Results for “david-fincher”

Conduct authorized red team initial access using EvilGinx3 adversary-in-the-middle phishing to capture session tokens and bypass multi-factor authentication.

Filtros de química medicinal. Aplique regras de similaridade a fármacos (Lipinski, Veber), filtros PAINS, alertas estruturais, métricas de complexidade, para priorização de compostos e filtragem de bibliotecas.

VIPER v2.1 — Range-bound liquidity sniper. Enters at support/resistance when Bollinger Bands are tight, ATR is low, RSI is at extremes within the range, and volume is declining. Works when nothing is trending — the chop predator. USD-weighted OI filtering (min $5M). DSL High Water Mode (mandatory). 5-minute scan interval. Second-best performer in the Senpi zoo at +6.2% ROI.

Detects AI-generated deepfake audio used in voice phishing (vishing) attacks by extracting spectral features and classifying samples with machine learning models.

Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.

Domain-Driven Design patterns. Bounded contexts, aggregates, entities, value objects, domain events, repositories, and application services. Strategic and tactical DDD for complex business domains. USE WHEN: user mentions "DDD", "Domain-Driven Design", "bounded context", "aggregate", "value object", "domain event", "ubiquitous language", "aggregate root", "domain service" DO NOT USE FOR: database schema design - use database skills; CQRS/Event Sourcing specifics - use `event-sourcing-cqrs`

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).

Detect and respond to Adversary-in-the-Middle (AiTM) phishing attacks that use reverse proxy kits like EvilProxy, Evilginx, and Tycoon 2FA to bypass MFA and steal session tokens.

Orquestrador do plugin civel (Civil Litigation Master) e porta unica do contencioso civel. Recebe qualquer demanda em linguagem natural, identifica a fase processual e o objetivo, e DIRIME (seleciona e conduz) TODAS as skills pertinentes sem esquecer nenhuma, fechando pela suprema-corte-civel. Use quando o operador descrever uma tarefa civel sem chamar skill especifica, ou disser civel-master, novo caso civel, vou ajuizar, fui citado, recebi sentenca, quero recorrer, preciso da inicial, contestacao, replica, tutela, agravo, apelacao.

Automates reverse engineering of PE and ELF binaries by driving IDA with the WPeGPT plugin, producing structured reports on program purpose, network IoCs, suspicious functions, and vulnerability assessment.

Simulator.Company chart and dashboard specialist. Use when the user wants to create, configure, or query charts (line, bar, area), dashboards, or financial time-series visualisations on a graph layer. Trigger on any of these intents: — Creating: "create chart", "add chart", "create dashboard", "add dashboard", "build visualisation", "chart на графе", "график с данными", "дашборд", "show metrics on graph", "visualise account data", "plot actor balances". — Configuring: "change chart type", "switch to bar chart", "update chart range", "use last day", "show turnover", "change time range", "filter chart actors". — Querying: "get chart data", "show dashboard", "what charts are on this layer", "fetch chart metrics", "dashboard data".

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

Builds and trains deep learning models with PyTorch, including tensors, autograd, and neural network modules.