Skill UI

This skill parses standardized C-CDA/CCD XML clinical documents exported from EHRs. It extracts both human-readable section narrative text and coded entries, mapping them by LOINC codes and template IDs. It is essential for preparing clinical free-text data for subsequent NLP analysis and supports XML-aware de-identification while preserving document structure.

This skill processes clinical text to interpret laboratory results. It parses reference ranges (e.g., "135-145", "<5") and derives structured abnormal flags (low, normal, high, critical) for a given numeric value. It is unit-agnostic and respects explicit flags provided by the originating lab, making it crucial for structured clinical data extraction and interpretation workflows.

This skill parses complex, free-text clinical trial eligibility criteria from sources like ClinicalTrials.gov into structured, machine-readable rules. It then evaluates these criteria against a comprehensive patient profile (derived from OpenMed data) to determine potential eligibility. The output provides an explainable verdict (eligible, ineligible, or unknown) for every criterion, serving as a critical decision support tool for clinicians and researchers.

Implement secure API design patterns including authentication, authorization, input validation, and protection against vulnerabilities. Covers JWT token contracts, refresh sessions, resource authorization, and input parsing. Suitable for backend developers securing endpoints, preventing abuse, and ensuring data integrity in production environments.

Extract structured fields from documents like invoices, payslips, and contracts with page grounding. Each value cites its source page and confidence score. Missing values are reported as not found instead of hallucinated. Supports PDF, PNG, and DOCX files. Ideal for accounting, compliance, and data parsing tasks where accuracy is critical. Uses Stipple API for processing.

Create reusable command-line scripts and utilities for the Hugging Face API, enabling chaining, piping, and intermediate processing. Supports direct API access and the hf CLI tool, with examples for model search, metadata enrichment, and model card parsing.

`jq` is the standard and powerful CLI tool for querying, filtering, and reshaping JSON data. This skill provides expert-level practical patterns for working with deeply nested structures, handling complex transformations (like flattening, renaming, and grouping), and integrating JSON logic into shell pipelines. Essential for parsing API outputs (AWS, GitHub), log files, and building robust Bash scripts.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

This skill provides a guide for implementing Electronic Data Interchange (EDI) with Odoo ERP. It covers mapping X12 and EDIFACT transactions to Odoo objects, configuring trading partners, and automating order processing. Includes Python code examples for parsing EDI files and generating acknowledgments.

This skill diagnoses latency and throughput in weather data workflows by measuring stages like discovery, transfer, parsing, and rendering separately. It identifies bottlenecks before code changes, supporting profiling and validation of performance fixes. It ensures accurate optimization by isolating slow steps in the pipeline.

Learn how to configure xberg's --format and --content-format flags to optimize document extraction for different consumers. Covers LLM context, RAG indexing, JSON parsing, and token reduction strategies for efficient data pipelines.