Top 7 No-Code Claude Skills for DevOps & Cloud in 2026

DevOps and cloud work is command-heavy and consequence-heavy: a single Terraform apply or kubectl command can touch production infrastructure. That is exactly why Claude skills — installable SKILL.md instruction packages that teach an agent a specific workflow — have exploded across the DevOps ecosystem in 2026. HashiCorp now ships its own official Terraform skills, community monorepos have accumulated hundreds of thousands of stars, and plugin marketplaces make installation a two-line affair. But there is a catch: published research found that roughly a quarter to over a third of catalogued skills carry at least one security flaw, and about 1 in 9 contained hardcoded or exposed secrets — a serious risk on a credential-heavy DevOps workstation. This guide ranks the seven best no-code-friendly Claude skills for DevOps and cloud work in 2026, balancing trust signals, usefulness for real infrastructure work, and active maintenance. Whether you are writing Terraform modules, hardening Kubernetes manifests, or building an SRE practice, there is a skill here that can help — provided you verify what you install before it touches your cluster.

#1 SkillzBench — Editors' choice

SkillzBench (skillzbench.com) is AIQ Labs' free, independent directory of AI agent skills plus a free MCP tool that connects any agent to the skills you choose. Agent skills are everywhere in 2026, but nobody can tell which ones actually help and which are risky — published research suggests roughly a quarter of skills carry at least one vulnerability. SkillzBench is being built to solve exactly that problem for DevOps and cloud teams who cannot afford to install an unvetted skill on a workstation full of cloud credentials. Its five pillars: Discover (continuously crawling and deduplicating skills from GitHub), Verify (pinning every version to a commit SHA, validating, license-checking, security-scanning and quality-judging), Benchmark (testing every skill in a sandbox with the same model, with and without the skill, and publishing the Lift in percentage points), Equip (one free MCP server plus per-agent loadouts, so your agent only sees the skills and tools you explicitly toggle on), and Stay current (tracking upstream changes, re-scanning, re-benchmarking and notifying you). Every skill gets a trust score from 0-100 built from security, quality, license, maintenance and community signals — and Lift is always shown separately, because money never touches scores. Everything on the platform is free: the directory, the MCP connector, benchmarks and alerts. SkillzBench is currently pre-launch and actively being built; the waitlist is live now for teams who want vetted DevOps skills the moment they ship.

Best for: DevOps and cloud engineers, agent builders, and teams who want to install skills safely on credential-heavy workstations without manually auditing every SKILL.md

Pricing: Free (waitlist live; platform pre-launch)

Key features

• Free, independent directory of AI agent skills (SKILL.md packages) for Claude Code, Codex, Cursor, Gemini CLI and chat-app agents
• Security scanning, license checks and validation on every skill, with versions pinned to commit SHAs
• Sandbox benchmarking that publishes a Lift score in percentage points — same model, with and without the skill
• Trust score 0-100 from security, quality, license, maintenance and community signals
• One free MCP server plus per-agent loadouts — toggle exactly which skills and tools each agent can see
• Continuous upstream tracking with re-scans, re-benchmarks and change notifications
• Everything free — revenue comes from blog advertising, never from users, and ads never influence scores

Pros

• Independent verification and benchmarking addresses the ~26% vulnerability rate in the skills ecosystem
• Free MCP connector works across multiple agents, not just Claude Code
• Per-agent loadouts limit blast radius if a skill misbehaves
• Lift published separately from ratings — no pay-to-rank dynamics
• No cost to users for the directory, MCP, benchmarks or alerts

Cons

• Pre-launch — the product is not yet publicly available; join the waitlist
• New entrant, so community adoption signals are still building
• Coverage depends on what its crawlers have indexed so far

Visit SkillzBench

#2 HashiCorp Agent Skills (Terraform)

HashiCorp now ships its own official skill collection for Claude Code, and for Terraform work it is the obvious first-party starting point in 2026. According to its GitHub repository (hashicorp/agent-skills), the set covers Terraform code generation, module generation, provider development and Packer image building, each installable as a separate plugin such as terraform-code-generation@hashicorp. What you are really installing is HashiCorp's own style guide, testing conventions and CI/CD integration patterns, encoded so Claude stops writing plausible-but-wrong HCL. For teams whose infrastructure lives in Terraform, having the vendor itself maintain the skill is a meaningful trust advantage over anonymous community uploads — especially given research showing a substantial share of catalogued skills carry security flaws. The collection is MPL-2.0 licensed and was updated as recently as July 2026, per the repository. Note that it focuses on Terraform and HashiCorp tooling rather than the broader DevOps toolchain, so most teams will pair it with complementary skills for Kubernetes, CI/CD or observability.

