Skill UI

A comprehensive guide detailing when and how to structure theoretical models in development economics research, particularly for World Bank Economic Review submissions. It emphasizes that models must serve a functional purpose—such as interpreting empirical coefficients, running policy counterfactuals, or aggregating to welfare statements—rather than being decorative. Key principles cover parameter identification, matching structural frictions to the local setting, and handling counterfactual assumptions (Lucas critique).

Cirq is a powerful, open-source Python framework developed by Google Quantum AI. It allows users to design, simulate, and execute complex quantum circuits. It supports advanced features including parameterized circuits, noise modeling, circuit transformation, and seamless integration with multiple quantum hardware backends (e.g., IonQ, Google Quantum, Pasqal).

A highly specialized, downstream skill for advanced threat analysis. This tool is used to recover hidden configurations, decode C2 (Command and Control) parameters, unpack staged payloads, and extract Indicators of Compromise (IOCs) from complex malware samples. It focuses on identifying the exact decode chains—including compression, encoding, and crypto steps—and linking recovered fields to observed behavioral outcomes within a sandbox environment.

This specialized skill is designed to trace and prove the complete flow of OAuth or OIDC authentication, acting as a downstream specialization after an orchestrator has established sandbox assumptions. It meticulously tracks the entire journey—from initial redirect and parameter handling (state, nonce, PKCE) through token exchange, claim mapping, and ultimately to the point where an identity or privilege is accepted by the backend service. Use this when the critical task is proving the security and mechanics of the authentication chain.

This specialized downstream skill is designed for deep analysis of complex attack paths originating from Server-Side Request Forgery (SSRF). It systematically tracks the entire exploitation chain: from the initial SSRF source (e.g., webhook, URL parameter) through internal service reachability, metadata endpoint exploitation, credential extraction, and finally, demonstrating how an attacker achieves accepted access or elevated privileges. Use this when tracing the decisive path through server-side requests and credential pivoting.

Guideline for front-end JavaScript reverse engineering using js-reverse MCP tools and jshookmcp. Covers locating signature and encrypted parameters, tracing request initiators, runtime sampling via hooks, local Node environment reconstruction, and evidence-first output for reproducing signing logic.

A systematic methodology for resolving deeply conflicting technical requirements (e.g., needing a system to be both fast and consistent). Instead of accepting a midpoint compromise or trade-off, TRIZ instructs users to name the conflict, separate the opposing states (using time, space, condition, or scale), and invent a concrete, no-compromise resolution. Ideal for architecture, API design, and complex system parameter definition.

Activates and provisions a Power Pages website in a Power Platform environment via the Power Platform REST API. Verifies PAC CLI and Azure CLI auth, gathers site parameters, confirms with the user, then posts the activation request and polls provisioning status until the site is live.

Adds an Azure DevOps connector to a Power Apps code app. Use when querying work items, creating bugs, managing pipelines, or making Azure DevOps REST API calls. Includes steps to apply a required HttpRequest body parameter fix, configure operations, build, and update the memory bank.

This skill configures the Canvas Authoring MCP server for Power Apps coauthoring sessions. It extracts parameters from the studio URL and connects the server to the specific app session. It handles authentication flows and checks for required .NET SDK versions. Use this before editing canvas apps to ensure MCP connectivity.

A comprehensive companion tool for the foundational Deep Learning textbook. It indexes 20 chapters and provides a crucial delta layer, updating core concepts from 2016 to 2026 (e.g., Transformers, Diffusion Models). The tool features four deterministic utilities: a reading-path planner, a training-failure diagnostic, a capacity planner, and a parameter/FLOP calculator. Ideal for students and practitioners who need to study the theory, understand its modern relevance, and translate academic knowledge into actionable engineering decisions.

A comprehensive guide to tuning game physics parameters, including mass, gravity, drag, and continuous collision detection (CCD). It addresses common stability issues like jitter and tunneling by correctly separating the fixed-timestep simulation (physics updates) from the variable-rate rendering (visual updates) using interpolation. Essential for creating predictable and responsive game motion across various hardware.