「data flow」の検索結果

Retrieve restaurants by city from the bundled dataset. Use this skill when recommending places to eat or validating dining options for a destination.

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。

Provides database transaction concurrency-control background. Use when reasoning about transactions, commits and aborts, serializability, isolation anomalies, conflicts, dependencies, and protocol families such as locking, timestamp ordering, OCC, MVCC, SSI, TicToc, hybrid protocols.

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。

Retrieve attractions by city from the bundled dataset. Use this skill when surfacing points of interest or building sightseeing suggestions for a destination.

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。

Extract CVSS (Common Vulnerability Scoring System) scores from vulnerability data sources with proper fallback handling. This skill covers understanding CVSS v3, handling multiple score sources (NVD, GHSA, RedHat), implementing source priority logic, and dealing with missing scores in security reporting.

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。

Teaches how to reason from a transaction protocol’s rules to a working model. Use when analyzing a concurrency-control paper, spec, or algorithm to identify state, metadata, invariants, operation rules, examples, counterexamples, guarantees, false aborts, unsafe commits, or tradeoffs before inspecting concrete traces.

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。

Calculate per-second RMS energy from audio files. Use when you need to analyze audio volume patterns, prepare data for silence/pause detection, or create an energy profile for audio analysis tasks.

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。

Search flights by origin, destination, and departure date using the bundled flights dataset. Use this skill when proposing flight options or checking whether a route/date combination exists.

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。

List cities for a given state using the bundled background data. Use this skill to validate state inputs or expand destination choices before flight/restaurant/attraction/driving/accommodation lookups.

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。

Transform sequential Python code into parallel/concurrent implementations. Use when asked to parallelize Python code, improve code performance through concurrency, convert loops to parallel execution, or identify parallelization opportunities. Handles CPU-bound (multiprocessing), I/O-bound (asyncio, threading), and data-parallel (vectorization) scenarios.

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。

Guide for translating Python classes, inheritance, and object-oriented patterns to Scala. Use when converting Python code with classes, dataclasses, abstract classes, inheritance, properties, static methods, class methods, or design patterns.

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。

Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.

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。

ALWAYS use this skill instead of the Read tool for PDF files. The Read tool cannot extract PDF tables properly. Use this skill when: (1) Reading ANY PDF file, (2) Extracting tables from PDFs, (3) Converting PDF tables to pandas DataFrames, (4) Processing multi-page PDFs

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。

Use scipy.optimize.curve_fit for nonlinear least squares parameter estimation from experimental 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。

Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with PyCBC for generating templates and performing matched filtering.

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。

Detect long-term trends in time series data using parametric and non-parametric methods. Use when determining if a variable shows statistically significant increase or decrease over time.

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。

Detect flood events by comparing water levels to thresholds. Use when determining if flooding occurred, counting flood days, aggregating instantaneous data to daily values, or classifying flood severity.

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。

Fit first-order dynamic models to experimental step response data and extract K (gain) and tau (time constant) parameters.

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。

Reduce dimensionality of multivariate data using PCA with varimax rotation. Use when you have many correlated variables and need to identify underlying factors or reduce collinearity.

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。

Read and process GLM output files. Use when you need to extract temperature data from NetCDF output, convert depth coordinates, or calculate RMSE against observations.

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。

Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

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。

An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms to seismic data. It is useful for annotating waveforms using pretrained SOTA ML models, for tasks like phase picking, earthquake detection, waveform denoising and depth estimation. For any waveform, you can manipulate it into an obspy stream object and it will work seamlessly with seisbench models.

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。

Multi-hop evidence search + structured extraction over enterprise artifact datasets (docs/chats/meetings/PRs/URLs). Strong disambiguation to prevent cross-product leakage; returns JSON-ready entities plus evidence pointers.

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。

Tools and techniques for detrending time series data in macroeconomic analysis. Use when working with economic time series that need to be decomposed into trend and cyclical components. Covers HP filter, log transformations for growth series, and correlation analysis of business cycles.

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。

Run Python code on cloud GPUs using Modal serverless platform. Use when you need A100/T4/A10G GPU access for training ML models. Covers Modal app setup, GPU selection, data downloading inside functions, and result handling.

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。