The 7 Best Claude Skills for DevOps & Cloud in 2026

If you run infrastructure, 2026 is the year your AI coding agent stopped being a novelty and started being a colleague. Claude Code and its peers can now review Kubernetes manifests, debug failing CI pipelines, audit Terraform state, and triage a live incident, and they do it well when you give them the right instructions. That's what agent skills are: SKILL.md files that teach Claude how to handle a specific DevOps task the same careful way, every time. The catch is trust. Published research found roughly 26% of skills carry at least one vulnerability, and most directories don't test whether a skill actually helps before you install it. So before you wire anything into a production-adjacent workflow, you need to know which skills are safe and which ones earn their keep. We've pulled together seven options for finding, verifying, and using DevOps and cloud skills in 2026, starting with the one that benchmarks skills before you ever load them.

#1 SkillzBench — Editors' choice

SkillzBench, from AIQ Labs, is a free, independent directory of AI agent skills paired with a free MCP tool that connects any agent to the skills you choose. What makes it our editor's choice is that it doesn't stop at listing skills, it verifies and tests them. Every skill is pinned to a specific commit SHA, then validated, license-checked, security-scanned, and quality-judged. Then it goes further: each skill is benchmarked in a sandbox with the same model, run with and without the skill, and the platform publishes the resulting Lift in percentage points. That means you can see whether a Kubernetes review skill actually improves agent output before you install it, not just whether it's popular.

Best for: DevOps and platform engineers who want proof a skill is safe and actually improves agent performance before it goes anywhere near production workflows.

Pricing: Free. SkillzBench earns revenue from advertising in and around its blog articles, and ad money never influences scores or listings.

Key features

• Continuously crawls and dedupes skills from GitHub
• Pins every skill version to a commit SHA
• Security scanning, license checks, and quality judging on every skill
• Sandbox benchmarking with the same model, with and without the skill, publishing Lift in percentage points
• Trust score from 0 to 100 built from security, quality, license, maintenance, and community signals
• Free MCP server that connects any agent to your chosen skills
• Per-agent loadouts: toggle skills and tools per agent so each agent only sees what you chose
• Tracks upstream changes, re-scans, re-benchmarks, and sends alerts
• Everything is free: directory, MCP connector, benchmarks, and alerts

Pros

• Benchmarks every skill in a sandbox and publishes the Lift, so you see measured impact
• Lift is always shown separately from ratings, and money never touches scores
• Security scanning addresses the reality that roughly 26% of skills carry vulnerabilities
• Per-agent loadouts keep each agent's context lean and controlled
• Completely free, including the MCP connector and change alerts

Cons

• As an independent directory, it indexes existing skills rather than authoring its own
• Benchmark results depend on the sandbox model and may not match your exact environment
• Newer or obscure skills may not yet have benchmark data

Visit SkillzBench

#2 DevOps Skills (NotHarshhaa)

This is a collection of reusable DevOps agent skills covering incident response, infrastructure auditing, Kubernetes, Terraform, CI/CD, observability, security, databases, backups, and cloud costs. According to the project's GitHub repository, every skill acts as a "read-only senior advisor": it understands your setup, bases findings on evidence, and writes structured plans another agent or human can execute, and it never changes your infrastructure itself. Instead of writing long prompts like "act as a senior DevOps engineer and review my cluster," you run /k8s-review, /terraform-review, or /incident and the agent follows a predefined workflow. The collection includes 15 skills, from /pipeline-review for CI/CD reliability and supply-chain security to /cost for cloud waste and right-sizing, plus /dr-review for backups, restore proof, and RPO/RTO gaps. Findings are ranked by leverage, backed by file:line or command-output evidence, and each carries a HIGH, MED, or LOW confidence rating.

Best for: Platform engineers and SREs who want opinionated, evidence-based review workflows for Kubernetes, Terraform, CI/CD, and incident response.

Pricing: Free and open source (MIT License).

