BenchFlow

Comprehensive clinical laboratory data harmonization for multi-source healthcare analytics. Convert between US conventional and SI units, standardize numeric formats, and clean data quality issues. This skill should be used when you need to harmonize lab values from different sources, convert units for clinical analysis, fix formatting inconsistencies (scientific notation, decimal separators, whitespace), or prepare lab panels for research.

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。

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。

High-performance numerical computing and machine learning workflows using JAX. Supports array operations, automatic differentiation, JIT compilation, RNN-style scans, map/reduce operations, and gradient computations. Ideal for scientific computing, ML models, and dynamic array transformations.

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 the relative contribution of different factors to a response variable using R² decomposition. Use when you need to quantify how much each factor explains the variance of an outcome.

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 security engineering skill for application security, penetration testing, security architecture, and compliance auditing. Includes security assessment tools, threat modeling, crypto implementation, and security automation. Use when designing security architecture, conducting penetration tests, implementing cryptography, or performing security audits.

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 when managing Maven dependencies, resolving dependency conflicts, configuring BOMs, or optimizing dependency trees in Java projects.

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。

This skill should be used when the user asks to "pentest SSH services", "enumerate SSH configurations", "brute force SSH credentials", "exploit SSH vulnerabilities", "perform SSH tunneling", or "audit SSH security". It provides comprehensive SSH penetration testing methodologies and techniques.

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。

Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

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。

**IMPORTANT**: Any change to React or Next.js code must read through this skill first. React and Next.js guidelines from Vercel Engineering covering visual instability, layout shifts, CLS, flickering, hydration issues, and font loading.

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。

Implement safety interlocks and protective mechanisms to prevent equipment damage and ensure safe control system operation.

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。

A toolkit for fuzzy string matching and data reconciliation. Useful for matching entity names (companies, people) across different datasets where spelling variations, typos, or formatting differences exist.

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。

Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.

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。

Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.

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。

Design effective excitation signals (step tests) for system identification and parameter estimation in control systems.

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 failed GitHub Action jobs for a pull request.

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 when working with Lean 4 (.lean files), writing mathematical proofs, seeing "failed to synthesize instance" errors, managing sorry/axiom elimination, or searching mathlib for lemmas - provides build-first workflow, haveI/letI patterns, compiler-guided repair, and LSP integration

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 images and multi-frame sequences using OpenAI GPT vision 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。

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。

Vercel's comprehensive UI guidelines for building accessible, performant web interfaces. Use this skill when reviewing or building UI components for compliance with best practices around accessibility, performance, animations, and visual stability.

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。

Basic usage of the General Lake Model (GLM) for lake temperature simulation. Use when you need to run GLM, understand input files, or modify configuration 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。

Use when erlang's concurrency model including lightweight processes, message passing, process links and monitors, error handling patterns, selective receive, and building massively concurrent systems on the BEAM VM.

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。

Data conditioning techniques for gravitational wave detector data. Use when preprocessing raw detector strain data before matched filtering, including high-pass filtering, resampling, removing filter wraparound artifacts, and estimating power spectral density (PSD). Works with PyCBC TimeSeries 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。