Skill UI

This expert skill specializes in guiding comprehensive GitHub issue resolution. It covers systematic bug investigation, feature implementation, and collaborative development workflows. Expertise includes issue triage, root cause analysis, test-driven development, and professional pull request management, ensuring vague reports are transformed into production-ready code following modern CI/CD practices.

This skill provides a robust system for tracking all code modifications using structured JSON records and enforcing a defined state machine (e.g., planned -> deployed). It is critical for AI coding workflows, ensuring that if a session expires or is abandoned, the next session receives the full context, progress summary, and blockers to resume seamlessly. Ideal for complex, multi-stage development projects and onboarding new codebases.

This skill helps create detailed step-by-step technical tutorials, quickstarts, and code walkthroughs. It focuses on clear prerequisite handling, environment setup instructions, and troubleshooting sections to ensure users achieve successful results. Suitable for developers needing structured guidance on software implementation and project initialization.

This skill provides comprehensive integration with the Telegram Bot API, covering setup via BotFather, message handling, webhooks, inline keyboards, groups, and channels. It includes boilerplate code for Node.js and Python to help developers quickly build and deploy messaging bots without starting from scratch.

This skill provides comprehensive testing approaches for Temporal workflows using pytest. It covers unit testing with time-skipping, integration testing with mocked activities, and replay testing for determinism validation. Resources guide local development setup, CI/CD integration, and achieving high code coverage targets. Ideal for validating Python SDK workflows efficiently.

Provision and manage AWS cloud infrastructure using Terraform Infrastructure as Code (IaC). Create reusable modules, manage state files, and implement best practices for declarative resource deployment. Suitable for DevOps workflows requiring automated server, network, and storage setup.

A comprehensive, multi-phase workflow designed for Infrastructure as Code (IaC) using Terraform. It guides users through provisioning cloud resources, creating reusable modules, managing complex state, handling multi-environment deployments, and integrating robust CI/CD pipelines and security practices. Ideal for establishing scalable, repeatable, and auditable cloud infrastructure.

This skill provides production-ready Terraform module patterns for AWS, Azure, and GCP infrastructure. It helps build reusable infrastructure components, standardize cloud resource provisioning, and implement infrastructure as code best practices. Users can create multi-cloud compatible modules and establish organizational Terraform standards. Includes guidance on module structure, variables, outputs, and testing with Terratest.

Expert in Terraform and OpenTofu for Infrastructure as Code (IaC). Covers advanced module design, state management, multi-cloud deployments, and CI/CD automation. Ensures secure, scalable, and compliant enterprise infrastructure patterns through policy-as-code and GitOps workflows.

This skill provides a systematic, multi-step approach to identify and resolve all failing unit tests within a codebase. It guides the user through smart error grouping (e.g., grouping by ImportError or AttributeError), prioritizing fixes from infrastructure to business logic, and ensuring thorough verification to prevent regressions. Ideal for use after major refactoring, debugging CI/CD failures, or when explicitly tasked with making the test suite pass.

This skill reviews generated or changed test code against universal testing rules before deployment. It prevents common failure modes like mock-heavy unit tests, near-duplicate test bodies, and tests that re-verify the framework. It enforces best practices such as testing behavior over implementation, justifying mocks at system boundaries, and ensuring every test has a clear justification. Use it when reviewing diffs, new test files, or when writing new tests to ensure high-quality, maintainable test suites.

Build end-to-end tests with TestDriver.ai, an AI code reviewer that runs every pull request in a real desktop sandbox, finds bugs, and creates regression tests for web, Electron, and native apps.