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Found 819 Skills
Analyze and optimize cloud infrastructure costs, identify waste, and track spend efficiency
Elasticsearch and Elastic APM integration with Serilog structured logging for .NET applications. Use when: (1) Implementing or configuring Serilog with Elasticsearch sink, (2) Setting up Elastic APM with data streams and authentication, (3) Creating logging extension methods in Infrastructure layer, (4) Enriching logs with app-name and app-type properties, (5) Configuring log levels and environment-specific logging, (6) Questions about logging security (PII, credentials), or (7) Troubleshooting observability and monitoring setup.
Guide for implementing formatting rules using Biome's IR-based formatter infrastructure. Use when working on formatters for JavaScript, CSS, JSON, HTML, or other languages. Examples:<example>User needs to implement formatting for a new syntax node</example><example>User wants to handle comments in formatted output</example><example>User is comparing Biome's formatting against Prettier</example>
Configures and manages Depot CI, a drop-in replacement for GitHub Actions that runs workflows entirely within Depot. Use when migrating GitHub Actions workflows to Depot CI, running `depot ci migrate`, managing Depot CI secrets and variables, running workflows with `depot ci run`, debugging Depot CI runs, checking workflow compatibility, or understanding Depot CI's current beta limitations. Also use when the user mentions .depot/ directory, depot ci commands, or asks about running GitHub Actions workflows on Depot's infrastructure without GitHub-hosted runners. NOTE: Depot CI is currently in beta with limited availability.
Cal.com self-hosted deployment to GCP Cloud Run with Supabase PostgreSQL. Docker Compose for local dev. TRIGGERS - deploy calcom, cloud run, self-hosted, docker compose, supabase, gcp deploy, infrastructure, cal.com hosting.
Provides strategic insights on AI-driven software democratization and agent-based development trends from Replit's perspective. Use when discussing the future of software engineering, AI agent infrastructure requirements, democratization of coding, or when analyzing how AI will transform software creation from expert-only to universal access. Triggers include questions about software engineering automation trends, agent sandbox environments, SWE-bench benchmarks, or strategic implications of AI coding assistants for startups and enterprises.
Microsoft Azure expert for az CLI, AKS, App Service, and cloud infrastructure
Consult this skill for Python testing implementation and patterns. Use when writing unit tests, setting up test suites, implementing TDD, configuring pytest, creating fixtures, async testing, writing integration tests, mocking dependencies, parameterizing tests, setting up CI/CD testing. Do not use when evaluating test quality - use pensive:test-review instead. DO NOT use when: infrastructure test config - use leyline:pytest-config.
Go testing patterns for production-grade code: subtests, test helpers, fixtures, golden files, httptest, testcontainers, property-based testing, and fuzz testing. Covers mocking strategies, test isolation, coverage analysis, and test design philosophy. Use when writing tests, improving coverage, reviewing test quality, setting up test infrastructure, or choosing a testing approach. Trigger examples: "add tests", "improve coverage", "write tests for this", "test helpers", "mock this dependency", "integration test", "fuzz test". Do NOT use for performance benchmarking methodology (use go-performance-review), security testing (use go-security-audit), or table-driven test patterns specifically (use go-test-table-driven).
Use when starting a new project, adding a new agent to an existing system, or setting up workflow infrastructure from scratch.
Comprehensive guide to why and how AI agents should use email. Use when evaluating whether an agent needs email, comparing email infrastructure options (AgentMail vs Gmail API vs Resend vs SendGrid vs SES), understanding security risks like prompt injection via email and OAuth credential exposure, or exploring common agent email use cases such as customer support agents, sales outreach, verification flows, and browser automation.
Workflow for learning CuTe Python DSL by reading, importing, profiling, and extracting reusable patterns from CUTLASS Blackwell example kernels. Use when: (1) studying CUTLASS CuTe DSL reference implementations, (2) importing CUTLASS examples into the project runtime infrastructure, (3) building CuTe DSL knowledge base entries from profiling experiments, (4) understanding CuTe DSL API patterns, TMA pipelining, warpgroup scheduling, or persistent kernel structure.