Results for “udf”

User-Mode Driver Framework v2 (UMDF). User-mode driver model that uses the same WDF object model as KMDF but runs in a host process (WUDFHost.exe) protected by the reflector. Required for some categories (Indirect Display Drivers, many sensor and camera drivers) and recommended for any driver that doesn't strictly need kernel mode. USE WHEN: user mentions "UMDF", "WUDFHost", "user-mode driver", "reflector", "IDD", "ISensor", "WDFHOST", "UMDF v2", "FX2" DO NOT USE FOR: KMDF (use `wdf-kmdf`), classic UMDF v1 (deprecated, COM-based)

UniFFI by Mozilla — generates idiomatic Kotlin, Swift, Python, and Ruby bindings from a Rust crate. Covers UDL definition, proc-macro mode, async support, callback interfaces, error handling, custom types, and the Kotlin Multiplatform fork (uniffi-kotlin-multiplatform-bindings) used by BDK, Breez SDK, CDK, LWK. USE WHEN: user mentions "UniFFI", "Rust to Kotlin", "Rust to Swift", "FFI bindings", "uniffi-rs", "UDL file", "uniffi-bindgen", "BDK bindings", "Breez SDK bindings", "kotlin-multiplatform-bindings", "Mozilla UniFFI" DO NOT USE FOR: Raw C FFI - use `languages/swift` interop quick-ref + Rust core DO NOT USE FOR: WebAssembly bindings - use wasm-bindgen DO NOT USE FOR: Flutter/Rust bridge - use `flutter_rust_bridge` skill if exists DO NOT USE FOR: React Native - use `uniffi-bindgen-react-native` (out of scope here)

Expert user experience researcher specializing in user behavior analysis, usability testing, and data-driven design insights. Provides actionable research findings that improve product usability and user satisfaction

Universal Reasoning Framework implementing λο.τ calculus over holarchic structures. Provides severity-based routing (R0-R3 pipelines), modular cognitive architecture (DEC, EVL, PAT, SYN, MEA, HYP, INT), fractal execution patterns, multi-level validation (η≥4, KROG), and adaptive learning. Triggers on: (1) complex multi-step reasoning, (2) high-stakes decisions requiring validation, (3) research synthesis across domains, (4) system design and architecture, (5) crisis management, (6) performance optimization. Implements scale-invariant reasoning from micro (tool calls) through meso (skill composition) to macro (orchestrated workflows).

Accelerate pandas workflows with GPU DataFrames using cuDF and dask-cuDF for ETL, joins, groupby, and large-scale data processing.

Configure Jetson UPHY lane allocation on Orin/Thor custom carriers by editing ODMDATA tokens and dispatching per-controller kernel-DT overlay skills.

Enforce code quality with Ruff in a controlled, iterative workflow: run checks with optional scope and rule overrides, apply safe and unsafe autofixes, review diffs, and resolve remaining findings.

Estimates the memory required to load Safetensors or GGUF model weights for inference from the Hugging Face Hub, using HTTP Range requests without downloading weights.

Kernel-Mode Driver Framework (KMDF), the Microsoft-recommended way to write Windows kernel-mode drivers. Covers DriverEntry, EvtDeviceAdd, IRPs and IOCTLs, I/O queues, PnP and Power state machines, IRQL discipline, memory pools, WPP tracing, SAL annotations, and Driver Verifier. USE WHEN: user mentions "KMDF", "WDF kernel", "Windows kernel driver", "DriverEntry", "WdfDriverCreate", "EvtDeviceAdd", "IRP", "IOCTL", "DISPATCH_LEVEL", "PASSIVE_LEVEL", "NTSTATUS", "PoolTag", "WdfRequestComplete" DO NOT USE FOR: UMDF v2 (use `wdf-umdf`), classic WDM-only drivers, file-system filters (FltMgr is a separate framework)

Use when planning a USENIX FAST project timeline from venue fit through choosing a Spring or Fall deadline, double-blind submission, the author-response period, shepherding or a one-shot revision, artifact evaluation, and the open-access camera-ready, with backward-planning offsets tuned to storage evaluation and honest handling of the two-deadline cycle.

