Results for “discrete-event-simulation”

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

Process-based discrete-event simulation in Python with SimPy — processes, queues, shared resources, and time-based events. Use when simulating systems where entities contend for shared resources over time, such as manufacturing systems, service operations, network traffic, or logistics. Part of the AlterLab Academic Skills suite.

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

Framework de simulação de eventos discretos baseado em processos em Python. Use essa habilidade ao construir simulações de sistemas com processos, filas, recursos e eventos baseados em tempo, como sistemas de manufatura, operações de serviço, tráfego de rede, logística, ou qualquer sistema onde entidades interagem com recursos compartilhados ao longo do tempo.

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.

Build stateful, coordinated applications on Cloudflare's edge using Durable Objects with SQLite storage, RPC methods, alarms, and WebSockets.

Build serverless background jobs, event-driven workflows, and durable execution with Inngest, without managing queues or workers.

Designs chaos experiments, creates failure injection frameworks, and facilitates game day exercises for distributed systems — producing runbooks, experiment manifests, rollback procedures, and post-mortem templates.

Simulator.Company chat & messaging specialist — sending messages to a user, and creating or reusing p2p (1:1) and group chats. In Simulator a chat is an actor of the Events system form (data.chatType = "p2p" | "group"), its participants are the actor's access-rule members, and its messages are `comment` reactions. Use when the user wants to "message", "write to", "DM", "send a message to" someone, "open a chat with", "start a conversation", or "reply in the chat". Activate on "write a message to user N", "send N a message", "open a chat with", "message someone", "напиши повідомлення користувачу", "надішли повідомлення", "відкрий чат з", "почни розмову з", "напиши юзеру", "напиши сообщение пользователю", "отправь сообщение", "открой чат с", "напиши в чат". For comments/approvals on an arbitrary actor (not a chat) use `simulator-reactions`; for files use `simulator-attachments`; for sharing/access in general use `simulator-access`.

Builds real-time incident response dashboards in Splunk, Elastic, or Grafana to provide SOC analysts and leadership with situational awareness during active incidents, tracking affected systems, containment status, IOC spread, and response timeline.

Use this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham", "Callaway and Sant'Anna", "de Chaisemartin", "Borusyak", "did_multiplegt", "fixest", "did2s", "bacon decomposition", or any reference to plotting coefficients around a treatment event. Also trigger when the user uploads panel data and wants to estimate treatment effects with variation in treatment timing. All code is in R.

Econometrics skill for Difference-in-Differences (DID) analysis. Activates when the user asks about: "difference in differences", "DID", "DiD", "diff-in-diff", "parallel trends", "treatment group", "control group", "pre-treatment", "post-treatment", "policy evaluation", "natural experiment", "staggered DID", "event study regression", "two-way fixed effects DID", "callaway santanna", "sun and abraham", "双重差分", "倍差法", "平行趋势", "处理组", "对照组", "政策评估", "事件研究", "交错DID", "渐进处理"