Results for “fleetdm”

Read BEFORE writing any code that builds or simulates OFDM systems. The default MATLAB patterns for OFDM (e.g., direct ifft/fft, awgn with 0 dBW power, missing symOffset) produce subtly incorrect results — always use ofdmmod/ofdmdemod instead of direct IFFT/FFT. This skill specifies the correct calling conventions for ofdmmod, ofdmdemod, ofdmChannelResponse, ofdmEqualize, and critical anti-patterns that must be avoided. Use when building OFDM transmitters or receivers, allocating subcarriers and guard bands, inserting pilots, computing SNR for OFDM, configuring fading channels (Rayleigh/Rician), estimating and equalizing channels, implementing timing and frequency synchronization, adding LDPC coding, designing resource grids, or setting up OFDM link simulations.

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 a carrier asks about fuel-card vendor selection (Comdata, EFS, WEX, RTS, Pilot Flying J, Love's Connect), discount levels, fraud control, driver authorization PINs, IFTA reporting features, cash advance, money codes, or how the card fits into payroll/settlement workflows.

ADR Fleet

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Build cross-platform Flutter apps with proven patterns for widgets, state management (Riverpod/Bloc), GoRouter navigation, performance optimization, and testing.

Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.

Use this skill when the user asks how to design or improve a fleet safety scorecard — what KPIs to track, how to weight them, telematics signals vs document compliance vs roadside outcomes, monthly cadence, driver-facing vs admin-facing dashboards, gamification considerations, peer-comparison metrics, and how a scorecard ties to insurance premiums + retention. Reference SMS Methodology v3.20 for industry-standard weights.

Frangi vesselness 3D filter: Ra formula correction, scikit-image deprecations, GPU VRAM guard

Flash a promoted BSP image to a Jetson device in RCM mode using NVIDIA's flash.sh or l4t_initrd_flash.sh toolchain.

Flutter cross-platform development guide covering widget patterns, Riverpod/Bloc state management, GoRouter navigation, performance optimization, and platform-specific implementations. Includes const optimization, responsive layouts, testing strategies, and DevTools profiling. Use when: building Flutter apps, implementing state management (Riverpod/Bloc), setting up GoRouter navigation, creating custom widgets, optimizing performance, writing widget tests, cross-platform development.

Run computational fluid dynamics simulations using Python, including Navier-Stokes equations, shallow water, and stratified flows with pseudospectral methods and HPC support.

Master Flutter development with Dart 3, advanced widgets, and multi-platform deployment.

Detect DCOM-based lateral movement by correlating Sysmon process creation and network connection events, WMI event analysis, and RPC endpoint mapper traffic to identify abuse of MMC20.Application, ShellBrowserWindow, and ShellWindows COM objects.

Use the note-management skill to capture a new fleeting note titled

Configure Fluentd and Fluent Bit for centralized log aggregation, routing, filtering, and enrichment across distributed infrastructure.

Routes legacy PWDEV Code command intents to their canonical PWDEV Flow skills, preserving history when adopting an existing .planning workspace.

Build cross-platform applications with Flutter 3+ and Dart, covering widget development, Riverpod/Bloc state management, GoRouter navigation, platform-specific implementations, and performance optimization.

Comprehensive guide to ddpm. Master the concepts, implementation, best practices, and real-world applications of ddpm in professional environments.

Issue Fleet

KOMODO v1.0 — Momentum Event Consensus. Uses leaderboard_get_momentum_events (real-time threshold crossings) to detect when 2+ quality SM traders cross momentum thresholds on the same asset/direction within 60 minutes. Confirmed by market concentration + volume. Enters with the momentum. Replaces MANTIS v1.0 and SCORPION v1.1 (both used stale position data).