Skill UI

Mandates Taskwarrior integration for every coding request by decomposing work into tracked tasks, starting them, syncing Timewarrior time tracking, and enforcing the full add→start→done lifecycle before writing code.

Automates creation of A/B test configurations for ML deployment, guiding users through best practices, code samples, and validation steps so production models are served with monitoring and rollout control.

Automates creation of acceptance criteria within enterprise workflows, guiding through best practices, generating code and configurations, and validating outputs against standards whenever acceptance criteria creator requests arrive.

Provides WCAG 2.1 based accessibility audit guidance for web interfaces, covering perceivable, operable, understandable, and robust principles along with code snippets for alt text, contrast, keyboard, focus, and media alternatives.

Automates adapting pretrained models via transfer learning by analyzing requirements, generating PyTorch or TensorFlow fine-tuning code, validating data, tracking KPIs, and saving artifacts so you can quickly reuse existing models for new datasets and tasks.

This skill outlines a systematic workflow for addressing review comments and issue feedback on GitHub Pull Requests (PRs) using the GitHub CLI (`gh`). It guides users through inspecting comments, planning fixes, applying code changes, and finally responding to the threads as resolved, ensuring a streamlined code review process.

AddressSanitizer accelerates fuzz testing by instrumenting C/C++ and unsafe Rust code to detect buffer overflows, use-after-free, and related memory corruption bugs at runtime, with configurable ASAN_OPTIONS for verbosity, abort behavior, and leak control.

Specializes in the expert deployment and orchestration of production-grade multi-agent systems using Google's Agent Development Kit (ADK). This skill manages the complex lifecycle, including AgentCard discovery, A2A protocol communication, state management via Memory Bank, and secure execution within the Code Execution Sandbox. Ideal for setting up robust, enterprise-level AI workflows on the Vertex AI Agent Engine, especially during CI/CD pipelines.

A specialized skill for architecting, building, and productionizing complex Agent Development Kit (ADK) agents and multi-agent systems. This process emphasizes best practices, including clean module structure, robust tool integration, comprehensive regression testing, and deployment automation for reliable, shippable AI solutions. Use this when requiring the creation of structured, enterprise-grade agent code.

Provision production-grade Vertex AI ADK Agent Engine infrastructure with Terraform, covering Agent Engine runtime, code execution sandbox, Memory Bank, VPC Service Controls, and secure IAM setup for multi-agent deployments.

AFL++ is an advanced fork of AFL that runs multi-core campaigns, supports diverse mutations, and targets large C/C++ codebases with stable tooling via Docker or host builds.

This skill provides a comprehensive guide to designing and developing autonomous AI agents for code plugins. It covers the required file structure (YAML frontmatter), best practices for defining agent roles, designing robust system prompts, and setting up clear triggering conditions. Use this when you need to conceptualize, structure, or refine an agent's capabilities to handle complex, multi-step tasks autonomously.