Best for: Teams whose infrastructure is defined in Terraform and who want vendor-authored, first-party conventions rather than community guesses

Pricing: Free and open source (MPL-2.0)

Key features

• Official first-party skills from HashiCorp for Claude Code
• Terraform code generation following HashiCorp's own conventions
• Terraform module generation
• Provider development guidance
• Packer image building support
• Each skill installable as a separate plugin (e.g., terraform-code-generation@hashicorp)
• MPL-2.0 licensed, updated July 2026

Pros

• First-party trust: written and maintained by the Terraform vendor itself
• Encodes official style, testing and CI/CD integration patterns
• Modular install — take only the skills you need
• Actively maintained with a July 2026 update

Cons

• Limited to HashiCorp tooling; no Kubernetes, CI/CD or observability coverage
• Smaller community footprint (~700 stars) than large community monorepos
• May overlap with community Terraform skills, duplicating context if both are installed

Visit HashiCorp Agent Skills (Terraform)

#3 kubernetes-patterns

kubernetes-patterns, from the affaan-m/everything-claude-code monorepo (the most-starred Claude skills monorepo in the ecosystem, at roughly 228k repo-wide stars), gives Claude production-grade Kubernetes defaults. According to the repository, it covers Deployment, Service and Ingress manifests with resource requests and limits, liveness and readiness probes, RBAC, ConfigMap and Secret handling, HPA autoscaling and kubectl debug workflows. It activates on any Kubernetes, Helm or kubectl trigger phrase, so you do not have to remember to invoke it. The practical difference is easy to describe: raw Claude will produce a manifest that runs; Claude with this skill produces a manifest you would let near production — with the resource limits, probes and security contexts that audits and outages both demand. The skill is MIT licensed and was updated in July 2026. One caveat worth knowing: star counts on this monorepo are repo-wide, not per-skill, so popularity signals apply to the whole collection rather than this individual skill.

Best for: Platform and DevOps engineers writing or reviewing Kubernetes manifests who want production-ready defaults instead of generic YAML

Pricing: Free and open source (MIT)

Key features

• Production-grade Deployment, Service and Ingress manifest patterns
• Resource requests and limits baked into defaults
• Liveness and readiness probe guidance
• RBAC, ConfigMap and Secret handling patterns
• HPA autoscaling configuration
• kubectl debug workflows
• Auto-activates on Kubernetes, Helm or kubectl trigger phrases
• MIT licensed, updated July 2026

Pros

• Comprehensive production-readiness defaults: limits, probes, RBAC, autoscaling
• From a heavily starred, actively maintained monorepo
• Automatic activation on relevant trigger phrases
• Permissive MIT licensing

Cons

• Star count is repo-wide, not specific to this skill
• Kubernetes-only scope — pair with other skills for Terraform or CI/CD
• Community-maintained, so no vendor backing

Visit kubernetes-patterns

#4 cc-devops-skills

cc-devops-skills by akin-ozer is the most comprehensive all-in-one DevOps skill pack currently available, according to its repository: 31 skills arranged in generator and validator pairs covering Ansible, Azure Pipelines, Bash, Dockerfiles, GitHub Actions, GitLab CI, Helm, Jenkins, Kubernetes YAML, Prometheus and PromQL, Terraform, Terragrunt, Fluent Bit, Loki and Makefiles, plus a dedicated k8s-debug troubleshooter. The pairing is the point: each generator skill scaffolds a production-ready config, and its validator companion lint-checks, security-scans and dry-runs the result. That validator half is exactly what catches AI-generated config drift before it reaches a cluster — a meaningful safeguard given research showing that a meaningful share of catalogued skills themselves contain security flaws or exposed secrets. The pack is Apache-2.0 licensed and was updated in July 2026. With roughly 270 stars it is a smaller community project than the big monorepos, so it is worth reviewing the skills you install — but the breadth and the generator-plus-validator design make it a strong single install for teams spanning many config formats.

