Skill UI

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 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.

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 module provides a comprehensive catalog of abstractions and utilities for handling API webhooks within the Webiny framework. It covers essential components such as payload types (IWebhookPayload), domain models (Webhook), delivery mechanisms, and core services like WebhookDispatcher and WebhookFactory. It supports advanced features including event routing, data validation, and secure payload signing, making it ideal for backend integration development.

This comprehensive guide details the programmatic management of semantic model refreshes using the Power BI Enhanced Refresh REST API and Fabric CLI. It covers the entire refresh lifecycle, including resolving model IDs, validating baseline data, triggering various refresh types (full, data-only, calculate), monitoring status, and performing robust post-refresh validation. Essential for automating ETL and data pipeline processes in a modern data platform.

This skill applies the RICE framework (Reach, Impact, Confidence, Effort) to objectively score and rank product initiatives. It helps teams prioritize backlogs, plan quarters, and compare multiple competing ideas by providing a structured, data-driven ranking. The output identifies quick wins, high-risk moonshots, and critical data gaps for validation.

This skill enables querying Delta Lake tables and raw files stored in OneLake using DuckDB. It supports two primary modes: local execution via CLI/Python for ad-hoc analysis, and in-notebook execution within Fabric, combining DuckDB's speed with notebook context. Ideal for data exploration, schema discovery, and data quality validation across the Fabric ecosystem.

This skill enforces a rigorous data validation protocol by establishing hard boundaries at every point data enters the system (API requests, user input, environment variables, webhooks, etc.). It mandates a four-phase process: detection, trust classification, mandatory filtering (schema validation, sanitization, type checking), and final verification. This prevents untrusted data from reaching core business logic, mitigating risks like SQL injection and XSS.

This skill utilizes NVIDIA's open-source garak framework to conduct comprehensive red-teaming assessments on Large Language Models (LLMs). It tests for critical vulnerabilities such as prompt injection, jailbreaks, data leakage, and toxic content generation by sending thousands of adversarial probes. Ideal for pre-deployment security validation, API guardrail testing, and generating defensible evidence for AI risk assessments.

This skill provides comprehensive monitoring advisory services for data assets and AI agents. It helps users assess data coverage gaps, analyze use-case criticality, and automatically create data quality monitors (table, metric, validation). Furthermore, it enables monitoring of AI agent performance, latency, and observability, ensuring robust data governance across the entire ML lifecycle.

This optimizer guides the acceleration of complex, multi-stage processes, such as CI/CD pipelines, large-scale refactoring, or data validation. It instructs users to decompose large tasks into independent execution lanes, build a dependency graph, batch read/check operations, and manage writes with strict isolation to maximize speed without sacrificing correctness. Ideal for optimizing development workflows and testing cycles.

Performs comprehensive historical backtesting for trading strategies using the high-performance neural-trader engine (Rust/NAPI). This skill supports walk-forward validation and captures key performance metrics. Critically, it Ed25519-signs the final artifact to provide cryptographic tamper evidence, ensuring data integrity for promotion from paper to live trading environments.