Skill UI

This skill manages the entire lifecycle of long-running deep learning training and inference jobs deployed on remote, ephemeral computing environments (e.g., AutoDL, RunPod, bare SSH, Slurm, K8s). It provides critical capabilities like resilient checkpointing, managing spot instance preemption, debugging resource failures (OOM, NaN), and ensuring safe billing teardown, making it essential for professional ML Ops on rented resources.

This skill enables parallel read-only multi-agent review of git diffs or specific file scopes. It identifies behavioral regressions, security risks, performance issues, and coverage gaps using four specialized sub-agents. The main agent synthesizes findings to filter and summarize critical issues without modifying code. Ideal for ensuring code quality and safety before merging changes.

A critical discipline for managing expenses when integrating paid AI or inference APIs. This guard treats dollar cost as a third complexity dimension, mandating the definition of cost contracts (max calls per run, max budget per run, max budget per day). It enforces best practices for bounding loops, retries, and fan-out, ensuring that code bugs cannot lead to unexpected overspending. Best practice requires configuring hard caps both in the code and at the provider dashboard for defense in depth.

This guide provides comprehensive patterns for designing and implementing secure multi-tenant SaaS applications using shared databases. It covers critical techniques like Row-Level Security (RLS) in PostgreSQL, implementing tenant-aware middleware in TypeScript (Express/Fastify), and enforcing data scoping at the ORM level (Prisma/Drizzle). Learn how to prevent data leakage and manage tenant isolation across shared schemas.

SkillCheck validates SKILL.md files against the agentskills specification and Anthropic best practices. It performs read-only checks on structure, semantics, naming, and quality to catch errors before publishing. The tool provides a score, grade, and detailed report with fix suggestions for critical issues to ensure high-quality skill authoring.

This skill helps select and place genuine testimonials, case studies, and social proof to address specific trust barriers in marketing. It involves analyzing audience psychographics and trust gaps to match the right proof type (authority, similarity, volume) to the context. It ensures ethical use of evidence to reduce hesitation at critical decision points without manipulation.

This skill utilizes the Taisly Agent Kit to manage the entire workflow for publishing approved short-form videos across major platforms, including TikTok, Instagram Reels, YouTube Shorts, X, and Facebook. It facilitates agent-assisted publishing by ensuring coordination of captions, metadata, and campaign details. A critical feature is the mandatory explicit confirmation gate, which prevents any state-changing publishing action without explicit user approval, prioritizing safety and compliance.

This skill provides a robust system for tracking all code modifications using structured JSON records and enforcing a defined state machine (e.g., planned -> deployed). It is critical for AI coding workflows, ensuring that if a session expires or is abandoned, the next session receives the full context, progress summary, and blockers to resume seamlessly. Ideal for complex, multi-stage development projects and onboarding new codebases.

Provides a structured reference for the 100 most critical web application vulnerabilities. Enables systematic vulnerability identification, impact assessment, and remediation guidance. Suitable for authorized security assessments and educational purposes.

This expert guide provides comprehensive best practices for developing robust, high-performance C++ code within Unreal Engine 5.x. It covers critical topics such as UObject lifecycle management, adhering to Epic Games' strict coding conventions, optimizing performance-critical sections (e.g., avoiding unnecessary Ticking), and correctly using soft references for asset loading. Use this skill for professional game development tasks requiring deep knowledge of the Unreal reflection system and advanced C++ patterns.

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

LWC is a durable memory system for coding agents that preserves project decisions, code structure, and verified context across sessions. It utilizes a physical Wiki document graph and a CodeGraph index to enable source-grounded recall. This tool helps agents search, update, and maintain knowledge without losing critical information during future coding-agent interactions.