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Agent skills
full-stack

Topic: full-stack

136 skills in this topic.

providing-feedback Implements feedback and notification systems including toasts, alerts, modals, progress indicators, and error states. Use when communicating system state, displaying messages, confirming actions, or showing errors.
ancoleman/ai-design-components 333
managing-secrets Managing secrets (API keys, database credentials, certificates) with Vault, cloud providers, and Kubernetes. Use when storing sensitive data, rotating credentials, syncing secrets to Kubernetes, implementing dynamic secrets, or scanning code for leaked secrets.
ancoleman/ai-design-components 333
securing-authentication Authentication, authorization, and API security implementation. Use when building user systems, protecting APIs, or implementing access control. Covers OAuth 2.1/OIDC, JWT patterns, sessions, Passkeys/WebAuthn, RBAC/ABAC/ReBAC, policy engines (OPA, Casbin, SpiceDB), managed auth (Clerk, Auth0), self-hosted (Keycloak, Ory), and API security best practices.
ancoleman/ai-design-components 333
security-hardening Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.
ancoleman/ai-design-components 333
shell-scripting Write robust, portable shell scripts with proper error handling, argument parsing, and testing. Use when automating system tasks, building CI/CD scripts, or creating container entrypoints.
ancoleman/ai-design-components 333
siem-logging Configure security information and event management (SIEM) systems for threat detection, log aggregation, and compliance. Use when implementing centralized security logging, writing detection rules, or meeting audit requirements across cloud and on-premise infrastructure.
ancoleman/ai-design-components 333
streaming-data Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
ancoleman/ai-design-components 333
testing-strategies Strategic guidance for choosing and implementing testing approaches across the test pyramid. Use when building comprehensive test suites that balance unit, integration, E2E, and contract testing for optimal speed and confidence. Covers multi-language patterns (TypeScript, Python, Go, Rust) and modern best practices including property-based testing, test data management, and CI/CD integration.
ancoleman/ai-design-components 333
theming-components Provides design token system and theming framework for consistent, customizable UI styling across all components. Covers complete token taxonomy (color, typography, spacing, shadows, borders, motion, z-index), theme switching (CSS custom properties, theme providers), RTL/i18n support (CSS logical properties), and accessibility (WCAG contrast, high contrast themes, reduced motion). This is the foundational styling layer referenced by ALL component skills. Use when theming components, implementing light/dark mode, creating brand styles, customizing visual design, ensuring design consistency, or supporting RTL languages.
ancoleman/ai-design-components 333
transforming-data Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.
ancoleman/ai-design-components 333
using-document-databases Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.
ancoleman/ai-design-components 333
using-graph-databases Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.
ancoleman/ai-design-components 333
using-relational-databases Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).
ancoleman/ai-design-components 333
using-timeseries-databases Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.
ancoleman/ai-design-components 333
writing-infrastructure-code Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.
ancoleman/ai-design-components 333
canvas-design Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
AgiFlow/aicode-toolkit 151