Durable multi-goal workflow that persists plan/ledger artifacts under .omc/ultragoal and prints Claude /goal handoff text for the active session

Perform nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows using the umap-learn library.

Creates concise early UX discovery artifacts for PRD development, customer development, new product ideas, feature bets, and redesigns. Use when the user asks to hypothesize an ideal customer profile, generate proto-personas, write customer problem or jobs-to-be-done hypotheses, create journey maps, shape value propositions, or turn product context into design-sprint-style customer insight artifacts for product and engineering teams.

Provides a frontend design mode for strong visual direction, UI polish, redesigns, landing pages, and production-grade interface work, including shadcn-based surfaces.

Form UX

Unity / UEFN / Fortnite person pack for Content Factory. Use when the user says person unity, @person-unity, Unity person, UEFN person, Fortnite map person, or HY-World person. Auto-loads unity-expert persona plus cf-hyworld-fortnite, image-generation, and storyboarding.

Nonlinear dimensionality reduction with UMAP — fast manifold learning for 2D/3D visualization, clustering preprocessing (e.g., HDBSCAN), and supervised or parametric UMAP. Use when projecting high-dimensional data to low dimensions for visualization, embedding generation, or as a preprocessing step before clustering. Part of the AlterLab Academic Skills suite.

Audits game user experience across HUD clarity, menu navigation, tutorial effectiveness, control responsiveness, camera systems, feedback loops, loading screens, and settings completeness. Detects game engine (Unity UGUI/UI Toolkit, Unreal UMG, Godot Control, web-based), maps all UI screens and input contexts.

UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.

Use this skill for tables, grids, filters, sorting, density, keyboard navigation, responsive behavior. Trigger when the task involves web design work related to Data Table Grid UX, implementation, audits, debugging, strategy, or validation.

Reviews authorized diffs for auth, input, filesystem, network, secrets, parsers, injection, CI/CD, and supply-chain regressions.

Dcf reference tool. Use when working with dcf in finance contexts.

Use this skill for forms, checkout flows, validation, error recovery, payment UX, friction reduction. Trigger when the task involves web design work related to Forms Checkout UX, production implementation, audits, debugging, strategy, or validation.

Use the diffs tool to produce real, shareable diffs (viewer URL, file artifact, or both) instead of manual edit summaries.

Build discounted cash flow models for company valuation. TRIGGERS - Use when user needs help with dcf-model related tasks.

Audits UX quality of a codebase or validates implementation against design mockups, then fixes issues and commits changes.

First-class design intelligence. Dispatches to the best available design skill (ui-ux-pro-max, frontend-design, superpowers), then layers IJFW constraints on top. Triggers: 'design', 'redesign', 'UI', 'UX', 'dashboard', 'page', 'component', 'make it look better', 'polish', 'pretty', 'professional', 'user experience', 'layout', 'visual', 'mobile-first', 'dark mode', 'accessibility', 'colors', 'typography', 'brand'.

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.

Use when fixing, auditing, improving, or building any web UI — before reading or editing component code. Fires on contrast/a11y/overflow/responsive/hover- focus/layout complaints, polish, audit, or any visual change. Never diagnose a UI defect from code alone: render at real viewports, measure (axe / contrast / a11y tree / console / keyboard / reflow), then cite the measurement.

Queries and monitors a UniFi network via the local UniFi OS gateway API, providing device status, active clients, health, top applications, and recent alerts.

Queries and monitors UniFi network devices, clients, health, DPI traffic, and alerts via the local UniFi OS gateway API.

Walk a business/UAT approver through a plain-language demo of a verified feature and capture business acceptance signoff.

Query and monitor UniFi network devices, clients, health, top applications, and alerts via the local UniFi OS gateway API.

Estimates memory requirements for running Hugging Face models, including optional KV cache, using HTTP range requests without downloading weights.

Design user flows and screen structure using UX patterns like progressive disclosure, hub-and-spoke navigation, and information pyramids.