Skill UI

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

This skill converts Microsoft Word documents (.docx) into Markdown format. It is essential for accurately analyzing, summarizing, or extracting data from proprietary Word files, as direct parsing is unreliable. It supports single files, batch processing of folders, and is designed to coordinate with sibling skills (PDF and Excel) when handling mixed document types to ensure no data is skipped.

This skill guide teaches how to use a CLI tool to programmatically create, modify, and validate complex Excel (.xlsx) files. It covers everything from building robust financial models and dashboards to parsing raw data and ensuring the output adheres to professional standards, including zero formula errors and proper print layouts. Ideal for data engineering and business intelligence workflows.

This skill provides a comprehensive guide for manuscript authors targeting the StarSem Conference on Computational Semantics (*SEM). It helps authors determine the conference fit, refine framing, establish the necessary evidence bar, and strategize submissions for topics related to meaning representation, semantic parsing, and inference. It is a specialized venue-selection and re-framing tool for advanced NLP/CS papers.

This advanced skill is designed for security research and CTF environments to trace subtle divergences in request handling across multiple network hops (proxies, gateways, backend servers). It focuses on identifying vulnerabilities stemming from differential interpretation of headers, path normalization, transfer framing (CL/TE), and host derivation, crucial for detecting request smuggling and parsing inconsistencies.

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 decodes complex pipe-delimited HL7 v2.x messages (including ADT, ORU, MDM) into structured segments and fields. It is essential for ingesting HL7 feeds from interface engines, allowing users to surface embedded clinical narratives (OBX-5 and NTE-3) for subsequent Natural Language Processing (NLP) analysis. It also provides segment-aware de-identification capabilities to redact PHI while preserving the native HL7 message 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.

A skill for mistake-proofing code, interfaces, schemas, and state machines so that misuse cannot be expressed. It helps design type-safe APIs, audit existing code for footguns, and fix recurring bugs by closing entire error classes. Covers concepts like making invalid states unrepresentable, parsing instead of validating, and distinguishing concepts that share a primitive.

Implement bounded, idempotency-aware API throttling, backoff, and retry handling for 429 and transient 5xx responses. Includes token bucket rate limiting, Retry-After parsing, and terminal error classification.

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.