Skill UI

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.

This skill provides a robust methodology for performing end-to-end UI testing and verification on Android emulators. Utilizing ADB commands, users can autonomously inspect UI element bounds (via uiautomator), simulate interactions (taps, swipes, input text), and capture visual/analytical screenshots. It is ideal for verifying UI changes in React Native or native Android applications, speeding up the debugging process, and generating comprehensive documentation for pull requests.

Hierarchical Agent Memory (HAM) scopes Claude Code agent context by generating per-directory CLAUDE.md files, tracking token savings, and surfacing analytics via a dashboard so multi-folder projects avoid repeated full scans.

This tool is designed for defining and scaffolding production-grade, multi-agent workflows. It allows developers to move beyond single prompts by structuring complex tasks into explicit steps. Core capabilities include selecting workflow patterns (e.g., sequential, parallel, orchestrator), defining handoff contracts, implementing error recovery strategies, and enforcing context and cost discipline across long-running AI processes.

Transform briefs into cinematic videos via Seedance 2.0, guiding text/image/video/audio inputs through director-level directives, multimodal tagging, and synchronized sound for 4-15s outputs.

Extracts cognitive fingerprints from any text using the DHDNA framework to reveal reasoning styles, decision habits, values, and emotional tones, ideal for profiling authors, comparing thinkers, or unpacking how someone reasons.

Guides execution of implementation plans by dispatching isolated subagents per task, enforcing a spec-first then quality review loop, and preserving overall coordination context for faster, high-quality iterations.

Creates detailed implementation plans for multi-step tasks by outlining file responsibilities, concrete coding/testing steps, and commit guidance so developers can execute with no prior context.

Explains the altitude-and-horizon mental model that differentiates PM and Director thinking, helping leaders diagnose friction zones, create cascading context, and focus on system-level strategy when scope or direction is unclear.

Helps product managers diagnose whether AI workflows rely on indiscriminate context stuffing or deliberate context engineering, then apply bounded domains, retrieval, and Research→Plan→Reset→Implement practices to improve reliability and output quality.

Guides product managers from discovery notes to an aligned PRD by orchestrating problem framing, user context, solution definition, and success criteria so engineering and stakeholders share a clear source of truth.

Guides product managers through choosing the right prioritization approach (RICE, ICE, Value/Effort, etc.) by asking adaptive questions about stage, team context, stakeholder needs, and decision cadence, then delivers tailored implementation guidance.