unit │ Trivy │ Tag │ Staging │
│ Build │ Integ │ SBOM │ Push │ Prod │
│ Cache │ E2E │ Sign │ Manifest │ Rollback │
└─────────┴──────────┴──────────┴──────────┴─────────────┘
Schritt 2: Pipeline generieren
GitHub Actions
# .github/workflows/docker.yml
name: Docker CI/CD
on:
push:
branches: [main, develop]
pull_request:
branches: [main]
release:
types: [published]
env:
REGISTRY: ghcr.io
IMAGE_NAME: ${{ github.repository }}
jobs:
# ═══════════════════════════════════════════════════════════
# BUILD & TEST
# ═══════════════════════════════════════════════════════════
build:
runs-on: ubuntu-latest
permissions:
contents: read
packages: write
security-events: write
outputs:
image-tag: ${{ steps.meta.outputs.tags }}
image-digest: ${{ steps.build.outputs.digest }}
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Container Registry
if: github.event_name != 'pull_request'
uses: docker/login-action@v3
with:
registry: ${{ env.REGISTRY }}
username: ${{ github.actor }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Extract metadata
id: meta
uses: docker/metadata-action@v5
with:
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
tags: |
type=ref,event=branch
type=ref,event=pr
type=semver,pattern={{version}}
type=semver,pattern={{major}}.{{minor}}
type=sha,prefix=
- name: Build and push
id: build
uses: docker/build-push-action@v5
with:
context: .
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
cache-from: type=gha
cache-to: type=gha,mode=max
provenance: true
sbom: true
# ═══════════════════════════════════════════════════════════
# SECURITY SCAN
# ═══════════════════════════════════════════════════════════
scan:
needs: build
runs-on: ubuntu-latest
if: github.event_name != 'pull_request'
steps:
- name: Run Trivy vulnerability scanner
uses: aquasecurity/trivy-action@master
with:
image-ref: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}@${{ needs.build.outputs.image-digest }}
format: 'sarif'
output: 'trivy-results.sarif'
severity: 'CRITICAL,HIGH'
exit-code: '1'
- name: Upload Trivy scan results
uses: github/codeql-action/upload-sarif@v3
if: always()
with:
sarif_file: 'trivy-results.sarif'
# ═══════════════════════════════════════════════════════════
# DEPLOY STAGING
# ═══════════════════════════════════════════════════════════
deploy-staging:
needs: [build, scan]
runs-on: ubuntu-latest
if: github.ref == 'refs/heads/main'
environment:
name: staging
url: https://staging.example.com
steps:
- name: Deploy to Staging
run: |
echo "Deploying ${{ needs.build.outputs.image-digest }} to staging"
# Deployment-Befehle hier hinzufügen
# ═══════════════════════════════════════════════════════════
# DEPLOY PRODUCTION
# ═══════════════════════════════════════════════════════════
deploy-prod:
needs: [build, scan]
runs-on: ubuntu-latest
if: startsWith(github.ref, 'refs/tags/v')
environment:
name: production
url: https://example.com
steps:
- name: Deploy to Production
run: |
echo "Deploying ${{ needs.build.outputs.image-digest }} to production"
# Deployment-Befehle hier hinzufügen
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 342 lines · 4 tokens per session scan A e902dcc66033
docker-cicd-pipeline is a command published in the GitHub repository TheBeardedBearSAS/claude-craft (105 stars, last pushed 7d ago), licensed MIT. It adds 4 tokens to every session and 2,442 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
deploy
Deploy application with pre/post-deploy checks.
security-scan
Run security audit on codebase.
test-suite
Run comprehensive test suite with coverage analysis.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.