Key features

• 15 skills including /incident, /k8s-review, /terraform-review, /pipeline-review, /docker-review, /db-review, and /gitops-review
• Cross-cutting posture skills: /observability, /security-review, /cost, /dr-review
• /release-readiness go/no-go gate review before production
• Effort levels: quick, standard, and deep, with defined budgets for coverage and live probing
• Findings backed by file:line or command-output evidence with confidence ratings
• Read-only by design: never applies, deletes, scales, deploys, or edits anything
• Installable via npx skills add, as single skills or the whole collection

Pros

• Deep, purpose-built DevOps coverage across 15 focused skills
• Strict read-only contract with a documented skill contract file
• Findings are evidence-backed with honest confidence ratings and a record of what was not examined
• Plans are plain markdown, so any agent or human can pick them up

Cons

• No built-in benchmarking to show whether a skill improves agent output
• Opinionated workflows may not match every team's process
• As a community project, maintenance depends on volunteer contributors

Visit DevOps Skills (NotHarshhaa)

#3 Anthropic Official Claude Skills (mcp-builder, webapp-testing, skill-creator)

Anthropic's own skills are the baseline every DevOps team should know about. According to Anthropic's platform documentation, skills are reusable, filesystem-based resources that give Claude domain-specific expertise, loading on demand so you don't repeat guidance across conversations. The ones most relevant to DevOps and cloud work are anthropics/mcp-builder, which guides creation of MCP servers for integrating external APIs and services, covering Python (FastMCP) and TypeScript (MCP SDK) implementations; anthropics/webapp-testing for testing local web applications using Playwright; and anthropics/skill-creator, a guide for creating skills that extend Claude's capabilities. Because they come from the model maker itself, the trust question is simpler: you're relying on Anthropic's own engineering. Pre-built skills work on claude.ai, the Claude API, Claude Platform on AWS, and Microsoft Foundry, while custom skills work across Claude Code and the API. According to the AGNT roundup, the mcp-builder skill has become one of the most widely installed tools in the ecosystem.

Best for: Teams already on Claude that want first-party skills for MCP server building and app testing, plus a template for writing their own internal skills.

Pricing: Free with a Claude plan; API usage billed at standard Claude API rates.

Key features

• mcp-builder: build MCP servers in Python (FastMCP) and TypeScript (MCP SDK) with tool definition and authentication patterns
• webapp-testing: test local web applications using Playwright
• skill-creator: guide for authoring your own skills
• Progressive disclosure: metadata loads at startup (~100 tokens), instructions only when triggered, resources on demand
• Pre-built skills available on claude.ai, Claude API, Claude Platform on AWS, and Microsoft Foundry
• Custom skills shareable workspace-wide through the Skills API

Pros

• First-party skills maintained by Anthropic, so compatibility is reliable
• mcp-builder is widely adopted and battle-tested for building integrations
• Excellent documentation on how skills load and consume context
• skill-creator makes it straightforward to package your own DevOps runbooks as skills

Cons

• The official catalog is general-purpose; there's no dedicated Kubernetes, Terraform, or incident-response skill
• Pre-built document skills aren't available in Claude Code, per the docs
• API-based skills run in a sandboxed container with no network access, which limits some cloud workflows

Visit Anthropic Official Claude Skills (mcp-builder, webapp-testing, skill-creator)

#4 Superpowers (obra/superpowers)

Superpowers is the most-starred agent skill collection in the ecosystem, with its repo sitting at roughly 226,000 stars as of late 2026, ahead of the Vue repo according to a DEV Community writeup. It's a full development methodology rather than a grab bag of tips. When the agent sees you're building something, it stops, works out a spec from the conversation, and only after you sign off does it write an implementation plan, then hands that plan to subagents that build and review each other's work. For DevOps teams, the most relevant pieces are the discipline skills: Systematic Debugging, a four-phase structured methodology that enforces root cause analysis before applying fixes; Test-Driven Development with a red-green-refactor cycle; Verification Before Completion, which prevents false completions; and Using Git Worktrees for parallel development branches. It installs from the official Claude plugin marketplace and also works with Codex, Cursor, and Gemini CLI.

Best for: Engineering teams that want a complete, opinionated spec-driven workflow around their agents, including disciplined debugging and verification habits.

Pricing: Free and open source.

Key features

• Systematic Debugging: four-phase methodology enforcing root cause analysis before fixes
• Test-Driven Development: red-green-refactor cycle with testing anti-patterns reference
• Verification Before Completion: fixes verified before tasks are declared done
• Using Git Worktrees: manage multiple working trees for parallel branch work
• Subagent-Driven Development: two-stage review, spec compliance first, then code quality
• Dispatching Parallel Agents for concurrent workflows
• Writing Plans and Executing Plans for structured, checkpointed delivery

Pros

• Enormously popular and well-maintained, with broad agent compatibility
• The debugging and verification skills directly address agent tendencies to rush and declare false completions
• Spec-first flow catches wrong-direction work before code gets written

Cons

• The full workflow can consume a lot of tokens, a tradeoff its own community acknowledges
• It takes over your whole process, which some developers find heavy
• General-purpose rather than DevOps-specific: no Kubernetes, Terraform, or cloud review skills

