Skill UI

This skill provides comprehensive strategies for testing OneNote integrations within CI/CD pipelines. It supports mock-only PR checks using MSW for zero-dependency testing, and robust live integration tests for nightly builds using Azure AD credentials. Ideal for automating quality assurance when connecting OneNote features via the Graph API.

Comprehensive guide for setting up Continuous Integration/Continuous Deployment (CI/CD) pipelines for Palantir Foundry integrations using GitHub Actions. This workflow covers running PySpark unit tests, performing SDK integration tests with mocked APIs, and implementing custom linting rules to enforce Foundry-specific best practices. Use this when ensuring code quality and validating complex business logic changes in an automated build process.

A comprehensive checklist for deploying Foundry-integrated applications to a production environment. It guides users through critical go-live procedures, covering pre-deployment credential management, rigorous code quality checks, setting up robust monitoring and alerting systems, and executing safe rollout and rollback strategies. Essential for ensuring system stability during major releases.

Performs rigorous, zero-context verification of all quantitative claims, comparisons, and scope statements reported in a research paper against the raw evidence files. It is designed to detect confirmation bias, such as rounding errors, data cherry-picking, or misreported deltas, ensuring strict fidelity between published claims and underlying raw results. Ideal for pre-submission quality control.

This advanced tool generates publication-ready academic figures and illustrations. It employs a sophisticated, multi-stage iterative workflow involving an AI Planner, Layout Optimizer, Style Verifier, and a dedicated native image generation bridge. The process includes strict visual review and self-correction loops (CVPR/NeurIPS standards) to ensure the highest level of academic polish.

This advanced tool acts as a specialized prompt engineer, guiding users to create highly optimized, production-ready prompts for various AI models (LLMs, Midjourney, etc.). It enforces strict writing rules, guides detailed intent extraction (Task, Context, Constraints), and provides model-specific best practices (e.g., Claude vs. GPT-5.x) to maximize AI output quality and maintain consistency. Essential for professional content creators, developers, and researchers needing precise AI interaction.

This skill facilitates an interactive Quality Assurance (QA) session. Users can conversationally describe encountered bugs or issues. The agent clarifies the problem, explores the codebase for context and domain language, and then meticulously files durable GitHub issues. It supports both single-issue reports and breaking down complex problems into multiple, independently verifiable tickets, ensuring developers receive actionable, high-quality reports written from the user's perspective.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

Provides a systematic guide for diagnosing and improving poor search quality in vector databases like Qdrant. This guide covers troubleshooting issues such as low recall, when approximate search degrades, selecting optimal embedding models, and correctly tuning HNSW parameters, quantization, and filtering strategies. Essential for optimizing RAG and semantic search pipelines.

This guide covers advanced retrieval strategies that significantly boost search quality beyond basic vector similarity search. Techniques include Hybrid Search (combining dense and sparse indexing), Reranking (improving result order), Relevance Feedback (guiding search with iterative user scores), and Maximal Marginal Relevance (MMR) for diversity. Use these methods when standard search results lack relevance, miss critical keywords, or exhibit poor precision.

This skill integrates with the Runway ML platform, enabling developers to programmatically generate high-quality video and edit images using the official Python SDK. Use this when your workflow requires advanced creative AI features, such as converting text prompts into videos, or managing complex creative tasks within a CI/CD pipeline. The skill provides necessary patterns and error handling for successful API calls.

This comprehensive guide details advanced workflows for high-quality video generation using platforms like Runway. Learn essential techniques including advanced prompt engineering (Subject + Action + Setting + Style), optimal model selection (e.g., gen4_turbo), and parameter tuning for duration and aspect ratio. The workflow also demonstrates efficient batch generation for creating multiple, consistent video assets.