Skill UI

Manage AI agent context by offloading data to files for just-in-time loading. Reduces context window bloat and enables dynamic discovery of relevant information. Supports scratch pads, plan persistence, and sub-agent communication to optimize long-term tasks and reduce token costs.

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

Orchestrate comprehensive legacy system modernization using the strangler fig pattern. This workflow enables gradual replacement of outdated components while maintaining continuous business operations through expert agent coordination. It includes phases for legacy assessment, test coverage establishment, and incremental migration implementation to minimize disruption and maximize modernization velocity.

An advanced debugging skill designed for AI agents. It utilizes gdb-cli to provide intelligent assistance for C/C++ programs, enabling the analysis of core dumps, debugging live processes, and investigating complex issues like crashes, deadlocks, and memory corruption. It correlates runtime state inspection with source code context for deeper insights.

This skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).

Performs a full GEO + SEO audit of any website using parallel subagent delegation: crawls pages, scores AI citability, brand authority, platform readiness, technical infrastructure, E-E-A-T content and schema, then produces an actionable improvement plan.

This skill simulates Geoffrey Hinton, the Turing Award winner and father of deep learning. It provides expert insights on backpropagation, neural networks, deep belief networks, and capsule networks. Use this when discussing Hinton's contributions or advanced AI concepts.

This workflow orchestrates review, tests, commits, and branch pushes using parallel agents to ensure changes move through validation into a pull request. It enforces repository policies, branch protection, and security checks before merging. Ideal for completed implementation work requiring strict validation, automated testing, and compliance with maintainer guidelines without bypassing safeguards.

Global Chat Agent Discovery enables developers to locate and compare MCP servers and AI agents via a cross-protocol directory. It supports MCP, A2A, and agents.txt protocols, offering programmatic access through an MCP server. Users can search for tools like database access or file conversion, validate agents.txt files, and discover endpoints for agent-to-agent communication across multiple registries.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.

Delegate well-specified implementation tasks to xAI's Grok Build CLI running headlessly while the orchestrating agent plans, writes task specs, reviews every diff, and owns the final result.

Build container-based hosted agents using Azure AI Projects SDK with HostedAgentDefinition. Covers installation, environment setup, authentication, core workflow, resource allocation, and tool configuration for Azure AI Foundry.