「ray data」の検索結果

PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.

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Queries the live CARLA world and resolves an entity description into a concrete actor id — find actors by type, role, or colour, or the single one nearest a point/actor; read the world snapshot; get static level bounding boxes by label; cast rays for semantic points; project a point to the ground; or (ros-topics) list the native ROS 2 topics each actor should be publishing and why any is silent. Identifies by stable attributes, not by any rank/order. Use when the user asks "what actors are here", "find the red prius", "the vehicle nearest the ego", "list the pedestrians", or needs a specific actor id for another skill.

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Create a dark monochrome procedural background with enlarged square pixels and visible Bayer-style ordered dithering. Use when a page needs an atmospheric near-black dither field, broad organic waves or cloud masses, and restrained gray-white highlights behind framed UI, hero content, or data overlays.

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Meme coin and token security audit — rug pull detection (honeypot, hidden mint, fee manipulation, LP lock bypass), Solana SPL token analysis (freeze authority, mint authority, metadata mutability), Token-2022 extension risks (transfer hooks, permanent delegate), DEX liquidity pool attacks (sandwich amplification, LP drain, bonding curve exploits), pump.fun/Raydium/Jupiter integration risks, token_scanner.py automation, and real exploit examples from 2024-2025. Use for any token audit, rug pull assessment, meme coin security review, or pre-investment due diligence.

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Meme coin and token security audit — rug pull detection (honeypot, hidden mint, fee manipulation, LP lock bypass), Solana SPL token analysis (freeze authority, mint authority, metadata mutability), Token-2022 extension risks (transfer hooks, permanent delegate), DEX liquidity pool attacks (sandwich amplification, LP drain, bonding curve exploits), pump.fun/Raydium/Jupiter integration risks, and real exploit examples from 2024-2025. Use for any token audit, rug pull assessment, meme coin security review, or pre-investment due diligence.

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Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.

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Retrieve the list of root GameObjects in the specified opened scene (or the active scene when `openedSceneName` is empty). Supports token-saving path-scoped reads over the root-GameObjects array via `paths` or `viewQuery`. Use 'scene-list-opened' to enumerate scenes.

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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. Automatically checks system RAM/GPU before loading the model, 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. For classical statistical time series models (ARIMA, SARIMAX, VAR) use statsmodels; for time series classification/clustering use aeon.

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Structured logging extensions for Golang using samber/slog-**** packages — multi-handler pipelines (slog-multi), log sampling (slog-sampling), attribute formatting (slog-formatter), HTTP middleware (slog-fiber, slog-gin, slog-chi, slog-echo), and backend routing (slog-datadog, slog-sentry, slog-loki, slog-syslog, slog-logstash, slog-graylog...). Apply when using or adopting slog, or when the codebase already imports any github.com/samber/slog-* package.

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Sets up CloudWatch observability for the first time - Omni (CloudWatch Application Observability) and classic CloudWatch. Omni: creating a Space or Domain; access grants (who has access, at what level) and access profiles bounding async alerts, integrations, or agents; instrumenting an app or AI agent with the plain ADOT SDK so traces reach Omni (Python/Node/Java/.NET on EC2/ECS/EKS/Lambda), incl. no-image-rebuild and .NET CoreCLR vars; ingesting Azure telemetry via the CloudWatch agent on an Azure VM or AKS; connecting Slack to a Space; whether GitHub or a custom MCP tool server (HTTP/stdio; API key, bearer, OAuth2) can be connected. CloudWatch: onboarding a service to Application Signals - ADOT auto-instrumentation, the amazon-cloudwatch-observability add-on, monitored service, reporting telemetry, ServiceEvents, CI/CD git/deployment metadata, Terraform/manifest edits. For using what is set up - queries, dashboards, alarms, Omni alerts, X-Ray, synthetics, Dynamic Instrumentation - use aws-observability.

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Builds, configures, debugs, and optimizes AWS observability - operator-symptom questions and detecting Omni vs classic CloudWatch. CloudWatch on an already-reporting service: Log Insights, metric/composite/anomaly alarms, custom metrics/EMF, dashboards, X-Ray/ADOT tracing, canaries, CloudTrail, Dynamic Instrumentation (live breakpoints/snapshots), the Application Signals service map, and fleet health views. CloudWatch Omni on an existing Space: SQL over logs and traces, PromQL over metrics, Omni dashboards, Omni alerts, context graph for root cause, programmatic/IaC access (API/SDK/CLI/CloudFormation), driving Omni from a coding agent or skills, and evaluating AI agent quality from traces - on-demand and online scoring of live traffic, readback, custom evaluators. For first-time setup - creating an Omni Space, granting access, ingestion, or ADOT instrumentation for Application Signals (ServiceEvents, CI/CD metadata) or Omni - use setting-up-cloudwatch-observability. Not for app logging or threat detection.

