Skill UI

This comprehensive guide provides expert knowledge on implementing structured data and schema markup using schema.org standards. Learn how to correctly use JSON-LD to help search engines understand your content, enabling rich results like star ratings, FAQs, and product details. It covers best practices, common schema types (Article, Product, FAQPage), and essential validation methods for maximizing search visibility.

This powerful tool compares identical tables across two SQL Server instances (e.g., Production vs. Staging). It efficiently detects missing rows, extra columns, schema drifts, and data mismatches. Leveraging Python and Apache Arrow for fast columnar data transfer, it provides a comprehensive reconciliation report, making it essential for data migration validation and ETL verification.

Bilig WorkPaper provides a code-first runtime environment for complex spreadsheet business logic. It allows agents to interact with structured data using formulas and JSON workbooks via deterministic API calls (MCP/TypeScript). This is ideal for replacing unreliable UI automation in use cases like quote calculators, budget modeling, and data validation, ensuring calculation integrity through explicit read-back proof.

This skill provides a comprehensive and reproducible execution workflow for matched tumor-normal pair analyses using the nf-core/pacsomatic pipeline. It manages the entire process, including input validation (BAM files), generating pacsomatic-compliant samplesheets, creating reproducible launch artifacts (params YAML), and supporting execution submission to various schedulers (Slurm, LSF, PBS). Use this skill for dry-run validation, local execution, or job submission, ensuring consistent and traceable results.

This demo skill verifies the compatibility of MDA-compiled SKILL.md files within the Claude Code environment. It confirms that the skill's frontmatter, metadata structure, and allowed tools are correctly parsed and honored by the loader, ensuring smooth integration of compiled content into the agent workspace. It is a technical utility for development validation.

SocialClaw is a powerful, agent-first skill designed for comprehensive social media automation. It allows users to schedule and publish content across 13 major platforms (including X, LinkedIn, Instagram, TikTok, and more) using a single workspace API key. Streamline your marketing campaigns by managing content creation, media uploads, scheduling validation, and performance analytics from one central workflow.

This skill provides a rigorous, operational framework for product and user research. It helps researchers select the appropriate study method (generative, evaluative, validation) based on goals and product stages. It also calculates defensible sample sizes and saturation levels, and synthesizes raw observations into governed, evidence-based insights, ensuring all findings are rooted in cross-participant recurrence.

This skill guides the design of robust, production-grade data extraction pipelines, moving beyond brittle one-off scripts. It supports three modes: API-driven web scraping (Firecrawl), traditional local Python extraction (PDF, CSV), and hybrid workflows. Key features include intelligent routing, mandatory data validation, secure key management, and adherence to scraping best practices.

This skill helps manage and execute steps within a Test-Driven Development (TDD) workflow cycle. Note that this skill serves as an alias for `tdd-workflows-tdd-cycle`, maintaining discoverability. It guides users through the complete TDD development cycle, ensuring structured testing and validation steps.

Provides a comprehensive, step-by-step workflow for developers to integrate new Azure Backup commands into the Azure Backup MCP toolset. It covers the entire development lifecycle, including option definition, service implementation, command logic, command registration, input validation, writing unit tests, and executing live/recorded tests, ensuring high quality and adherence to best practices before PR submission.

An internal tool designed to validate candidate meta-skills (SKILL.md) before registration. It performs comprehensive structural linting (G1 rules) and a scheduler dry-run (G2) to ensure the workflow is deterministic, references are correct, and templates resolve properly. It outputs detailed JSON diagnostics.

A specialized internal helper for deep meta-stack trace investigation in Python environments. This tool guides users through generating Python-specific root-cause checks, creating minimal reproducible test cases using pytest, and identifying precise targets for defensive patches (e.g., validation or guard clauses). It ensures robust debugging and testing practices.