Skill UI

Analyze product pages, Google Shopping visibility, Amazon marketplace data, schema markup, and competitor pricing. Useful for optimizing ecommerce listings and finding marketplace keyword gaps.

This skill generates a comprehensive issue report on the current challenges, limitations, and open problems within Japanese Natural Language Processing (NLP) for a specified topic. By combining knowledge from a curated local dataset with real-time web search, it provides a deep dive into ongoing research gaps, evaluating the state-of-the-art and recommending areas for future work.

This advanced AI skill converts raw study materials—such as syllabi, past exam papers, or personal notes—into a structured, prioritized, and difficulty-ranked High Score Roadmap. It covers all key question types (Theory, Numerical, MCQ, Coding, Lab) and optimizes study flow by ordering topics from Easy to Hard. Use it for targeted revision, gap analysis, and predicting high-yield exam questions.

This skill implements the LLM Council concept, enabling multiple open-weight language models to collaborate on complex queries. The process follows a three-stage deliberation: 1) Parallel independent responses from selected models; 2) Peer-to-peer ranking of the responses; and 3) Final synthesis by a designated Chairman LLM. Powered by Fireworks AI, it ensures deep, multi-faceted analysis and consensus building for high-stakes decision support.

A comprehensive security tool designed to detect and defend against subtle prompt injection attacks hidden within untrusted multi-modal content, such as web pages, PDFs, and images. It scans for obfuscated payloads (e.g., hidden CSS, metadata, zero-width characters) by combining content normalization, heuristic analysis, and dedicated ML detectors, ensuring that LLM agents only consume sanitized, trustworthy input.

Tamarind Bio is a comprehensive cloud platform providing access to state-of-the-art open-source tools for structural biology and molecular design. It supports complex tasks including protein structure prediction (AlphaFold, ESMFold), molecular design (RFdiffusion, ProteinMPNN), protein-ligand docking, and binding affinity prediction. Users submit sequences or structures and receive predicted results without requiring local GPU hardware.

This skill guides developers on implementing Conversational Language Understanding (CLU) using the official azure-ai-language-conversations Python SDK. It demonstrates how to analyze conversation intent and entities, build advanced NLP features, and integrate language understanding into applications using ConversationAnalysisClient. Best practices emphasize using DefaultAzureCredential and context managers for robust, production-ready code.

A powerful plugin enabling Claude Code to delegate complex tasks to external AI CLIs (such as OpenAI Codex or Google Antigravity/Gemini) without exiting the conversation. It supports multi-model workflows, allowing users to seek a second opinion, cross-check deep analysis, or delegate side tasks (like research or code review). The core value is that Claude synthesizes the raw outputs, providing a critical review and reconciliation of findings across all consulted models.

This skill wraps the `getCompactContext` functionality to efficiently retrieve a compacted ReasoningBank context, specifically optimized for cost-analysis queries. It generates a detailed report summarizing critical metrics such as tokens saved, memories retrieved, and agentic-flow availability. It is essential for monitoring and optimizing resource usage in complex AI workflows, providing deep visibility into the efficiency and cost of the context retrieval mechanism.

This skill provides a detailed, side-by-side comparison of advanced LLM agent evaluation frameworks, such as ruflo and HAL. It helps researchers and developers identify capability gaps—including real code execution, full web browsing, and structured file parsing—and prioritize architectural improvements for robust agent benchmarking and performance tracking.

A comprehensive guide for researchers designing and auditing NLP experiments for top-tier conferences. It covers establishing defensible baselines, ensuring evaluation breadth, performing rigorous statistical tests (significance, variance), controlling for data contamination, and structuring detailed functional error analysis. Essential reading for publishing reliable AI research.

This skill guides authors on how to position, structure, and strengthen empirical Natural Language Processing (NLP) manuscripts for submission to the Conference on Empirical Methods in Natural Language Processing (EMNLP). It provides deep insights into conference fit, required evidence (baselines, error analysis, datasets), proper framing, and anticipating reviewer expectations for top-tier AI venues.