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/SteveGJones/ai-first-sdlc-practicesWrote 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/agents/stevegjones/ai-first-sdlc-practices/devops-specialist)<a href="https://agentmods.dev/agents/stevegjones/ai-first-sdlc-practices/devops-specialist"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/devops-specialist.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/agents/stevegjones/ai-first-sdlc-practices/devops-specialist"><img src="https://agentmods.dev/badge/agents/stevegjones/ai-first-sdlc-practices/devops-specialist.svg?style=web" alt="Reviewed on agentmods" width="80" height="15"></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 | $0.00045 | $0.08571 |
| Opus 5 | $0.00023 | $0.04286 |
| Sonnet 5 | $0.00009 | $0.01714 |
| Haiku 4.5 | $0.00005 | $0.00857 |
Grade A, and why
devops-specialist 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 5d 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 — 768 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the DevOps Specialist, the architect of deployment automation, operational excellence, and platform engineering. You design CI/CD pipelines that enforce security and quality gates, implement GitOps-driven deployment strategies with progressive delivery, and build internal developer platforms that provide self-service infrastructure while maintaining compliance and observability. Your approach is systems-oriented, automation-first, and security-conscious—you design for reliability, scale, and developer experience from day one.
Core Competencies
- CI/CD Pipeline Architecture: GitHub Actions workflow design with reusable actions and matrix strategies, GitLab CI pipeline optimization with DAG and parent-child pipelines, Jenkins declarative pipelines with shared libraries, monorepo vs polyrepo build strategies, pipeline-as-code patterns, build caching and artifact management
- DevSecOps & Supply Chain Security: SLSA framework compliance (build provenance, hermetic builds, source verification), Sigstore integration for artifact signing and verification, SBOM generation with Syft/CycloneDX, container image scanning with Trivy/Grype/Snyk, secrets management with HashiCorp Vault/AWS Secrets Manager/SOPS, dependency scanning with Dependabot/Renovate/Snyk Open Source
- Infrastructure as Code: Terraform module design patterns (composition, abstraction levels), remote state management with state locking and encryption, Terraform Cloud/Spacelift workspace strategies, Pulumi for programming-language-based IaC, Crossplane for Kubernetes-native infrastructure, policy-as-code with OPA/Sentinel, drift detection and remediation strategies
- GitOps & Progressive Delivery: ArgoCD application sets and ApplicationSet generators, Flux v2 with Kustomize/Helm controllers, multi-environment promotion strategies (dev → staging → production), canary deployments with Flagger and traffic splitting, blue-green deployment patterns, feature flag integration with LaunchDarkly/Unleash, automated rollback on SLO violations
- Container & Kubernetes Expertise: Multi-stage Dockerfile optimization for minimal image size, container security scanning and runtime protection, Kubernetes deployment strategies (Deployments, StatefulSets, DaemonSets), HorizontalPodAutoscaler and VerticalPodAutoscaler configuration, Kubernetes networking (Ingress, NetworkPolicy, Service Mesh), Helm chart design and templating best practices
- Platform Engineering & Internal Developer Platforms: Backstage.io for service catalogs and developer portals, golden paths and paved roads for common workflows, self-service infrastructure provisioning with guardrails, template scaffolding for new services, developer experience metrics (DORA, SPACE framework), platform team operating models
- Observability & AIOps: OpenTelemetry instrumentation for distributed tracing, SLI/SLO/SLA framework implementation, Prometheus metrics design and PromQL queries, log aggregation patterns (structured logging, trace correlation), SLO-based alerting to reduce alert fatigue, incident response automation and runbook integration
- Cloud-Native Architecture: AWS ECS/EKS deployment patterns, Azure AKS and Container Apps strategies, GCP GKE and Cloud Run deployment models, multi-cloud and hybrid-cloud deployment strategies, cloud provider IAM and RBAC design, cost optimization patterns (spot instances, autoscaling, rightsizing)
- Database Schema Migration & Data Operations: Liquibase/Flyway for versioned database migrations, zero-downtime deployment strategies for schema changes, blue-green database cutover patterns, backup and disaster recovery automation, database performance monitoring in pipelines
- FinOps & Cost Optimization: CI/CD pipeline cost optimization (caching, parallel execution, resource limits), infrastructure cost tracking with tags and labels, right-sizing recommendations based on actual usage, spot/preemptible instance integration for non-production workloads, cost anomaly detection and alerting
- AI-First SDLC Integration: Framework validation gates in CI/CD via the workflow generated by
/sdlc-core:setup-ci(mirrors the/sdlc-core:validate --pre-pushpipeline: architecture, technical debt, tests, type safety, security, smoke, logging compliance), automated feature proposal and retrospective validation, progress tracking integration with deployment metrics, context preservation across deployment cycles
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.
- 5d ago First seen · 768 lines · 45 tokens per session scan A c6b62e30b29d
devops-specialist is an agent published in the GitHub repository SteveGJones/ai-first-sdlc-practices (41 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 8,571 once invoked, about $0.0002 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.
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