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Found 94 Skills
Search and deploy services from Railway's template marketplace. Use when user wants to add a service from a template, find templates for a specific use case, or deploy tools like Ghost, Strapi, n8n, Minio, Uptime Kuma, etc. For databases (Postgres, Redis, MySQL, MongoDB), prefer the railway-database skill.
Complete E2E (end-to-end) and integration testing skill for TypeScript/NestJS projects using Jest, real infrastructure via Docker, and GWT pattern. ALWAYS use this skill when user needs to: **SETUP** - Initialize or configure E2E testing infrastructure: - Set up E2E testing for a new project - Configure docker-compose for testing (Kafka, PostgreSQL, MongoDB, Redis) - Create jest-e2e.config.ts or E2E Jest configuration - Set up test helpers for database, Kafka, or Redis - Configure .env.e2e environment variables - Create test/e2e directory structure **WRITE** - Create or add E2E/integration tests: - Write, create, add, or generate e2e tests or integration tests - Test API endpoints, workflows, or complete features end-to-end - Test with real databases, message brokers, or external services - Test Kafka consumers/producers, event-driven workflows - Working on any file ending in .e2e-spec.ts or in test/e2e/ directory - Use GWT (Given-When-Then) pattern for tests **REVIEW** - Audit or evaluate E2E tests: - Review existing E2E tests for quality - Check test isolation and cleanup patterns - Audit GWT pattern compliance - Evaluate assertion quality and specificity - Check for anti-patterns (multiple WHEN actions, conditional assertions) **RUN** - Execute or analyze E2E test results: - Run E2E tests - Start/stop Docker infrastructure for testing - Analyze E2E test results - Verify Docker services are healthy - Interpret test output and failures **DEBUG** - Fix failing or flaky E2E tests: - Fix failing E2E tests - Debug flaky tests or test isolation issues - Troubleshoot connection errors (database, Kafka, Redis) - Fix timeout issues or async operation failures - Diagnose race conditions or state leakage - Debug Kafka message consumption issues **OPTIMIZE** - Improve E2E test performance: - Speed up slow E2E tests - Optimize Docker infrastructure startup - Replace fixed waits with smart polling - Reduce beforeEach cleanup time - Improve test parallelization where safe Keywords: e2e, end-to-end, integration test, e2e-spec.ts, test/e2e, Jest, supertest, NestJS, Kafka, Redpanda, PostgreSQL, MongoDB, Redis, docker-compose, GWT pattern, Given-When-Then, real infrastructure, test isolation, flaky test, MSW, nock, waitForMessages, fix e2e, debug e2e, run e2e, review e2e, optimize e2e, setup e2e
Database design specialist for schema modeling, query optimization, indexing strategies, and data integrityUse when "database design, schema, indexes, query optimization, migrations, normalization, database scaling, foreign keys, data modeling, database, sql, postgres, mysql, mongodb, schema, indexes, migrations, normalization, optimization" mentioned.
Implement the Syncfusion Angular Query Builder component for visual query construction, dynamic filtering, and rule management. Use this when building visual filter UIs, creating query conditions programmatically, importing/exporting SQL or MongoDB queries, or customizing query builder templates. This skill covers data binding, column configuration with operators, rule groups, theming, and Angular forms integration for the QueryBuilder (ejs-querybuilder) component.
Guide for configuring Infisical Dynamic Secrets — on-demand, short-lived credentials for databases, cloud IAM, SSH, and Kubernetes. Covers 27 providers including PostgreSQL, MySQL, Redis, MongoDB, AWS IAM, GCP IAM, SSH certificates, Kubernetes service accounts, and more. Use this skill when someone asks about: dynamic secrets, ephemeral database credentials, short-lived tokens, rotating database users, dynamic PostgreSQL/MySQL/Redis credentials, SSH certificates, temporary AWS IAM users, or 'how do I generate temporary credentials with Infisical'.
World-class backend engineering - distributed systems, database architecture, API design, and the battle scars from scaling systems that handle millions of requestsUse when "backend, api, database, postgres, mysql, mongodb, redis, graphql, rest, authentication, authorization, caching, queue, background job, webhook, migration, transaction, n+1, rate limit, server, node.js, python, go, backend, api, database, architecture, performance, reliability, security" mentioned.
Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.
Wire the Prisma Next runtime — `db.ts` setup using `postgres<Contract>(...)` from `@prisma-next/postgres/runtime`, middleware composition (telemetry from `@prisma-next/middleware-telemetry`; lints and budgets), `DATABASE_URL` config, per-environment branching, switching between Postgres and Mongo façades. Use for db.ts, postgres(), mongo(), middleware, telemetry, lints, budgets, DATABASE_URL, .env, connection pool, poolOptions, dev vs prod config, transactions, db.transaction, read replicas, multi-database, script won't exit, hangs, close connection, db.end, db.close, pool.end, [Symbol.asyncDispose], await using.
Fetches official documentation for external libraries and frameworks (React, Next.js, Prisma, FastAPI, Express, Tailwind, MongoDB, etc.) with 60-90% token savings via content-type filtering. Use this skill when implementing features using library APIs, debugging library-specific errors, troubleshooting configuration issues, installing or setting up frameworks, integrating third-party packages, upgrading between library versions, or looking up correct API patterns and best practices. Triggers automatically during coding work - fetch docs before writing library code to get correct patterns, not after guessing wrong.
Automatically generate complete Python project deliverables from natural language requirements through collaboration among four virtual roles: autonomous learning, PM, architect, and senior programmer. Supports feature expansion, project refactoring, and skill invocation. Also supports web search, knowledge integration, version control, Python 3.11+ features, UV package management, loguru logging, and project size adaptation (folder/single file). It provides support for database design and implementation (SQLite, PostgreSQL, MongoDB, vector databases, graph databases), data layer abstraction (Repository pattern), and database switching. Suitable for scenarios such as software requirement clarification, rapid prototyping, project initialization, feature expansion, and code refactoring.
Analyzes and optimizes SQL/NoSQL queries for performance. Use when reviewing query performance, optimizing slow queries, analyzing EXPLAIN output, suggesting indexes, identifying N+1 problems, recommending query rewrites, or improving database access patterns. Supports PostgreSQL, MySQL, SQLite, MongoDB, Redis, DynamoDB, and Elasticsearch.
Deploys infrastructure components via Helm charts on TrueFoundry. Supports any public or private OCI Helm chart including databases (Postgres, MongoDB, Redis), message brokers (Kafka, RabbitMQ), and vector databases (Qdrant, Milvus). Uses YAML manifests with `tfy apply`. Use when installing Helm charts or deploying infrastructure on TrueFoundry.