Skill UI

A comprehensive guide to configuring a structured, local development environment for LangChain applications. It covers setting up project architecture, managing dev dependencies, and implementing a two-tiered testing strategy: isolated unit tests using mocked LLMs (for fast, API-free testing) and robust integration tests using real API providers. Ideal for establishing a reliable, rapid iteration workflow.

This skill coordinates the complex parallel execution of end-to-end, integration, and unit tests across multiple frameworks (e.g., Jest, Playwright, pytest) and CI/CD environments. It manages test splitting, worker allocation, tiered execution strategies, and aggregates results into unified reports. Ideal for optimizing the testing lifecycle and ensuring robust quality assurance in large-scale, multi-component applications. Key functions include intelligent retry logic for flaky tests and building CI pipelines (e.g., GitHub Actions matrix).

This skill provides a comprehensive framework for implementing robust API rate limiting. It supports advanced algorithms like sliding windows, token buckets, and fixed counters, backed by Redis for distributed state. Use it to protect endpoints from excessive requests, enforce tiered quotas (Free, Pro, Enterprise), and prevent abuse like brute-force attacks by setting per-user, per-endpoint, or per-API-key limits.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.

This framework defines a comprehensive, context-driven matrix for managing security incidents within a Security Operations Center (SOC). It details tiered escalation paths, response Service Level Agreements (SLAs), and notification procedures based not only on severity (P1-P4) but also on business risk, asset criticality, and data sensitivity. It guides analysts through triage, investigation, and timely escalation to management (CISO/Legal) to ensure rapid containment and appropriate organizational response during a breach.

Implements Microsoft's Enhanced Security Admin Environment (ESAE) tiered administration model for Active Directory. This skill guides the hardening of AD privileged access architecture by establishing strict separation between Tier 0, Tier 1, and Tier 2 accounts. It is essential for mitigating advanced threats like pass-the-hash, Kerberoasting, and ensuring compliance with industry standards (NIST 800-53).

This skill details the design and implementation of Privileged Access Workstations (PAWs). It covers establishing a tiered administration model, enforcing device compliance using Microsoft Intune or Group Policy, integrating Just-in-Time (JIT) access provisioning, and connecting with leading PAM platforms like CyberArk or BeyondTrust to secure sensitive administrative operations.

Auto-generate professional GEO service proposals from audit data, translating findings into business risks, tiered recommendations, pricing, timeline, and PDF-ready documentation for prospect follow-up.

AuraKit converts a single /aura command into a secure full-stack Claude pipeline with 34 modes, OWASP SEC-01~15 safeguards, runtime hooks, tiered models, token savings, and multilingual reviewers.

Instantly generates LLM-optimized summaries for single academic papers using the AlphaXiv service. Supports input via arXiv ID or URL, providing tiered fallback (Overview → Markdown → LaTeX Source) for comprehensive understanding.

A comprehensive, step-by-step runbook designed for diagnosing and resolving critical operational issues in ClickHouse databases. Covers tiered severity levels (P1-P3) for scenarios ranging from server outages and OOM kills to performance degradation and stuck queries. Provides essential system tables queries and commands for service recovery and system health checks.

This guide provides best practices for scaling multi-tenant deployments in Qdrant. It advises against the inefficient 'one collection per tenant' approach. Solutions include using shared collections with payload filtering, implementing custom sharding for large-scale tenants (100k+), and employing tiered multitenancy for unevenly sized workloads, ensuring optimal resource utilization and performance.