Skill UI

Search, import, and analyze TikTok, YouTube, and Instagram content via Memories.ai LVMM, indexing videos once for repeatable Q&A, summarization, social research, and searchable knowledge-base building backed by MAG.

Generate SEO-optimized competitor comparison, vs, alternatives, and roundup pages. Use it to build structured feature matrices, schema markup, pricing comparisons, and conversion-focused content for competitive search intent.

Analyze page content quality, E-E-A-T signals, readability, structure, linking, and AI-content risk. Useful for SEO audits, thin-content reviews, and assessing whether a page is helpful, trustworthy, and citation-ready.

Analyze a website for AI search visibility across Google AI Overviews, ChatGPT search, and Perplexity. Checks citability, structure, brand signals, crawler access, and llms.txt readiness to improve generative engine optimization.

A comprehensive skill for ingesting, analyzing, and manipulating multi-modal video and audio content. Supports real-time desktop capture, live stream monitoring (RTSP), and file uploads (URL/local). Capabilities include advanced indexing (visual, semantic, temporal), precise moment searching, professional timeline editing (subtitles, overlays, dubbing), transcoding, and generating actionable alerts. Ideal for content creators, monitoring systems, and complex media workflows.

This skill orchestrates the entire coding lifecycle as a state machine, ensuring tasks proceed from issue intake through implementation, structured review, deployment, and final acceptance verification. It enforces a rigorous 'Definition of Done' (DoD) proof, moving beyond simple code changes. Ideal for complex feature development requiring structured handoffs and explicit verification evidence.

This methodology converts verbose, natural language system instructions into an atomic, structured-label format (e.g., trigger:, action:). By structuring rules, it bypasses the limitations of natural language processing, ensuring LLMs allocate full attention to every constraint, thereby maximizing compliance, improving reliability, and reducing token usage.

This skill manages complex coding tasks from initial implementation through testing, review incorporation, deployment, and final runtime verification. It ensures the task meets explicit Definition of Done (DoD) criteria iteratively, minimizing the need for manual prompting across multiple development stages.

Creates concise Architecture Decision Records (ADRs) that capture context, decision, and consequences whenever a significant technical choice has been finalized so future engineers understand why the architecture looks the way it does.

This skill enforces a mandatory, structured gate for starting new implementation tasks. It mandates that all work must be tracked via a GitHub issue, requiring explicit, testable acceptance criteria before any execution can begin. It defines a strict workflow (Problem, Goal, Scope, Acceptance Criteria, Status) to manage task readiness, ensuring that development only proceeds when criteria are fully defined and verifiable.

Generates structured RFC documents to propose and decide on major technical or process changes, guiding stakeholders through options, criteria, and approvals before execution.

Guides product teams through hypothesis writing, metric definition, sample-size planning, ICE prioritization, and statistical interpretation so experiments stay practical, defensible, and aligned with business decision thresholds.