Skill UI

A comprehensive toolkit designed for advanced data scraping and automation workflows on X/Twitter. Capabilities include deep tweet search, user profile analysis, bulk follower/media export, automated posting (replies, DMs), real-time account monitoring via webhooks, and integration with multiple AI agent platforms (Hermes, OpenClaw). Ideal for market research, social listening, and content automation.

A comprehensive guide for creating high-value competitor comparison and alternative pages. Designed for SEO and sales enablement, this skill covers multiple formats (You vs Competitor, Competitor Alternatives, etc.), emphasizing deep research, trust-building content, and modular architecture to capture competitive search traffic and guide users toward conversion.

This tool is designed to help optimize and refine prompts used with Large Language Models (LLMs). By structuring inputs, it ensures that the instructions given to AI systems are clear, comprehensive, and contextually rich, resulting in significantly higher quality and relevance of the generated output. Useful for developers, writers, and researchers.

This agent provides structured summarization for academic papers, reports, and articles. It goes beyond simple summarizing by extracting key findings, generating comparative matrices across multiple sources, and producing properly formatted citations (APA, IEEE, etc.). It is designed for analysts, product managers, and researchers who need actionable, deep insights from dense documentation.

A comprehensive collection of agent skills for rigorously auditing digital interfaces and user flows. It evaluates designs against 168 research-backed UX/UI principles, detects common design antipatterns, and provides specialized review for AI-powered interfaces, ensuring adherence to modern best practices.

This skill acts as a Visual Psychologist and Environmental Researcher, guiding the systematic mapping of visual elements—including color, typography, spacing, and imagery—to desired emotional states and conversion goals. It ensures that design decisions are strategic, supporting persuasion and brand narrative rather than merely being decorative. Use it when you need visuals to evoke a specific feeling (e.g., trust, urgency, calm) for a defined target audience, while respecting cultural and accessibility constraints.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

This skill provides comprehensive interaction with the DeerFlow AI agent platform via its robust HTTP API. It is designed for managing complex, multi-step AI workflows, including deep academic research, code execution, web browsing, and file analysis. Users can initiate and continue conversation threads, manage agent memory, upload documents (PDF, etc.), and delegate complex research tasks to the autonomous agent system.

This skill provides anti-slop frontend design assistance by offering researched art directions based on 247 curated gallery sites. It enforces strict house styles, checks rules via browser inspection, and runs a look-and-fix loop on screenshots. Suitable for landing pages, marketing sites, portfolios, and app screens across various stacks like React, Vue, and Tailwind. It helps avoid generic AI-made looks and ensures premium, award-level design quality.

An advanced, AI-powered agent designed to generate comprehensive and workable unit tests for virtually any programming language (e.g., Python, C#, Java, Go, TypeScript). It operates via a sophisticated multi-agent pipeline (Research → Plan → Implement → Build → Test → Fix → Lint). This ensures generated tests are not only written following project conventions but also compile, pass all tests, and effectively improve code coverage.

Continuous file-based planning system for multi-step AI agents. Maintains task_plan.md, findings.md, and progress.md files on disk to track project progress. Injects planning context into lifecycle hooks and supports session recovery. Designed for complex research or development tasks requiring 5+ tool calls.

A persistent, graph-based knowledge base designed to accumulate structured knowledge throughout the entire research lifecycle. It stores and connects diverse entities—including published papers, novel ideas, conducted experiments, and testable claims. Inspired by LLM Wiki patterns, it ensures knowledge compounding, meaning the wiki becomes progressively smarter with every piece of input, rather than requiring re-derivation for every query.