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Audit and fix visual hierarchy, spacing, color, and depth in web UIs. Use when the user mentions "my UI looks off" (or amateur/unprofessional), "fix the design", "Tailwind styling", "color palette", "visual hierarchy", "design system", "spacing scale", or "component styling". Also trigger when building consistent design tokens, creating dark mode themes, improving data-visualization clarity, or polishing UI details before launch. Covers grayscale-first workflow, constrained design scales, shadows, and component styling. For typeface selection, see web-typography. For usability audits, see ux-heuristics.

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IDAPython and IDALib script reference for reverse engineering. Activate when the user needs to write IDAPython scripts in IDA, use IDALib for headless analysis, operate on IDB databases, debug with IDA, manipulate memory/registers, traverse functions/blocks/instructions, work with Hex-Rays decompiler API, handle obfuscation, or batch-process binaries.

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Generate A2UI v0.8 surfaces for the OpenClaw Windows tray node, which renders A2UI natively in WinUI/XAML (not a WebView). Read this when pushing canvas.a2ui.push/canvas.a2ui.pushJSONL to a Windows node - covers the 18 supported components (Row, Column, List, Card, Tabs, Modal, Divider, Text, Image, Icon, Video, AudioPlayer, Button, CheckBox, TextField, DateTimeInput, MultipleChoice, Slider), the icon-name enum, the A2UIValue tagged union, JSONL envelope shapes, and action.context security / dataBinding rules. Catalog-strict: components outside this list render as the fallback Unknown placeholder.

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Generate meaning-carrying editorial data-illustrations in the monotykamary / Linear aesthetic (near-black grayscale, Inter display + mono labels, hairline framed figures) with a single coral accent. This is a GENERATIVE GUIDE, not a template gallery: it teaches the "claim -> geometry" method so any session can invent the RIGHT figure for what a post actually argues, then render it as a self-contained, theme-aware, reduced-motion-safe HTML/SVG figure. Use when writing/illustrating a blog post, essay, spec, or slide and you want a chart, diagram, or illustration that carries a thesis (not decoration) in the house style. Triggers: "make a chart/diagram/illustration for this", "visualize this argument", "monotykamary/Linear style figure", "editorial graphic".

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Extract structured data matching a JSON schema from websites. Handles complex nested schemas, arrays, pagination, and validation. Always outputs via formatOutput.

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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.

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Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.

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Extract features from 1D signals using signalTimeFeatureExtractor, signalFrequencyFeatureExtractor, and signalTimeFrequencyFeatureExtractor. Use when computing time-domain features (amplitude, energy, shape factors), frequency-domain features (spectral location, power, bandwidth, PSD), or time-frequency features (spectral shape, instantaneous, ridges, wavelet, EMD-derived) on a per-frame basis. Use when the user asks to "extract features", "compute spectral features", "build a feature table for a classifier", "get per-frame statistics", "run feature extraction on this signal", or describes a vibration / biosignal / radar / sensor signal needing features for downstream ML or analysis. Includes optional GPU acceleration via canUseGPU and gpuArray. Does not cover filter design, audio-specific feature extraction (use audioFeatureExtractor in Audio Toolbox instead), batch dataset orchestration, or 2D / image features.

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PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.

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Annotate bacterial and archaeal genomes and plasmids with Bakta's Prodigal/HMM/diamond pipeline. Identifies CDS, ncRNA, tRNA, rRNA, tmRNA, sORFs, CRISPR arrays, oriC/oriV/oriT, and gaps against a curated UniRef-derived database. Produces NCBI-compatible GFF3, GenBank, EMBL, JSON, FASTA, TSV, and a circular genome plot. Use Prokka for legacy pipelines or non-bacterial kingdoms; PGAP for NCBI GenBank submission.

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Use this repo skill for Dask, the Python parallel computing library, when working with lazy task graphs, schedulers, Dask Array, Dask DataFrame, Dask Bag/bytes IO, configuration, diagnostics, CLI usage, or contributor validation.

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Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.

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Think and work like an expert Herpetologist. Use when a task calls for Herpetologist judgment. Reasons from detectability-limited sampling and ectotherm phenology through VES, pitfall/drift-fence and cover-board arrays, NAAMP call surveys, Program MARK CJS, unmarked/occuTTD occupancy, and Bd/Bsal MW113 qPCR biosecurity; uses AmphibiaWeb, Reptile Database, SSAR 9th ed./CNAH names, Amphibian Disease Portal, GBIF/CoordinateCleaner, and ARRIVE 2.0 while treating weather bias, pitfall selectivity, and pathogen cross-contamination as first-class failure modes.

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