Skill UI

This skill provides a comprehensive solution for embedding the powerful, Photoshop-compatible Photopea editor into any web application. Using photopea.js, developers can control the embedded instance via JavaScript, enabling advanced workflows such as opening remote files, loading local assets (fonts, brushes), executing custom scripts to manipulate layers and text, and exporting the final result in various formats (PNG, JPG, PSD, WebP). Ideal for building professional creative tools directly into your product.

A comprehensive guide on embedding before-and-after screenshots directly into pull request descriptions. This skill helps reviewers visualize UI changes, layout adjustments, and data visualizations instantly, without needing to check out the branch. Includes best practices for image sizing, structured Markdown patterns, and specific API/upload guidance for Azure DevOps and GitHub.

A comprehensive guide and workflow for embedding R scripts, primarily using the ggplot2 library, into Power BI Reports (PBIR). This tool enables the creation of high-quality, static, publication-ready statistical visualizations directly within the report canvas by executing R code on the defined dataset. It covers the full lifecycle: adding the visual, writing the script, injecting the code, and validating the output.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

A comprehensive workflow for establishing and maturing a Third-Party Risk Management (TPRM) program. This guide covers the entire vendor lifecycle, from initial inventory and risk tiering to conducting due diligence (SOC 2, ISO 27001), embedding security requirements in contracts, and ensuring continuous monitoring and secure offboarding. Essential for compliance and supply chain risk management.

This skill provides a repeatable assessment for RAG systems, testing for critical vulnerabilities in vector stores and embedding models. It specifically addresses embedding inversion, cross-tenant data leakage, and knowledge-base poisoning, aligning with OWASP LLM08:2025. Use when performing authorized security audits of retrieval layers and multi-tenant isolation.

This skill provides comprehensive techniques for security-testing Retrieval-Augmented Generation (RAG) pipelines. It probes for indirect prompt injection, which occurs when malicious instructions are embedded in retrieved documents, and tests for embedding-space manipulation. Using advanced tools like NVIDIA garak, Promptfoo, and PyRIT against vector stores (FAISS, Chroma, etc.), users can validate retrieval guardrails and secure CI/CD gates against adversarial inputs and data leakage.

A spectral vector search tool that enhances nearest-neighbor search with graph Laplacian features, capturing both semantic similarity and structural roles in embedding spaces.

A professional 3D mockup template for showcasing modern devices (iPhone, MacBook). It allows embedding real, dynamic HTML content directly into the screen canvas, simulating advanced product launches, app demos, or design presentations. Features include 3D rotation, glass lens refraction, and realistic reflections.

This skill allows for the deterministic re-running of recorded user session paths (trajectories). It is essential for regression testing, bug reproduction, and verifying UI flow consistency after deployments. Crucially, it utilizes browser-selectors embedding similarity to intelligently recover and retry interactions even when the underlying webpage structure (DOM) has drifted, providing robust testing capabilities.

This tool ingests raw market data (OHLCV) for a specified symbol. It normalizes the data into relative OHLCV vectors, converts these vectors into high-dimensional embeddings, and then stores and indexes them using HNSW. This process prepares the data for fast, similarity-based pattern detection and advanced financial time series analysis.

This memory bridge ingests Claude Code's local auto-memory markdown files and structures them into AgentDB. It generates high-fidelity ONNX embeddings, handles deduplication using cosine similarity, and indexes the memories in a scalable namespace. This enables seamless, unified semantic search across all historical sessions and projects, significantly enhancing RAG capabilities.