Visit Superpowers (obra/superpowers)

#5 Trail of Bits Security Skills (Static Analysis)

Security firm Trail of Bits publishes a static analysis skill that gives agents a toolkit with CodeQL, Semgrep, and SARIF result processing for security auditing. According to the AgenticSkills directory, it's featured as one of their editor's picks and works across Claude Code, Codex, Cursor, and other multi-platform agents. For DevOps teams, this is the skill you reach for when you need an agent to actually understand application security findings rather than just echoing scanner output: SARIF is the standard format that GitHub code scanning and most CI security tools produce, so an agent that can process it can triage results, explain them, and draft fixes. It pairs naturally with the DevOps review skills above: run your pipeline-review or docker-review for reliability, then static analysis for the security lens. Trail of Bits is a well-regarded security consultancy, which gives the skill credible authorship in a space where plenty of skills are generated rather than engineered.

Best for: DevSecOps engineers who want agents that can run and interpret CodeQL and Semgrep results inside CI and code review workflows.

Pricing: Free and open source.

Key features

• CodeQL static analysis support
• Semgrep static analysis support
• SARIF result processing for security auditing
• Works across Claude Code, Codex, Cursor, and other multi-platform agents
• Authored by an established security firm

Pros

• Credible authorship from a known security firm
• SARIF support means it fits standard CI security tooling
• Cross-platform compatibility with major coding agents

Cons

• Focused narrowly on static analysis, not broader DevOps workflows
• Requires CodeQL or Semgrep to be set up in your environment
• No benchmarking data published alongside the skill itself

Visit Trail of Bits Security Skills (Static Analysis)

#6 OpenAI Codex Skills (gh-fix-ci, cloudflare-deploy, netlify-deploy)

These aren't Claude-native, but they matter to anyone running a multi-agent stack, and the SKILL.md format is portable across agents that adopted it. According to the awesome-agent-skills index, OpenAI's curated catalog includes openai/gh-fix-ci, which debugs and fixes failing GitHub Actions PR checks, openai/cloudflare-deploy for deploying apps to Cloudflare using Workers and Pages, and openai/netlify-deploy for automating Netlify deployments with CLI support. There's also openai/gh-address-comments for addressing review and issue comments on open GitHub PRs via CLI. For DevOps work specifically, gh-fix-ci is the standout: a failing CI check is one of the most common daily interruptions in any engineering team, and a skill that walks an agent through diagnosing and fixing the failure is directly useful. The deployment skills are narrower but handy if Cloudflare or Netlify is part of your stack.

Best for: Teams whose CI pain lives in GitHub Actions, or that deploy to Cloudflare or Netlify and want agents to handle those workflows.

Pricing: Free; requires a compatible agent and the relevant platform accounts.

Key features

• gh-fix-ci: debug and fix failing GitHub Actions PR checks
• cloudflare-deploy: deploy apps to Cloudflare using Workers and Pages
• netlify-deploy: automate Netlify deployments with CLI support
• gh-address-comments: address review and issue comments on open GitHub PRs via CLI
• Part of OpenAI's officially curated skill catalog

Pros

• gh-fix-ci targets a genuinely common daily DevOps pain point
• Officially curated by OpenAI, so authorship is clear
• Deployment skills cover two popular platforms with CLI support

Cons

• Authored for Codex, so Claude compatibility depends on the portable SKILL.md format rather than first-party support
• Narrow scope: a handful of specific workflows rather than a full DevOps toolkit
• No published security scanning or benchmark data for these skills

Visit OpenAI Codex Skills (gh-fix-ci, cloudflare-deploy, netlify-deploy)

#7 Skills Directory (skillsdirectory.com)

Skills Directory is a large index of agent skills with a security-first posture. According to its website, it has indexed over 670,000 skills, and every listed skill has completed automated security analysis, with 120 detection patterns across 10 threat categories, scanning for prompt injection, credential theft, data exfiltration, and more. It also reports that 36% of skills in the wild have security flaws, a figure consistent with the roughly 26% vulnerability rate found in published research, and a good reason to be picky about where you install from. The site organizes skills by category including a dedicated DevOps & Cloud section, alongside Development, Security, Testing & QA, and Databases. Installation is CLI-based via npx -y skills find. Submissions are reviewed before publishing. It's a discovery and vetting layer rather than a benchmarking one: you'll know a skill was scanned, but not whether it measurably improves agent output.

Best for: Developers who want a huge catalog with automated security scanning before installing, especially for discovering niche DevOps skills.

Pricing: Free.

