Skill UI

This skill automatically detects and extracts tabular laboratory panels from diverse sources, including scanned images, photos, and delimited files (CSV/TSV). It converts complex visual data into clean, structured rows, capturing the test name, measured value, unit, reference range, and abnormal flag. It is essential for preparing medical data for subsequent standardization using LOINC and UCUM, and for mapping into FHIR resources.

A comprehensive framework for conducting authorized security assessments on Large Language Models (LLMs) and AI agents. It covers critical vulnerabilities including advanced prompt injection (OWASP Top 10), tool abuse, RAG data poisoning, memory manipulation, and system prompt extraction. Ideal for red teaming, security auditing, and ensuring the robustness of AI systems in production environments.

A comprehensive guide for installing, configuring, and validating the local long-term memory plugin (`memory-tencentdb`) within the OpenClaw environment. This guide covers everything from environment pre-checks to advanced configuration for data capture, information extraction, context recall, and vector embedding, ensuring robust, self-contained memory capabilities for RAG applications.

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.

Utilize zero-shot NER to extract custom entity types (e.g., Drug, Symptom, Device) from complex clinical or biomedical text without requiring any labeled training data or fine-tuning. OpenMed wraps GLiNER/GLiNER2, allowing users to define labels on the fly. The workflow involves building a model index and then running inference via CLI or Python API, making it ideal for rapid prototyping or unstable schemas.

Provides a hardened FastAPI endpoint for OpenMed's core functionalities: clinical Named Entity Recognition (NER), Personally Identifiable Information (PII) extraction, and de-identification. Designed for self-hosted microservices, it handles inference requests, model lifecycle management, and supports dynamic batching, ensuring secure, on-device processing of sensitive clinical data.

A specialized skill designed to convert complex legal materials—such as contracts, matter descriptions, corporate structures, timelines, or compliance processes—into context-appropriate, structured Mermaid diagrams. It utilizes a multi-step process involving structured data extraction, LLM enrichment, and mandatory gates to ensure accuracy. Ideal for visualizing relationships, obligations, and flows in legal documents, but strictly limited to legal or legal-adjacent diagrams.

This tool acts as a model-routing advisor for extracting structured data from complex legal documents, such as contracts. It analyzes key parameters—including stakes, cost, required speed, and document type (e.g., scanned vs. clean digital)—to recommend the optimal LLM model combination (Primary, Fallback, Avoid). It provides critical decision support, guiding users on which AI approach to use for extracting clauses, dates, and parties, without performing the actual extraction.

This skill guides the extraction of structured data from multiple documents (PDF, DOCX) into a comprehensive, cited Excel matrix. It is ideal for legal, contractual, or research review processes where users need to compare specific clauses, provisions, or pieces of information across numerous source files, ensuring every extracted data point includes a citation (page number and context quote) for verifiable accuracy.

This skill enables deterministic programmatic PDF operations including text and table extraction, merging and splitting pages, filling form fields, and generating new PDFs from JSON data. It utilizes libraries like pypdf, pdfplumber, and reportlab to handle structured PDF work efficiently. Ideal for automating document processing tasks such as report analysis, form completion, and document consolidation within a Python environment.

Extract product data, prices, reviews, stock, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool, with guided workflows for price monitoring, review analysis, and seller discovery.

Selects and runs the best Apify Actor for multi-platform web data extraction, checking schema, pricing, provenance, and limits before execution.