「develop」の検索結果

Swift language patterns and best practices including concurrency, performance, and modern idioms. Use for Swift language-level code review or architecture guidance.

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。

TDD and testing skills for iOS/macOS apps. Covers characterization tests, TDD workflows, test contracts, snapshot tests, test infrastructure, and deterministic quality gates (fitness functions, coverage ratchet, mutation testing). Use for test-driven development, adding tests to existing code, or building test infrastructure.

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。

Legal document generation and compliance guidance for indie Apple developers. Covers privacy policies, terms of service, EULAs, GDPR/CCPA/DPDP compliance, and Apple App Store legal requirements. Use when user needs legal documents or compliance guidance.

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。

Apple Search Ads campaign strategy for indie developers — paid acquisition, keyword bidding, budget planning, and ROAS optimization. Use when user asks about running ads, paid user acquisition, or Apple Search Ads campaigns.

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。

Guideline-4.3 anti-spam / originality gate — score whether an app (and each portfolio addition) is meaningfully distinct in function, content, and metadata before you invest or submit. Use at validation/new-app time and again before submission. Protects the whole developer account from 4.3 (spam/duplicate) rejections. NOT rejection-handler (that works an existing rejection) and NOT competitive-analysis (that positions in the market).

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。

Senior developer-level release review for macOS/iOS apps. Identifies security, privacy, UX, and distribution issues with actionable fixes. Use when preparing an app for release, want a critical review, or before App Store submission.

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。

Growth skills for indie Apple developers — user acquisition, analytics interpretation, press/media outreach, community building, and indie business operations. Also covers the stage-by-stage store-growth audit and the post-launch store-signals loop. Use when user asks about growing their app, auditing growth levers, understanding metrics, getting press coverage, or running an indie dev business.

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 B2B cold emails and follow-up sequences that get replies. Use when the user wants to write cold outreach emails, prospecting emails, cold email campaigns, sales development emails, or SDR emails. Covers subject lines, opening lines, body copy, CTAs, personalization, and multi-touch follow-up sequences.

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。

treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; qua

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。

SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. Univariate methods include univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. Bivariate methods include Lee's L and cross variogram. Multivariate methods include MULTISPATI PCA and multivariate local Geary's C recently developed by Anselin. The

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 basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.

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 package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in

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。

Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) provide us with a wealth of data, such as RNA-seq, DNA Methylation, SNP and Copy number variation data. It's easy to download data from TCGA using the gdc tool, but processing these data into a format suitable for bioinformatics analysis requires more work. This R package was developed to handle these 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。

mastR is an R package designed for automated screening of signatures of interest for specific research questions. The package is developed for generating refined lists of signature genes from multiple group comparisons based on the results from edgeR and limma differential expression (DE) analysis workflow. It also takes into account the background noise of tissue-specificity, which is often ignored by other marker generation tools. This package is particularly useful for the identification of g

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。

the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms.

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。

The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and enc

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 the HyperFrames CLI development loop: init, add, catalog, capture, lint, check, snapshot, compare, grade-compare, preview, play, present, beats, keyframes, single or batch render, publish, cloud, cloudrun, feedback, lambda, doctor, browser, info, upgrade, skills, compositions, docs, benchmark, telemetry, transcribe, auth, tts, and remove-background. Also use when diagnosing build or render failures. validate, inspect, and layout are deprecated aliases; use check. Covers local, HeyGen-hosted cloud, AWS Lambda, and Google Cloud Run rendering.

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 the user asks to profile how they work, adapt assistant responses to them, or refresh an existing developer profile. Generates USER-PROFILE.md from session analysis or a questionnaire.

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。

Your education and edtech brain trust. 25 named personas across 7 cells covering learning science, curriculum design, AI tutoring, higher-ed strategy, edtech platforms, assessment, and workforce development.

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 implementing any feature or bugfix, before writing implementation code

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 executing implementation plans with independent tasks in the current session

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 ChatGPT apps with MCP servers on Cloudflare Workers. Extend ChatGPT with custom tools and interactive widgets (HTML/JS UI). Use when: developing ChatGPT extensions, implementing MCP servers, or troubleshooting CORS, widget 404s, MIME types, ASSETS binding errors, Next.js integration issues, or edge platform limitations.

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 implementation is done and tests pass and the user must choose to merge locally, open a PR, keep the branch, or discard it. Presents those four options, executes the choice, and cleans up the worktree for merge and discard.

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。

Expert guidance for designing, assessing, and optimizing Azure workloads using Azure Well Architected. Covers design review checklists, recommendations, design principles, tradeoffs, service guides, workload patterns, and assessment questions. Use when designing AI/HPC/SaaS workloads, AVD/AVS deployments, WAF assessments, or App Service/AKS/SQL architectures, and other Azure Well Architected related development 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。