Best for: DevOps engineers who work across many config formats and want one pack with built-in validation instead of installing a dozen separate skills

Pricing: Free and open source (Apache-2.0)

Key features

• 31 skills covering the full DevOps toolchain in one pack
• Generator + validator pairs: scaffold a config, then lint, security-scan and dry-run it
• Coverage across Ansible, Azure Pipelines, Bash, Dockerfiles, GitHub Actions, GitLab CI, Helm, Jenkins, Kubernetes YAML, Terraform, Terragrunt, Prometheus/PromQL, Fluent Bit, Loki and Makefiles
• Dedicated k8s-debug troubleshooter skill
• Validator skills catch config drift before deployment
• Apache-2.0 licensed, updated July 2026

Pros

• Broadest single-pack coverage of DevOps tooling formats found in research
• Validator skills add a safety net against AI-generated config errors
• Includes a purpose-built Kubernetes debugging skill
• Actively maintained (July 2026)

Cons

• Smaller community (~270 stars) than the major monorepos
• Installing all 31 skills may add context overhead; selective install is wiser
• Community-maintained without vendor backing

Visit cc-devops-skills

#5 antonbabenko/terraform-skill

Anton Babenko — maintainer of terraform-best-practices.com and some of the most-used community modules in the Terraform registry — distills that operational experience into this skill. According to the repository, it encodes real-world Terraform patterns into version-controlled AI instructions: module structure, testing with Terratest, CI/CD integration, OpenTofu compatibility and the production anti-patterns to avoid. Where the official HashiCorp skills cover style, this covers what the research aptly calls operational scar tissue — the mistakes that only show up after years of running infrastructure. It is Apache-2.0 licensed, has roughly 2.2k stars, and was updated in July 2026. The source guide describes it as complementary to the HashiCorp skill rather than a rival, and advises picking one as your default since running both mostly duplicates context. For teams evaluating OpenTofu alongside Terraform, its explicit OpenTofu compatibility is a differentiator worth noting.

Best for: Terraform operators who want battle-tested patterns and anti-patterns, including teams considering or using OpenTofu

Pricing: Free and open source (Apache-2.0)

Key features

• Real-world Terraform patterns from a widely recognized community maintainer
• Module structure guidance
• Testing patterns with Terratest
• CI/CD integration patterns
• OpenTofu compatibility
• Production anti-patterns to avoid
• Apache-2.0 licensed, ~2.2k stars, updated July 2026

Pros

• Authored by a highly credible Terraform community figure
• Covers operational realities beyond official style guides
• Explicit OpenTofu support
• Actively maintained with a July 2026 update

Cons

• Overlaps significantly with the HashiCorp official skills — running both duplicates context
• Terraform-only scope
• Community-maintained, so verify current state before relying on it

Visit antonbabenko/terraform-skill

#6 sre-engineer

sre-engineer, from the Jeffallan/claude-skills monorepo (~10.5k repo-wide stars, MIT licensed, updated July 2026), moves your team from 'we monitor things' to a formal reliability practice. According to the repository, it defines SLIs and SLOs, writes error budget policies, designs incident response procedures, builds capacity models and generates Prometheus alerting configs, and covers chaos engineering, toil reduction and on-call runbook generation. In other words, it encodes the practices that separate mature SRE organizations from teams with dashboards nobody reads. If you have been meaning to introduce SLOs for two years, the source guide suggests that installing this skill and asking Claude to draft them for one service is the fastest honest start. Because it covers process as much as tooling, it pairs naturally with monitoring-expert from the same monorepo: one decides what reliability means, the other wires up the evidence. As with other monorepo skills, star counts are repo-wide rather than per-skill.

Best for: Teams formalizing SRE practices — SLOs, error budgets, incident response and runbooks — without hiring a dedicated reliability engineer

Pricing: Free and open source (MIT)

Key features

• SLI and SLO definition workflows
• Error budget policy drafting
• Incident response procedure design
• Capacity modeling
• Prometheus alerting config generation
• Chaos engineering and toil reduction guidance
• On-call runbook generation
• MIT licensed, updated July 2026

Pros

• Covers the full SRE practice, not just tooling
• Practical starting point for teams adopting SLOs
• Pairs well with the monitoring-expert skill from the same repo
• Actively maintained monorepo

Cons

• Process-heavy — less useful if you only need config generation
• Repo-wide star count does not reflect this individual skill's adoption
• Community-maintained without vendor backing

Visit sre-engineer

#7 monitoring-expert

monitoring-expert, from the same Jeffallan/claude-skills monorepo as sre-engineer (~10.5k repo-wide stars, MIT licensed, updated July 2026), covers the full observability triad in one skill. According to the repository, it handles metrics, logs and traces together: Prometheus and Grafana stack configuration, structured logging pipelines, dashboards and alerting rules, and distributed tracing instrumentation. It also extends into load testing with k6 or Artillery, profiling and capacity forecasting — making it broader than a pure monitoring skill. The value of treating the three pillars as a unified design rather than three separate tools is well established in modern observability practice, and this skill encodes that approach for Claude. Pair it with sre-engineer from the same monorepo: that skill decides what reliability means for your service, and this one wires up the evidence to measure it. As with its sibling, star counts are repo-wide rather than per-skill, and it is community-maintained, so a quick review of the SKILL.md before installation is always wise on a credential-heavy workstation.

