Skills
The open registry for AI agent skills — structured prompts and workflows with recommended models, example prompts, and compatible tools.
Skills
17
Categories
9
Compatible tools
5
Contributors
1
Showing 1–17 of 17 skills
Writes idempotent Ansible playbooks and roles from a described target state. Structures tasks with proper handlers, variables, and templates; favors modules over shell commands; applies role-based layout and inventory grouping; and adds check-mode safety and tags. Produces playbooks that are re-runnable without unintended side effects.
Produces operational runbooks for services and common incidents. Documents prerequisites, step-by-step diagnosis and remediation, exact commands, verification checks, rollback steps, and escalation paths. Structures each runbook so an on-call engineer can follow it under pressure, and keeps destructive steps clearly flagged with safeguards.
Produces production-ready nginx configuration for common scenarios: reverse proxy, load balancing, TLS termination, static file serving, HTTP/2, gzip/brotli, caching, rate limiting, and security headers. Explains each directive, warns about risky defaults, and includes a validation step so the config can be tested before reload.
Writes reusable, well-structured Terraform modules with clean input/output interfaces, sensible defaults, validation rules, and examples. Covers module composition, variable typing and validation, remote state and backends, provider version pinning, and testing with terraform validate/plan and tools like Terratest. Emphasizes least-privilege IAM and safe defaults.
Adds production-grade observability to a codebase by instrumenting it with structured logs, metrics, and distributed traces. Recommends span boundaries, cardinality-safe labels, and OpenTelemetry conventions, then generates the wiring code and dashboards needed to make a service debuggable in production.
Generates and refactors Helm charts to package Kubernetes applications. Produces templated manifests, values.yaml with sensible defaults, helpers, chart dependencies, and hooks, and parameterizes images, resources, probes, and ingress so a workload can be deployed consistently across environments.
Reviews and rewrites Dockerfiles for smaller images, faster builds, and better security. Applies multi-stage builds, optimal layer ordering and caching, minimal base images, non-root users, and .dockerignore tuning, and flags vulnerabilities and bloat while keeping the build reproducible.
Designs feature-flagging and progressive-delivery strategies and generates the integration code. Covers flag naming and lifecycle, targeting and segmentation rules, percentage rollouts, kill switches, and cleanup of stale flags across providers like LaunchDarkly, Unleash, Flagsmith, or a homegrown config service.
Parses and analyzes application, system, and access logs to surface errors, anomalies, and root causes. Correlates events across services, identifies recurring patterns and spikes, extracts structured fields from unstructured lines, and produces a prioritized summary with likely causes and recommended next steps.
Generates production-ready Kubernetes manifests — Deployments, Services, Ingresses, ConfigMaps, Secrets, HPAs, and more — from a plain-language description of the workload. Applies best practices for resource limits, health probes, security contexts, and rolling update strategies, with optional Kustomize overlays or Helm chart scaffolding.
Generates and assists with incident response procedures for production systems. Helps with root cause analysis, creates runbooks for common failure modes, builds communication templates for stakeholders, and produces post-incident review documents. Supports SRE and on-call workflows.
Generates production-ready infrastructure as code (IaC) configurations for cloud deployments. Supports Terraform, Pulumi, CloudFormation, and CDK. Creates modular, reusable infrastructure components with proper networking, security groups, IAM policies, and monitoring configurations.
Builds and explains cron expressions from natural language schedules. Supports standard cron (5-field), extended cron (6-field with seconds), and cloud-specific formats (AWS EventBridge, Google Cloud Scheduler). Validates expressions and shows next run times.
Plans and generates migration strategies for framework upgrades, language versions, database changes, and architecture shifts. Produces step-by-step migration guides with rollback plans, risk assessment, and automated codemods where possible.
Generates .env files, configuration schemas, and environment variable documentation from application requirements. Includes validation rules, default values, required vs optional flags, and example values. Supports multiple environments (dev/staging/prod).
Generates Docker Compose configurations from application requirements. Handles service dependencies, networking, volumes, health checks, environment variables, and multi-stage builds. Supports development and production profiles.
Designs and implements CI/CD pipelines for various platforms (GitHub Actions, GitLab CI, Jenkins, CircleCI). Covers build, test, lint, security scan, deploy stages with proper caching, parallelization, and environment management.
Skills vs MCP servers
what's the difference?Skillsthe “what to do”
A skillA reusable, structured prompt/workflow with recommended models, an example prompt, and compatible tools. packages know-how — instructions, an example promptA ready-to-use prompt template that demonstrates how to invoke the skill., and recommended models — so an agent performs a task consistently. Skills add knowledge, not new connections.
MCP serversthe “how to connect”
An MCP serverModel Context Protocol server — a standard way to expose tools, resources, and prompts to AI agents and IDEs. gives an agent new capabilities by connecting it to real systems (databases, APIs, files) over a transportHow the client talks to the server: stdio (local process), SSE, or HTTP streaming.. MCP adds connections and actions, not task instructions.
Rule of thumb: reach for a skill when you need the model to do a task well, and an MCP server when you need it to reach a tool or system. They compose — a skill can rely on tools an MCP server provides.
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