Skill UI

Hypogenic is an advanced framework that uses Large Language Models (LLMs) to systematically generate and test hypotheses on complex tabular datasets. It supports three powerful modes: data-driven generation (HypoGeniC), literature-grounded refinement (HypoRefine), and combined union methods. This tool is essential for accelerating scientific discovery in fields such as predictive modeling, content analysis, and deception detection by combining empirical data insights with theoretical knowledge.

Configure comprehensive CI/CD pipelines for integrating Grammarly's text analysis into your development workflow. This setup uses GitHub Actions to run unit and integration tests on pull requests, validating API connectivity, enforcing content quality gates (e.g., minimum grammar scores), and ensuring documentation quality before merging to the main branch.

This skill provides advanced sentiment analysis tailored for financial news and market texts. It supports two modes: high-speed, localized analysis using FinBERT, and advanced, reasoning-based analysis via LLM. Users can determine the sentiment polarity (positive, negative, or neutral), score, and specific reason for financial content, making it ideal for market intelligence and data enrichment.

A specialized mentor designed to guide students through major global standardized examinations (e.g., SAT, ACT, 高考, IB, A-Level, 中考). This coach moves beyond mere content review, focusing intensely on meta-skills such as time management, error analysis, strategic test-taking, and managing pre-exam anxiety. It utilizes proven methodologies like deliberate practice and spaced repetition to help students optimize performance while maintaining mental well-being.

Execute comprehensive, asynchronous transcription workflows using AssemblyAI. This tool handles file upload, queues jobs, and provides advanced audio intelligence features. Capabilities include speaker diarization, sentiment analysis, named entity recognition (NER), topic categorization (IAB), content safety moderation, and sensitive PII redaction. Ideal for processing meeting recordings, interviews, and large volumes of spoken content.

This internal agent skill processes raw X/Twitter posts into structured bug candidates. It systematically performs parsing, content deduplication, PII redaction, reliability scoring, and classification. Finally, it clusters related reports using deterministic signatures, providing a highly organized output for efficient bug triage and analysis from large-scale social media streams.

DeepXiv provides a structured and progressive method for accessing open-access academic literature. Instead of forcing the download of entire papers, it allows users to search by topic or ID and progressively read content—from brief overviews and metadata maps to specific sections. This is ideal for researchers who need layered paper access, trend analysis, or quick, targeted knowledge retrieval without overwhelming context.

A comprehensive tool designed to scan any agent skill for potential security risks and vulnerabilities before implementation or use. Powered by local static analysis (A.I.G.), it ensures that no file contents, credentials, or personal data ever leave your device, providing maximum privacy and safety for integrating new AI capabilities across various agent platforms.

A guide and toolkit for optimizing Grammarly API costs in large-scale applications. It addresses common cost overruns associated with high text volume by implementing strategies such as result caching, chunking long documents, sample-based scoring, and setting usage budget alerts. Use this when integrating Grammarly into enterprise workflows or managing billing for high-volume content analysis.

Implements a robust data pipeline for handling large documents with the Grammarly API. This utility manages text chunking to comply with API character limits (max 100k), processes text in manageable segments, and aggregates multiple scoring metrics (like correctness, clarity, and tone) into a single, comprehensive report. Ideal for large-scale content analysis and data ingestion.

This skill provides a comprehensive set of APIs for advanced writing analysis using Grammarly. It allows users to score text based on engagement, correctness, clarity, and tone. Furthermore, it includes functionality for AI detection and sophisticated plagiarism checking. It is ideal for developers needing to build content verification, grammar-checking, or academic integrity tools.

This guide provides comprehensive strategies for minimizing operational costs within the MindTickle learning platform. It details cost drivers across various components, including seat licenses, content storage, quiz assessments, call recording analysis, and API integrations. Users can learn best practices for content deduplication, implementing incremental sync, setting proper retention policies, and optimizing resource usage to reduce unnecessary spend and improve overall platform efficiency for sales organizations.