Skill UI

This skill automates the final deployment phase of an agent system. It converts a validated build sheet into four ordered JSON payloads (environment, agent, session, kickoff) and generates a resumable `launch.sh` script. Crucially, this process enforces API-key safety by requiring the user to run the script in their shell, pulling the Anthropic API key from the environment variable, thus ensuring the key never appears in logs or files. It provides a structured, multi-step launch sequence.

This specialized sub-agent is responsible for transforming high-level feature proposals and technical specifications into a formal, comprehensive 'design.md' document. It systematically captures all necessary technical details, including architectural decisions, data flow diagrams, detailed file changes, API contracts, and thorough testing strategies. Use this skill during the planning phase of any complex software change to ensure technical completeness and feasibility.

agy-auto is a security harness for Google Antigravity CLI enabling unattended execution with layered policy controls. It prevents dangerous permissions using hard-deny rules, fast-allow for safe operations, and an LLM classifier for ambiguous commands. It supports scoped token approvals for human judgment, ensuring secure pair-programming without exposing system files or credentials.

Designs data models, API contracts, file structures, and architectural patterns for software systems. Produces structured blueprints with ADR summaries, security notes, and migration strategies to guide implementation and code review handoffs.

Atlas Ledger is a critical companion designed to provide lasting 'memory' to project contracts. It automatically processes detected deviations or 'drift' (e.g., from audits or reviews) and distills these instances into formal, actionable contract clauses (WHEN/DON'T/INSTEAD). These clauses are written to a persistent project memory file, Atlas.md. This mechanism ensures that lessons learned from failure are systematically captured, allowing future contract cycles to incorporate these rules, thereby strengthening overall project governance and reliability.

This Python SDK enables hierarchical file system operations for Azure Data Lake Storage Gen2. It supports file and directory management, data upload/download, access control, and async processing for big data analytics workloads. Ideal for developers managing cloud storage infrastructure.

This skill covers managing block storage volumes including LVM, cloud-based EBS, filesystem creation, snapshots, and RAID configurations. It guides users through provisioning raw disks, partitioning, formatting, and extending volumes in production environments. Prerequisites include root access and relevant packages like lvm2 and mdadm.

This skill runs Bumblebee scans on macOS/Linux to detect compromised packages, extensions, and configurations. It supports baseline, project, and deep scan profiles to identify supply-chain vulnerabilities and incident response needs without modifying the system.

Secure Docker images and container runtime configurations with non-root users, read-only filesystems, capability dropping, and image scanning.

Run read-only security exposure checks for CVEs and package advisories on local systems. Generates structured markdown audit reports without modifying files or requiring sudo. Supports auditing npm, Python, Homebrew, and running processes to identify vulnerabilities safely.

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.

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).