Agents-at-Work

This skill outlines two methods to change the root password in Linux: one when you have the current password, and another when you need to reset it without the current password.

This skill outlines the workflow for generating a Know Your Customer (KYC) report, encompassing company website research, GLEIF/LEI lookup, and adverse media/news screening to build a foundational client profile.

This skill describes how to perform an LEI search on the GLEIF website to find a company's LEI number for KYC analysis.

This skill describes how to reset a forgotten Linux root password by booting into single-user mode through the Grub menu.

This skill summarizes the "Social Media Manager Confidential" podcast, which offers realistic, positive, and insightful resources for social media managers. It covers strategies, content tips, and transparent advice for building ethical and profitable businesses that support their lifestyle goals. The podcast also extends an invitation to an exclusive, free private community for social media managers.

Understand the basic structure of an Excel file, differentiating between a workbook and its constituent worksheets, and how to navigate between them. It is foundational for anyone new to Excel or needing a refresher on its basic organization.

This skill helps users understand the basic layout of the Excel interface, including the concepts of workbooks and worksheets.

Query the SEC EDGAR database to retrieve official company filings including 10-K, 10-Q, 8-K, and proxy statements.

Analyzes 3D mesh files (STL) to calculate geometric properties (volume, components) and extract attribute data. Use this skill to process noisy 3D scan data and filter debris.

You will implement a dialogue parser that converts a given text file into a structured JSON graph. You will be given a text file `/app/script.txt`, and output a validated JSON graph `/app/dialogue.json` and visualization `/app/dialogue.dot`. You should implement a function `def parse_script(text: str)` in your parser `solution.py` that takes the script content as input and returns the parsed graph (as a dictionary or object with nodes/edges). This is required for validation. Your parser should process basic structure such as graph, node, and edge, so a given dialogue with the following input format can be parsed:

Search the web for real-time financial data, news articles, and market information using targeted queries.

Sets up a Python-based backend service using FastAPI and integrates OpenAI's API. This skill covers handling environment variables, Server-Sent Events (SSE), and multipart form data for AI applications.

Define and manage structured multi-line prompt templates for AI agents, specifically for SWE-bench tasks and multimedia skill extraction.

Extract and clean readable text content from HTML pages, including financial tables, earnings reports, and news articles.

Analyze and synthesize information from previously collected documents to extract specific financial data points and insights.

Analyze and resolve BGP oscillation and BGP route leaks in Azure Virtual WAN–style hub-and-spoke topologies (and similar cloud-managed BGP environments). Detect preference cycles, identify valley-free violations, and propose allowed policy-level mitigations while rejecting prohibited fixes.

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You are helping a research team verify the integrity of their bibliography before submitting a paper. The team suspects that some citations in their BibTeX file may be fake or hallucinated.

You will implement a dialogue parser that converts a given text file into a structured JSON graph. You will be given a text file `/app/script.txt`, and output a validated JSON graph `/app/dialogue.json` and visualization `/app/dialogue.dot`. You should implement a function `def parse_script(text: str)` in your parser `solution.py` that takes the script content as input and returns the parsed graph (as a dictionary or object with nodes/edges). This is required for validation.

Please use D3.js (v6) to visualize input data stored at `/root/data/stock-descriptions.csv` and `/root/data/indiv-stock/` for company stock details and individual stock price histories. Please return the output as a single-page web app at `/root/output/index.html`. I should be able to open it in a web browser. Also make sure to generate files organized in the paths below to support this web app: - `/root/output/js/d3.v6.min.js` - `/root/output/js/visualization.js` - `/root/output/css/style.css` - `/root/output/data/`: copy the provided input data