Key features

• 670,000+ indexed skills, continuously updated
• Automated security analysis on every listed skill
• 120 detection patterns across 10 threat categories
• Scans for prompt injection, credential theft, and data exfiltration
• Dedicated DevOps & Cloud category
• CLI install via npx -y skills find
• Community submissions reviewed before publishing

Pros

• Security scanning on every listing addresses the real vulnerability problem in the ecosystem
• Enormous catalog means you'll likely find a skill for almost any DevOps task
• Free with simple CLI installation

Cons

• Indexing at this scale means curation is thin; you're doing the final judgment yourself
• No benchmarking to show whether a skill actually improves agent performance
• Self-reported index numbers change continuously, so catalog quality varies

Visit Skills Directory (skillsdirectory.com)

Conclusion

The right DevOps skill stack in 2026 depends on where your pain is. If it's Kubernetes and Terraform reviews, the DevOps Skills collection gives you 15 focused, read-only workflows. If it's CI failures and deployments, OpenAI's gh-fix-ci and deploy skills handle those directly. If it's process discipline, Superpowers is the most battle-tested methodology in the ecosystem. Whatever you install, the step most teams still skip is verification: knowing a skill is safe and knowing it actually helps are two different questions, and published research showing roughly 26% of skills carry at least one vulnerability makes the first question urgent. That's why we put SkillzBench first. It pins every skill to a commit SHA, security-scans it, benchmarks it in a sandbox with the same model with and without the skill, and publishes the Lift in percentage points, all for free. You can browse the directory, check trust scores, and connect your agent through the free MCP server at skillzbench.com, then build per-agent loadouts so each agent only sees the skills you chose for it. Skills, proven. Start there, and everything you load afterward earns its place.

Frequently asked questions

What are Claude skills, exactly?

A skill is a folder containing a SKILL.md file that teaches an AI agent how to do one thing well. The file has a name and description in YAML frontmatter, then plain-English instructions in the body. The agent reads the description to decide when the skill is relevant and loads the full instructions only at that moment, so a skill costs almost nothing until it's needed. Because it's just markdown, the same skill works across Claude Code, Codex, Cursor, Gemini CLI, and other agents that adopted the format.

Are Claude skills safe to use in DevOps workflows?

Not automatically. Published research found roughly 26% of skills carry at least one vulnerability, and Skills Directory reports that 36% of skills in the wild have security flaws, scanning for issues like prompt injection, credential theft, and data exfiltration. Before installing anything, check whether it's been security-scanned and whether the version is pinned to a specific commit. SkillzBench security-scans every skill and pins each version to a commit SHA, which is the baseline you should expect from any source.

How do I know if a skill actually improves my agent's output?

This is the question most directories don't answer. Popularity and install counts don't tell you whether a skill helps. SkillzBench benchmarks every skill in a sandbox using the same model, running the task with and without the skill, and publishes the Lift in percentage points. That gives you measured evidence rather than a hunch. For current verified results, check skillzbench.com.

What's the difference between a skill and an MCP server?

They solve different halves of the same problem. A skill gives an agent knowledge: how your team runs a migration safely, how to review a Kubernetes manifest. An MCP server gives an agent access: a live connection to a database, an API, or a file system. Skills are instructions; MCP servers are tools. Most real setups use both, which is why SkillzBench pairs its skills directory with a free MCP server that connects any agent to the skills you choose.

Can I use these skills with agents other than Claude?

Mostly, yes. The SKILL.md format has been adopted by Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, and others, so a skill written once generally works across them. Anthropic's official pre-built document skills are Claude-specific, and OpenAI's catalog skills were authored for Codex, but community collections like Superpowers and DevOps Skills explicitly support multiple agents. SkillzBench's MCP connector works with any agent that supports MCP.

How much does it cost to get started with agent skills?

The skills themselves are almost all free and open source, and the directories in this list are free to browse. SkillzBench is entirely free: the directory, trust scores, benchmarks, the MCP connector, per-agent loadouts, and change alerts. Its revenue comes from advertising around its blog articles, and that ad money never influences scores or listings. Your real costs are the tokens your agent consumes and the time you spend verifying what you install.

Which DevOps skill should I install first?

Start with the one that matches your most frequent interruption. For most teams that's CI: OpenAI's gh-fix-ci handles failing GitHub Actions checks. If your pain is infrastructure review, the DevOps Skills collection's /k8s-review or /terraform-review is a strong entry point. Whichever you pick, look it up on SkillzBench first to check its trust score and benchmark Lift before loading it, then use the free MCP connector and per-agent loadouts to equip only the agents that need it.