Skill UI

A comprehensive tool for creating high-quality training datasets for custom object detection and segmentation models. It supports multiple advanced annotation methods including BBox drawing, SAM2 pixel-perfect segmentation, and DINOv3 visual grounding. Features include AI auto-detection, object tracking, and standardized COCO format export, streamlining the entire data pipeline from capture to training.

Generates publication-quality, editable SVG vector graphics for structured diagrams, including system architectures, complex workflows, and pipelines. By taking a structured FigureSpec JSON as input, this tool ensures deterministic output: the same spec always yields the same diagram, making it ideal for formal documentation and system modeling where precise layout is mandatory. It operates entirely locally without external APIs. Use when you need a highly structured, technical diagram rather than a natural illustration or data plot.

This skill converts complex technical descriptions, such as system architectures, data flow diagrams, flowcharts, and sequence diagrams, into professional, production-quality SVG diagrams. It supports various technical models, including Agent and Memory architectures, and exports the final output as high-resolution PNG files for seamless documentation and presentation use.

Automates the CI/CD process for Glean enterprise search connectors. This tool runs comprehensive unit tests with mocked responses on every Pull Request, and executes integration tests against a staging Glean instance upon merging to the main branch. It validates connector data transforms, indexing API calls, and performs search relevance regression testing to ensure high search quality.

A comprehensive technical guide detailing the process of migrating large-scale enterprise search platforms from sources like Elasticsearch or Algolia to Glean. It covers data export, schema transformation, using bulk indexing APIs, creating data sources, and validating search quality for smooth, large-scale deployment.

This tool provides comprehensive observability for enterprise search systems (Glean). It tracks critical metrics such as search latency (p95), zero-result query rates, indexing throughput, and stale content ratios. By monitoring connector sync status and detecting indexing degradation, it allows teams to proactively maintain search relevance and ensure sub-second response times, preventing silent data quality decay.

Utilizes Grammarly's advanced APIs to conduct comprehensive content auditing. This skill detects the likelihood of AI-generated content and checks the provided text against vast databases for plagiarism. It provides a combined score assessing overall writing quality, clarity, and academic integrity, making it essential for professional, academic, and published content review.

Provides a comprehensive checklist for deploying Hex data analytics integrations into production. It covers critical operational best practices, including API key management, rate limit handling, error resilience (circuit breakers, exponential backoff), and robust monitoring strategies. This guide ensures that data pipelines are deployed reliably, minimizing downtime and guaranteeing consistent, high-quality data reporting for go-live scenarios.

This harness provides comprehensive, reproducible evaluation pipelines for complex LLM chains and agents (LangChain/LangGraph 1.0). It integrates golden dataset management, LangSmith evaluation, RAGAS metrics, deepeval LLM-as-judge, and structured agent trajectory analysis. Use it when establishing quality benchmarks for new chains, diagnosing performance regressions after model switches, or implementing CI/CD gates to prevent quality drops.

This skill provides comprehensive patterns for extracting and transforming Navan booking and transaction data. It addresses complex data pipeline challenges such as API pagination, date range chunking, and UUID-based deduplication. It supports both direct REST API implementation (via code examples) and configuration for managed connectors like Fivetran and Airbyte. Ideal for populating data warehouses, building robust analytics dashboards, or ensuring data quality from Navan sources.

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 skill guides the setup of Continuous Integration and Continuous Deployment (CI/CD) pipelines for Salesforce projects using GitHub Actions. It covers automated metadata deployment, rigorous Apex unit testing, and integration testing, ensuring code quality and stability before every release. Key steps include configuring JWT authentication, setting up GitHub Secrets, and defining multi-stage workflows to validate and deploy changes across environments.