Skill UI

This skill guides the parallel assignment of specialized agents when debugging multiple, independent failure domains. Instead of using one agent for all issues, isolate each problem (e.g., a specific test file or subsystem) to its own dedicated agent. This allows agents to work concurrently without sharing context or interfering with each other, significantly accelerating the debugging process and maintaining context purity.

Ginkgo Cloud Lab provides remote access to an advanced autonomous lab infrastructure, enabling users to submit and manage complex molecular biology protocols. It supports diverse applications including protein expression and purification (cell-free, E. coli, Pichia), quantitative assays (HiBiT, A280, LabChip), nucleic acid synthesis (IVT mRNA/circRNA), and characterization techniques (Thermal Shift, Echo-MS, SPR). The platform features an AI agent (EstiMate) for custom workflow feasibility and pricing.

Use terminal commands to discover, list, create, edit, move, copy, enable/disable, and delete AI agent skills across 11 tools, handling format conversions and scope overrides between global and project configurations.

This skill rigorously verifies all claims of completion, whether for a task, a bug fix, or a feature rollout. It prevents false assurances by demanding fresh, multi-faceted evidence, including full test suite results, comprehensive coverage reports, integration status, and validation commands. Use it before declaring a component ready for GO or accepting a sub-agent's success report.

This skill integrates the Clawdbot agent with the LINE messaging platform, enabling robust communication capabilities. It supports core messaging actions such as sending text, sending files, and replying to user messages. This is highly useful for developing AI agents and bots marketed to regions like Japan, Taiwan, Thailand, and Indonesia.

This skill integrates an AI agent (Clawdbot) with Signal, one of the most private messaging platforms. It enables automated communication by utilizing the signal-cli tool to perform actions such as sending messages, sending files, reacting, and replying within a designated chat channel. Ideal for building secure, automated bot interactions in private messaging environments.

This comprehensive guide details the full plugin architecture for extending Claude Code. Learn how to build various extension points, including auto-activating skills, slash commands, specialized agents, and Model Context Protocol (MCP) servers. It serves as the definitive reference for integrating custom tools and workflows into the Claude ecosystem.

A powerful pre-action hook designed to enforce rigorous checks before AI agents modify code, create files, or execute destructive commands. Instead of relying on superficial self-evaluation, GateGuard forces the LLM to gather concrete facts (like dependencies, data schemas, and impacted modules), significantly improving code reliability and architectural depth. It provides multiple gates for edits, writes, and destructive bash commands.

A comprehensive diagnostic guide for common issues encountered with CAST AI agents, Kubernetes autoscaling, API connectivity, and resource provisioning. Covers scenarios like agent crashes, 401 errors, and scaling failures, providing specific commands (kubectl, curl, helm) and detailed fixes for robust troubleshooting.

This skill helps test the robustness of an AI agent's authorization mechanisms. Use it when the agent handles user roles, permissions, or sensitive data (e.g., admin functions, cross-tenant data). It simulates attempts to bypass authentication, escalate privileges, or access data belonging to other users or tenants.

Integrates CAST AI cost validation into CI/CD pipelines (GitHub Actions, Terraform). This skill automatically enforces cost governance by checking minimum savings thresholds, validating Terraform plans to prevent destructive changes, and ensuring agent health status before deployment, guaranteeing cost-aware and safe deployments.

This guide details how to use sophisticated coding agents (such as Codex, Claude Code, or Pi) through a structured bash workflow. It provides methods for complex development tasks, including building new features, refactoring large codebases, and reviewing pull requests (PRs). Key best practices covered include managing pseudo-terminals (PTY), defining focused working directories (`workdir`), and executing tasks in the background for robust, multi-step automation.