「machine」の検索結果

Make mechanical refactoring (file splits, function moves, module extractions, renames) machine-checkable instead of eyeballed. Reproduce a relocation commit byte-for-byte from faithful primitives, and split an extraction into a verifiable prepare + move + postpare. Use when doing or reviewing such changes.

日本語の概要は準備中です。原文の説明を表示しています。

Machine-learning predictive strategy based on sklearn walk-forward training, feature engineering, and signal generation. Suitable for any OHLCV data.

日本語の概要は準備中です。原文の説明を表示しています。

Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.

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Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.

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Detect AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models, supporting batch audio analysis, confidence scoring, and forensic reporting. Use for deepfake voice detection, vishing investigations, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

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Implement User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch to build behavioral baselines, calculate anomaly scores, perform peer group analysis, and alert on insider threat indicators such as data exfiltration, privilege abuse, and unauthorized access. Use when building or tuning a UEBA pipeline rather than a one-off manual hunt.

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Detect anomalies in DNP3 communications used in SCADA/ICS systems by monitoring unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic using deep packet inspection and machine learning approaches. Use when securing energy-sector or other OT/ICS networks, investigating suspicious DNP3 master/outstation activity, or building an anomaly-based IDS for industrial control traffic.

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Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.

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Trigger machine account authentication with PetitPotam (MS-EFSR) and Coercer (MS-RPRN, MS-DFSNM, MS-FSRVP, MS-EVEN) via Coercer's scan/coerce/fuzz modes, feeding the coerced NTLM auth into a relay against AD CS Web Enrollment (ESC8), LDAP (RBCD), or SMB. Use in authorized engagements to complete a coercion-relay chain against a Domain Controller, or to validate coercion detections and signing/EPA mitigations.

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Parse Windows LNK shortcut files to extract target paths, MAC timestamps, volume serial numbers, and machine identifiers for forensic timeline reconstruction. Use when investigating recently-accessed files, tracking removable media or network paths referenced by shortcuts, or building a DFIR timeline from LNK artifacts.

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AI agent payment infrastructure — ACTP escrow, x402 instant payments, USDC settlement on Base L2. Interactive onboarding: asks your preferences, generates customized agent code, and verifies setup. Covers provider/requester patterns, adapter routing, 8-state machine, pricing, disputes, and identity.

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Use when the user asks to generate images via the Luma AI API (Dream Machine / Photon); collects a prompt and options interactively, then calls the API using the bundled script. Requires LUMA_API_KEY — will prompt the user if missing.

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Render clean blueprint-style SVG diagrams from JSON specs. Use when users ask to draw, sketch, or diagram a request flow, neural net, transformer block, system architecture, state machine, data pipeline, or any node-and-edge technical visual they want as an SVG for docs, READMEs, posts, or slides.

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The first run: install the Curviate CLI, authenticate this machine with `curviate setup`, confirm with `curviate doctor`, connect a LinkedIn account, and prove the whole path works by reading the same profile live and then from the store. Use when Curviate is being set up for the first time, when `curviate` is not installed or not authenticated, when a command fails with a credential or account error, or when someone asks how to get started. Reads only: it issues no write of any kind.

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Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

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Distributed training orchestration across clusters. Scales PyTorch/TensorFlow/HuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.

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Use when the user wants to run the cognee API server (and optional UI) on their own machine — starting it, checking it's healthy, connecting the SDK or other clients to it, and choosing the right auth posture.

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Health smoke tests + auto-fix for gbrain installs (and OpenClaw services when present). Run after machine/container restarts or whenever something seems broken. Tests critical services, auto-fixes bounded local issues, and reports worker topology without starting daemons. Extensible via user-defined test scripts in ~/.gbrain/smoke-tests.d/*.sh.

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Publish the user's own gbrain over MCP so other devices, desktop apps and cloud agents can reach it. `gbrain mcp expose` installs and signs in Tailscale, publishes the running `gbrain serve --http` on the tailnet (HTTPS, tailnet-only by default; Funnel only when a client lives in a vendor cloud), keeps the server alive as a user service, and hands back the MCP URL. Then register one least-privilege client per consumer, choose native OAuth/PKCE or a private machine handoff for that client, and verify the connection inside the intended harness.

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Create, use and clean up cua sandboxes (disposable Linux or macOS computers) locally or in the Cua cloud with the `cua` CLI or the cua SDK, and browse the web inside one. Use when a task needs an isolated machine to run code, test an app, drive a desktop GUI, browse or test a website, fill a web form, take a web page screenshot, or reproduce something without touching the user's own computer.

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Use when you need to visually interact with a GUI: test buttons, fill forms, verify visual layouts, fuzz web pages, automate user flows, take screenshots, or perform end-to-end QA on any application. Works on cloud VMs, Docker containers, local machines, and sandboxes. Needs the `cua` CLI (curl -fsSL https://cua.ai/install.sh | sh).

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This skill should be used to run a sandboxed deep verification of a qwen-code PR — "/verify-pr <n>", "深度验证这个 PR", A/B load-bearing proof against the base build, mock-free harnesses with wire oracles, and targeted gates — producing tmp/pr<n>-verify-<ts>/report.md plus a machine-readable verdict. Designed for the token-free CI verify job; also usable locally.

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Diagnose frozen, stuck, or slow Qwen Code sessions on this machine. Scans for problematic processes, high CPU/memory usage, hung subprocesses, and debug logs. Use /stuck or /stuck <PID> to focus on a specific process.

日本語の概要は準備中です。原文の説明を表示しています。