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Found 651 Skills
Configure host-based firewalls (iptables, nftables, UFW) and cloud security groups (AWS, GCP, Azure) with practical rules for common scenarios like web servers, databases, and bastion hosts. Use when exposing services, hardening servers, or implementing network segmentation with defense-in-depth strategies.
Expert-level Kamal deployment guidance for deploying containerized applications to any server. Use this skill when users ask about Kamal, container deployment, zero-downtime deployments, deploying Rails/web apps to VPS/cloud servers, kamal setup, kamal deploy, Docker deployment without Kubernetes, or deploying to Hetzner/DigitalOcean/AWS with Kamal. Also use when users mention DHH's deployment tool, 37signals deployment, or want an alternative to Heroku/Render/Vercel with self-hosted infrastructure.
Cloud infrastructure and DevOps workflow covering AWS, Azure, GCP, Kubernetes, Terraform, CI/CD, monitoring, and cloud-native development.
Provides comprehensive security review capability for TypeScript and Node.js applications, validates code against XSS, injection, CSRF, JWT/OAuth2 flaws, dependency CVEs, and secrets exposure. Use when performing security audits, before deployment, reviewing authentication/authorization implementations, or ensuring OWASP compliance for Express, NestJS, and Next.js. Triggers on "security review", "check for security issues", "TypeScript security audit".
Audit rapidly generated or AI-produced code for structural flaws, fragility, and production risks.
23 production-ready engineering skills covering architecture, frontend, backend, fullstack, QA, DevOps, security, AI/ML, data engineering, computer vision, and specialized tools like Playwright Pro, Stripe integration, AWS, and MS365. 30+ Python automation tools (all stdlib-only). Works with Claude Code, Codex CLI, and OpenClaw.
Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.
Scans code for security vulnerabilities including injection attacks, authentication flaws, exposed secrets, insecure dependencies, and data exposure. Use when the user says "security review", "is this secure?", "check for vulnerabilities", "audit this", or before deploying to production.
OAuth and OIDC misconfiguration testing playbook. Use when reviewing redirect URI handling, state and nonce validation, PKCE, token audience, callback binding, and identity-provider trust flaws.
Use this skill whenever a user wants to deploy, host, run, or set up any project on a Linux VPS (Virtual Private Server). Triggers include: setting up a Node.js/Python/other app on a server, checking server compatibility with a project, making an app accessible online, fixing port issues, keeping an app running with PM2 or systemd, setting up tunnels (ngrok, localtunnel, pinggy), cloning private GitHub repos to a server, configuring environment variables, managing logs, enabling auto-restart on reboot, dealing with AWS/GCP firewalls, or any combination of these. Always use this skill when the user is working on a remote Linux server and wants to deploy or run any kind of application — even if they don't use the word "VPS" explicitly.
Automates declarative resource creation and provisioning for data pipelines, supporting BigQuery, Dataform, Dataproc, BigQuery Data Transfer Service (DTS), and other resources. It manages environment-specific configurations (dev, staging, prod) through a deployment.yaml file. Use when: - Modifying or creating deployment.yaml for deployment settings. - Resolving environment-specific variables (e.g., Project IDs, Regions) for deployment. - Provisioning supported infrastructure like BigQuery datasets/tables, Dataform resources, or DTS resources via deployment.yaml. Do not use when: - Resources already exist. - Managing resources not supported by `gcloud beta orchestration-pipelines resource-types list`. - Managing general cloud infrastructure (VMs, networks, Kubernetes, IAM policies), which are better suited for Terraform. - Infrastructure spans multiple cloud providers (AWS, Azure, etc.). - Already uses Terraform for the target resources.
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.