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 powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

LiteParse is a fast, open-source, and local document parser designed for layout-aware text extraction from PDFs, images, and office files. It outputs structured JSON with bounding boxes and metadata, making it ideal for building advanced RAG systems, citation grounding, and complex data pipelines without needing cloud APIs. Features include OCR, batch processing, and page screenshot capture.

A comprehensive tool for extracting YouTube transcripts, searching videos, browsing channels, and monitoring new uploads. It supports detailed transcript analysis, content summarization, and playlist management by leveraging the TranscriptAPI. Ideal for researchers, content strategists, and market analysts who need structured data from video content without downloading files.

HasData is a comprehensive cloud platform designed for extracting structured public web data. It supports three core execution modes: Web Scraping API for arbitrary URLs (including JS rendering), pre-parsed Scraper APIs for known platforms (like Google or Amazon), and asynchronous Scraper Jobs for bulk or recursive crawling. Ideal for market research, e-commerce intelligence, job data collection, and local business lead generation.

Use the HasData CLI to access real-time, structured web data from major platforms. This tool provides dedicated subcommands for various APIs (including Google SERP, Amazon, Zillow, Booking, etc.), enabling users to automate data collection, scrape specific URLs, and retrieve structured information like local business listings, product pricing, and real estate data directly from the command line.

This skill automatically processes MP4 videos by extracting key frames at configurable intervals. It then applies Tesseract OCR to recognize text on these frames, generating a comprehensive, structured Markdown report. The report includes full video metadata and timestamped, searchable transcripts, making it ideal for analyzing lectures, presentations, or screencasts programmatically.

A powerful command-line utility that transforms virtually any website into a structured CLI command. By utilizing OpenClaw's browser, it allows users to scrape and extract structured data (such as JSON) from over 36 platforms—including social media, finance, and developer communities—directly from the terminal. This tool eliminates the need for complex scraping code or browser extensions, making web data accessible via simple one-liners.

A lightweight, read-only utility that analyzes past skill usage from decision logs. It aggregates skill co-occurrence frequencies, tracks meta-skill usage statistics, and surfaces router fixture data. The resulting JSON summary is highly structured and ideal for downstream LLM consumption, enabling users to understand their workflow patterns and most frequently used skills over time.

This meta-skill is designed for complex decision analysis when a user provides or references structured documents such as contracts, quotes, spreadsheets, or notices. Instead of simple summarization, it extracts key evidence, identifies risks (e.g., renewal risk, payment terms), and structures the output into decision-ready formats, such as 'sign/reject/negotiate' recommendations, evidence tables, or required next actions. It supports input from PDF, DOCX, and XLSX formats.

This meta-skill provides a complete pipeline to bootstrap a domain knowledge base from any single seed input (URL, PDF, Git repository, or free text). It automatically classifies the source type, ingests the content using a multi-search engine, persists the summary to memory, and finally generates a structured XLSX index file, making the information immediately searchable and usable.