Skill UI

A comprehensive playbook for digital forensic experts. It details systematic techniques and tools for detecting and extracting hidden data embedded across various media types, including images (LSB, PNG chunks, JPEG DCT, EXIF metadata), audio (spectrogram, DTMF), and files (polyglots, appended data). Essential for deep-dive forensic investigations.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

Analyzes options chain data by separating real calls and puts from lottery-like speculative contracts to produce more reliable put/call sentiment, anomaly detection, and expiry breakdowns for watchlists, sectors, and event-driven names.

A dedicated skill for performing manual data synchronization and knowledge recall with OpenViking. It allows users to explicitly trigger chat transcript syncing from OpenClaw sessions and perform targeted memory searches via command line, without disrupting normal conversation flow or occupying the standard context engine slot.

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 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.

The official skill collection for Longbridge Securities, offering over 125 agent skills. It provides comprehensive financial analysis across multiple markets (HK, US, A-share, SG), including real-time quotes, advanced charting, company fundamentals, portfolio management, and options analysis. Supports trilingual interaction (Simplified/Traditional Chinese/English), making it an essential resource for global investors and financial analysts.

This skill processes raw weekly CRM export files (CSV) to generate a clean, structured regional sales summary. It automatically removes duplicate records, calculates the total revenue for each defined region, and outputs the results as a standardized JSON object. Ideal for automating reporting pipelines or preparing data for dashboards.

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.

This utility serves as a stub experiment generator, primarily for demonstration purposes within meta-paper writing workflows. Given a specific topic phrase, it deterministically produces a consistent CSV file containing simulated experimental results. This function allows users to prototype and test complex paper structures and formats without needing access to real scientific data.

This tool reads a CSV file containing experimental results (x, y_baseline, y_ours). It utilizes matplotlib to generate a two-line comparative plot of these data points and outputs the visualization directly as a PDF file. It is highly useful for academic purposes, allowing users to easily compare baseline model performance against proposed methods for inclusion in research papers.