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Found 384 Skills
Workflows for generating terraform solution that are the composition of one or several Terraform IBM Modules (TIM). Use when working with IBM Cloud infrastructure as code, Terraform modules, infrastructure automation, or cloud resource provisioning. Provides workflows for module discovery, composition patterns, code generation, and validation. Essential for tasks involving IBM Cloud VPC, compute, networking, security, databases, observability, or any IBM Cloud service deployment. Triggers on keywords like "terraform", "IBM Cloud", "infrastructure", "IaC", "modules", "deploy", "provision", or specific IBM Cloud services (VPC, VSI, OpenShift, etc.).
Expert guidance for building production-ready FastAPI applications with modular architecture where each business domain is an independent module with own routes, models, schemas, services, cache, and migrations. Uses UV + pyproject.toml for modern Python dependency management, project name subdirectory for clean workspace organization, structlog (JSON+colored logging), pydantic-settings configuration, auto-discovery module loader, async SQLAlchemy with PostgreSQL, per-module Alembic migrations, Redis/memory cache with module-specific namespaces, central httpx client, OpenTelemetry/Prometheus observability, conversation ID tracking (X-Conversation-ID header+cookie), conditional Keycloak/app-based RBAC authentication, DDD/clean code principles, and automation scripts for rapid module development. Use when user requests FastAPI project setup, modular architecture, independent module development, microservice architecture, async database operations, caching strategies, logging patterns, configuration management, authentication systems, observability implementation, or enterprise Python web services. Supports max 3-4 route nesting depth, cache invalidation patterns, inter-module communication via service layer, and comprehensive error handling workflows.
Docs as QA: audit doc coverage and freshness, validate runbooks, and maintain documentation quality gates for APIs, services, events, and operational workflows. Includes AI-assisted audits, observability patterns, and automated coverage tracking.
Implements comprehensive observability with OpenTelemetry tracing, Prometheus metrics, and structured logging. Includes instrumentation plans, sample dashboards, and alert candidates. Use for "observability", "monitoring", "tracing", or "metrics".
Set up comprehensive observability for Databricks with metrics, traces, and alerts. Use when implementing monitoring for Databricks jobs, setting up dashboards, or configuring alerting for pipeline health. Trigger with phrases like "databricks monitoring", "databricks metrics", "databricks observability", "monitor databricks", "databricks alerts", "databricks logging".
Expert guidance for designing, implementing, and maintaining cloud infrastructure using Experience in Infrastructure as Code (IaC) principles. Use this skill for architecting cloud solutions, setting up CI/CD pipelines, implementing observability, and following SRE best practices.
Senior Java & Spring Boot 4 / Spring Framework 7 architect skill for 2026-standard development. Use when the user asks to build, scaffold, design, review, or explain Java applications using Spring Boot 4.x, Spring Framework 7.x, Spring Modulith, or any related Spring ecosystem project. Triggers include: creating REST APIs, designing microservices, configuring data access (JdbcClient, JPA 3.2, R2DBC), reactive programming (WebFlux), security (Spring Security 7), observability, GraalVM native images, Gradle/Maven build configuration, Jakarta EE 11 migration, and any task requiring idiomatic modern Java (Java 25: records, sealed classes, structured concurrency, scoped values, pattern matching, JSpecify null safety).
Configures .NET CI/CD pipelines (GitHub Actions with setup-dotnet, NuGet cache, reusable workflows; Azure DevOps with DotNetCoreCLI, templates, multi-stage), containerization (multi-stage Dockerfiles, Compose, rootless), packaging (NuGet authoring, source generators, MSIX signing), release management (NBGV, SemVer, changelogs, GitHub Releases), and observability (OpenTelemetry, health checks, structured logging, PII). Spans 18 topic areas. Do not use for application-layer API or UI implementation patterns.
Spring Modulith for modular architecture in Spring Boot 3.x. Covers module structure, API vs internal packages, inter-module events, module testing, documentation generation, and observability. USE WHEN: user mentions "spring modulith", "modular monolith", "@ApplicationModule", "module boundaries", "inter-module events", "@ApplicationModuleTest", "modular architecture" DO NOT USE FOR: simple applications - unnecessary complexity, microservices - use proper service boundaries, existing tightly coupled monoliths - requires significant refactoring
Investigate incidents, debug performance issues, analyze logs, and manage observability resources in Dynatrace using the dtctl CLI. Use this skill whenever the user asks about error rates, latency spikes, service health, crash-looping pods, web vitals, SLO status, open problems, root cause analysis, log patterns, trace analysis, or building dashboards — even if they don't mention Dynatrace by name. Also covers DQL queries, workflow management, notebook and dashboard creation, settings configuration, and any operations against a Dynatrace environment.
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; add tracing or @observe; send results to Confident AI; monitor production; run online evals; inspect traces; or iterate on prompts, tools, retrieval, or agent behavior from eval failures. AI agents are the primary use case. Covers Python SDK, pytest eval suites, CLI generation, tracing, Confident AI reporting, and agent-driven improvement loops. DO NOT TRIGGER for unrelated generic pytest, non-AI test setup, or non-DeepEval observability work unless the user asks to compare or migrate to DeepEval.
Comprehensive testing doctrine for software and AI systems — covers positive patterns, anti-patterns, gates for coding agents writing tests, CI discipline, and an LLM/agent evaluation primer. Use when authoring or reviewing tests, adding mocks, deciding test placement, generating tests via agents, debugging flaky CI, designing eval suites for LLM features, or rebuilding a brittle test suite. Contains 12 positive patterns (selector hierarchy, table-driven, builders, real-system gates), 25 anti-patterns across Brittleness, Flakiness, Mock-misuse, Process, and AI-specific families, 7 mandatory gates for agents writing tests, flaky-test taxonomy with quarantine workflow, contract / property / mutation testing patterns, and an oracle-ladder primer for LLM-as-judge and agent eval. Language-agnostic — pseudo-code only. Don't use for general code review, library-specific debugging unrelated to tests, non-testing CI pipeline design, or production observability.