データ・AIのスキル

World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.

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。

Lomb-Scargle periodogram for finding periodic signals in unevenly sampled time series data. Use when analyzing light curves, radial velocity data, or any astronomical time series to detect periodic variations. Works for stellar rotation, pulsation, eclipsing binaries, and general periodic phenomena. Based on lightkurve library.

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 python package for running the GaMMA earthquake phase association algorithm. The algorithm expects phase picks data and station data as input and produces (through unsupervised clustering) earthquake events with source information like earthquake location, origin time and magnitude. The skill explains commonly used functions and the expected input/output format.

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。

Analyze geospatial data using geopandas with proper coordinate projections. Use when calculating distances between geographic features, performing spatial filtering, or working with plate boundaries and earthquake 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。

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。

Reference for the TRL (Transformer Reinforcement Learning) library codebase. Use proactively before reading or editing any file under `trl/` so you have the intended contracts and invariants in mind, not just what the current code says. Covers trainer hierarchy (SFT, DPO, GRPO, KTO), shared utility functions (selective_log_softmax, decode_and_strip_padding, padding helpers), configuration system, model wrappers, and how data flows through any TRL trainer.

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 for parsing structured unit commitment input data from JSON, CSV, benchmark cases, spreadsheets, databases, or nested tables; finding fields for time periods, resources, load, reserve, generator limits, initial conditions, startup data, renewable availability, and production costs without assuming one source-specific schema.

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。

Build deterministic, verifiable data visualizations with D3.js (v6). Generate standalone HTML/SVG (and optional PNG) from local data files without external network dependencies. Use when tasks require charts, plots, axes/scales, legends, tooltips, or data-driven SVG output.

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。

Parse SQLite databases into structured JSON data. Use when exploring unknown database schemas, understanding table relationships, and extracting map data as JSON.

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。

Guidance for analyzing network packet captures (PCAP files) and computing network statistics using Python, with tested utility functions.

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 data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.

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。

Power system network data formats and topology. Use when parsing bus, generator, and branch data for power flow analysis.

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。

Choose and implement clustering algorithms for grouping speaker embeddings after VAD and embedding extraction. Compare Hierarchical clustering (auto-tunes speaker count), KMeans (fast, requires known count), and Agglomerative clustering (fixed clusters). Use Hierarchical clustering when speaker count is unknown, KMeans when count is known, and always normalize embeddings before clustering.

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。

Build unified multi-level category taxonomy from hierarchical product category paths from any e-commerce companies using embedding-based recursive clustering with intelligent category naming via weighted word frequency analysis.

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。

Combine visual features (face detection, lip movement analysis) with audio features to improve speaker diarization accuracy in video files. Use OpenCV for face detection and lip movement tracking, then fuse visual cues with audio-based speaker embeddings. Essential when processing video files with multiple visible speakers or when audio-only diarization needs visual validation.

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。

Train GPT-2 scale models (~124M parameters) efficiently on a single GPU. Covers GPT-124M architecture, tokenized dataset loading (e.g., HuggingFace Hub shards), modern optimizers (Muon, AdamW), mixed precision training, and training loop implementation.

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 this skill when reading sensor data from CSV files, writing simulation results to CSV, processing time-series data with pandas, or handling missing values in datasets.

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。

Geospatial routing data handling for depot and station coordinates, route node IDs, internal index mappings, great-circle distance matrices, and route-distance reconstruction. Use when optimization or reporting tasks involve latitude/longitude, station IDs, depots, distance metrics, vehicle routes, or validating travel distance from reported paths.

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。

Analyzes 3D mesh files (STL) to calculate geometric properties (volume, components) and extract attribute data. Use this skill to process noisy 3D scan data and filter debris.

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 this skill to build a multi-element Excel dashboard — Dashboard sheet on open, multiple formula-driven KPI cards, multiple charts, sparklines, and conditional formatting — from CSV or tabular input. Trigger on: 'dashboard', 'KPI dashboard', 'analytics dashboard', 'executive dashboard', 'metrics dashboard', 'CSV to dashboard', 'data visualization'. Output is a single .xlsx. Scene-layer on officecli-xlsx: inherits every xlsx hard rule. DO NOT invoke for: a single budget tracker / one-sheet CSV-with-formatting (use xlsx), a 3-statement / DCF / LBO financial model (use financial-model), a weekly report with ≤ 1 chart and < 10 rows (use xlsx).

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。

Master Unity ECS (Entity Component System) with DOTS, Jobs, and Burst for high-performance game development. Use when building data-oriented games, optimizing performance, or working with large entity counts.

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。

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems. Use PROACTIVELY for database optimization or complex analysis.

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。

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

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。