'computer' 검색 결과

Analyze Zeek network logs to find lateral movement, when an intruder spreads between internal computers. Examine administrative file sharing, authentication across multiple hosts, and remote service operations to identify suspicious connections and reconstruct incident timelines.

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• Tracing spread to other computers after an initial compromise
• Investigating unauthorized transfers to administrative shares
• Finding one account authenticating to many internal hosts

Collect evidence from a compromised computer before shutdown or remediation erases it. Preserve memory, network connections, processes, and system state, then record integrity hashes and handling history to support incident investigation and evidence tracking.

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• Preserving evidence before shutting down a compromised computer
• Collecting memory evidence for suspected fileless malware
• Recording active connections and processes during an intrusion

Reconstruct activity on a compromised computer by combining file timestamps, event logs, browser history, and other evidence into one timeline. Extract and filter events with Plaso, then review and annotate them collaboratively in Timesketch.

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• Reconstructing the sequence of a computer compromise
• Comparing events across logs and file records
• Investigating timestamp changes and cleared logs

Analyze Windows event logs in Splunk to investigate suspicious logins, privilege changes, persistence, and movement between computers. Build detection searches and organize endpoint activity into timelines, with findings linked to MITRE ATT&CK attack techniques.

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• Investigating suspicious Windows logins
• Building detection searches in Splunk
• Creating incident timelines

Build visual AI systems that detect objects or divide images into labeled regions. This skill guides dataset preparation, model selection, training, evaluation, inference optimization, and deployment using frameworks such as PyTorch and Ultralytics.

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• Build an object detection pipeline
• Clean images and convert annotation formats
• Optimize a trained model for deployment

Let an agent launched through cmux inspect and operate macOS apps using screenshots and accessible interface elements. It supports clicking, typing, scrolling, and dragging, with explicit user requests and permissions granted to the local helper.

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• Ask an agent to click controls in a macOS app
• Enter text into forms or editors
• Check the screen after an action

Operate Chrome or Brave using signed-in browser sessions to verify deployed pages, reproduce bugs, inspect page structure, and examine errors or network traffic. An optional macOS bridge extends control to native applications and operating system input.

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• Verify page appearance after deployment
• Reproduce blank pages and disappearing content
• Inspect console errors and network traffic

Guide shell, Python, and Node.js execution on the computer running a connected A0 CLI. It explains session reuse, interactive input, output retrieval, and recovery steps when execution permissions, timeouts, or disconnections interrupt the work.

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• Run shell commands on the connected A0 CLI host
• Execute Python or Node.js on the host
• Retrieve output or answer an interactive program

Develop and migrate LangBot messaging adapters for Event-Based Agents, its event-driven architecture. Check conversion into shared event and message formats, implement platform APIs, and verify behavior through unit tests and real platform interactions.

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• Adding a messaging platform adapter to LangBot
• Migrating an existing adapter to the EBA layout
• Testing event and message conversion in both directions

Explore computer architecture and circuit design settings by repeatedly running programs, measuring results, and adjusting parameters. Compare configurations against a baseline, respect design constraints, and receive a report with the best configuration found and the search history.

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• Tune cache sizes and processor settings
• Reduce circuit area while meeting timing constraints
• Compare compiler flags for runtime or code size

Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.

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• Find models to run on a local computer
• Choose quantization for available memory
• Launch and test a local OpenAI-compatible API

Find recent arXiv papers on EEG, or brainwave, decoding and brain-computer interfaces. Evaluate candidates using five quality criteria, select the top three, and save structured Chinese and English reviews with paper metadata as Markdown files.

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• Tracking recent EEG decoding papers
• Finding research on speech decoding from EEG
• Comparing brain foundation model papers

World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.

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Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".

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Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.

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Use when a task needs an app on the user's own screen — a native desktop application (instrument control software, Origin, GraphPad, ImageJ, a reference manager, a spreadsheet), or a desktop browser window that needs window-level control. Read before the first `computer` call. Covers the observe-act-observe loop, why element indexes go stale, which action to prefer for which control, what "unverified" means and why it must never be reported as success, and the recovery for each error code. Not for page-level web automation (use the browser connector) and not for anything that can be done from the shell.

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Manage Simulink Real-Time target computers from MATLAB — list available targets, check whether a target is connected, read its lifecycle status, add/remove/rename targets, set IP addresses and credentials, choose the default target, install TLS material, configure a startup application, and run target lifecycle ops (connect, reboot, RTOS update). Use whenever the user asks "which targets are available", "is my target connected", "what's the status of the target", "add a Speedgoat", "switch default target", "change the target IP", "reboot the box", "update the target", or works with `slrealtime.Targets`, `tg = slrealtime`, `addTarget`, `removeTarget`, `getTargetNames`, `getDefaultTargetName`, `setDefaultTargetName`, `connect`, `isConnected`, `status(tg)`, `setipaddr`, `setupTLSCertificate`, `setStartupApp`, `reboot`, `update`, or `slrealtime.testSetup`. Covers Speedgoat target computers and Simulink Real-Time Linux devices.

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Use when driving a desktop GUI through a computer-use driver — windows, menus, native apps, OS dialogs. You build a table of safe actions; Jev picks the next one in about 0.4 seconds.

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Use Avibe's local Computer Use CLI for carefully bounded desktop observation and actions.

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操作 macOS 桌面应用，探测窗口和自动化接口、截图、读取或修改辅助功能元素、执行鼠标键盘动作，以及通过 CDP 操作内嵌 Chromium 页面。适用于桌面应用自动化与界面验收；普通网页任务优先使用已有浏览器工具。

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Automates desktop GUI workflows via computer use API with screenshot capture. Use when scripting GUI interactions or recording browser sessions for tutorials.

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Computer vision expert specializing in real-time hand tracking and gesture interface designUse when "hand tracking, gesture recognition, mediapipe, hand gestures, touchless interface, sign language, hand pose, finger tracking, mediapipe, hand-tracking, gesture-recognition, computer-vision, hci, touchless, ml" mentioned.

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Perform a Computer Systems Validation (CSV) assessment following GAMP 5 methodology. Covers user requirements, risk assessment, IQ/OQ/PQ planning, traceability matrix creation, and validation summary reporting. Use when a new computerized system is being introduced in a GxP environment, when an existing validated system is undergoing significant change, when periodic revalidation is required, or when a regulatory inspection demands a validation gap analysis.

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Design a compliance architecture that maps applicable regulations to computerized systems. Covers system inventory, criticality classification (GxP-critical, GxP-supporting, non-GxP), GAMP 5 category assignment, regulatory requirements traceability, and governance structure definition. Use when establishing a new regulated facility, formalising compliance across multiple systems, addressing a regulatory gap analysis, harmonising compliance after mergers or reorganisations, or preparing a site master file that references computerized systems.

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