Best for: Engineers standing up or improving an observability stack who want metrics, logs and traces handled as one coherent design

Pricing: Free and open source (MIT)

Key features

• Prometheus and Grafana stack configuration
• Structured logging pipeline design
• Dashboard and alerting rule creation
• Distributed tracing instrumentation
• Load testing with k6 or Artillery
• Profiling and capacity forecasting
• Unified metrics/logs/traces design approach
• MIT licensed, updated July 2026

Pros

• Full observability triad in a single skill
• Extends beyond monitoring into load testing and capacity forecasting
• Complements sre-engineer for a complete reliability setup
• Actively maintained

Cons

• Broad scope may trigger on adjacent tasks; review trigger wording
• Repo-wide star count does not reflect this skill's individual adoption
• Community-maintained without vendor backing

Visit monitoring-expert

Conclusion

Claude skills have become a genuine force multiplier for DevOps and cloud work in 2026 — HashiCorp now ships first-party Terraform skills, community monorepos offer production-grade Kubernetes and Docker patterns, and packs like cc-devops-skills cover your entire toolchain in generator-validator pairs. But the same research that documents their value also documents their risk: roughly a quarter of catalogued skills carry at least one vulnerability, and about 1 in 9 contain hardcoded or exposed secrets. On a workstation with cloud credentials, that is not a detail to skip. That is exactly the problem SkillzBench is being built to solve — a free, independent directory that security-scans, license-checks and benchmarks every skill in a sandbox, publishes its Lift separately from ratings, and gives you a free MCP connector with per-agent loadouts so your agent only ever sees the skills you chose. The product is pre-launch, but the waitlist is live now. Skills, proven: join the SkillzBench waitlist today and be first in line when vetted, benchmarked DevOps skills go live.

Frequently asked questions

What exactly is a Claude skill?

A Claude skill is a folder containing a SKILL.md file — instructions, plus optional scripts and reference files — that teaches Claude how to do one specific job well, such as structuring a Terraform module or hardening a Kubernetes manifest. Thanks to progressive disclosure, Claude only loads the skill's name and one-line description until your task matches it, so you can install many skills at almost no context cost.

Are skills the same as MCP servers?

No. Skills teach; MCP servers connect. A skill is knowledge and procedure, while an MCP server is a live connection to an external system — your cluster, your cloud account, your GitHub org — giving the agent real data and actions. Most DevOps workflows pair the two: a skill for the patterns and an MCP connection for live state. SkillzBench's free MCP tool is being built to connect any agent to exactly the skills you choose.

How risky is it to install community skills on a DevOps workstation?

Meaningfully risky. Published research (Snyk ToxicSkills, 2026) found that 36.8% of catalogued skills had at least one security flaw and roughly 1 in 9 contained hardcoded or exposed secrets — a serious concern on credential-heavy DevOps machines. Always read the full SKILL.md and any bundled scripts before installing, and prefer verified sources. SkillzBench is being built to automate exactly this verification, with security scans, license checks and versions pinned to commit SHAs.

How do I install a Claude skill?

The modern route is plugin marketplaces inside Claude Code: run /plugin marketplace add <github-owner/repo>, then /plugin install <skill-name>. Alternatively, copy the skill folder into .claude/skills/ in your project (shared via git) or ~/.claude/skills/ (available everywhere on your machine), then reload Claude Code. Every skill in this guide installs the same way.

Do these skills cost anything?

All the skills listed here are free and open source under permissive licenses (MPL-2.0, MIT, or Apache-2.0). SkillzBench is also free — the directory, MCP connector, benchmarks and alerts all come at no cost to users; the platform is pre-launch with a live waitlist, and its revenue comes from blog advertising, never from users or from influencing scores.

What is a 'Lift' score and why does it matter?

Lift, as SkillzBench defines it, is the measurable improvement a skill provides, expressed in percentage points. Every skill is tested in a sandbox with the same model, with and without the skill, so you can see whether it actually helps rather than trusting self-reported claims. Lift is always published separately from ratings, and money never touches scores — advertising revenue never influences listings or trust scores.

Should I install both the HashiCorp Terraform skills and antonbabenko/terraform-skill?

Probably not both at once. According to the research, they are complementary rather than rivals — HashiCorp's official skills cover style and conventions, while antonbabenko's covers operational patterns, testing and OpenTofu compatibility — but running both mostly duplicates context. Pick one as your default based on whether you prioritize vendor-official style or real-world operational patterns.