Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.
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。
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概要と使いどころ
Generate specialized skills for each subsystem in the monorepo. Creates shared language skills and subsystem-specific checklists for high-quality AI code generation.
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。
Commit changes, push to GitHub, and open a PR. Includes quality checks (security, patterns, simplification). Use --quick to skip checks.
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。
Institutional-quality equity research initiation reports for A-share companies. Covers company analysis, financial modeling, valuation, chart preparation, and final report assembly. Adapted from the original initiating-coverage skill for Chinese market conventions, CAS accounting, and AkShare data sources. Triggers on "A股首次覆盖", "首次评级", "initiate coverage China", "initiate on [company]", "A股研究报告", or "start research on [company]".
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。
Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed. The full decision matrix for OOM (--novram / --cache-none / --disable-smart-memory), shared-VRAM creep on Windows (--reserve-vram N), model-switching with big text encoders (--cache-none), high-VRAM throughput (--gpu-only / --highvram), and attention-backend selection (--use-sage-attention for speed, --use-pytorch-cross-attention as the highest-quality / Z-Image-safe fallback). Also the acceleration-stack + Blackwell/RTX 5000 (sm_120) notes. Use when a graph OOMs (especially long video like LTX 2 / WAN), when the GPU spills into shared VRAM and slows to a crawl, when switching between models eats all RAM, when Z-Image produces black/garbled output under Sage, or when deciding which attention backend to launch with. Flag names verified against upstream comfy/cli_args.py; see Sources.
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。
Local SEO analysis covering Google Business Profile optimization, NAP consistency, citation health, review signals, local schema markup, location page quality, multi-location SEO, and industry-specific recommendations. Detects business type (brick-and-mortar, SAB, hybrid) and industry vertical (restaurant, healthcare, legal, home services, real estate, automotive). Use when user says "local SEO", "Google Business Profile", "GBP", "map pack", "local pack", "citations", "NAP consistency", "local rankings", "service area", "multi-location", or "local search".
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。
Deep single-page SEO analysis covering on-page elements, content quality, technical meta tags, schema, images, and performance. Use when user says "analyze this page", "check page SEO", "single URL", "check this page", "page analysis", or provides a single URL for review.
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。
AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.
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。
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
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Reviews macOS Swift 6+ code for modern idioms, SOLID principles, SwiftData patterns, and concurrency best practices. Use when reviewing macOS code quality or asking about best practices.
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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。
NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages.
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standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations.
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SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.
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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.
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A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.
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This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and qua
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Spatially-aware quality control (QC) software for both spot-level and artifact-level QC in spot-based spatial transcripomics, such as 10x Visium. These methods calculate local (nearest-neighbors) mean and variance of standard QC metrics (library size, unique genes, and mitochondrial percentage) to identify outliers spot and large technical artifacts. Scales linearly with the number of spots and is designed to be used with 'SpatialExperiment' objects.
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。
performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results.
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A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided.
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This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, an
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Create high-quality pull requests via gh pr create. Use when the user wants to create a PR, submit a PR, open a pull request, submit for review, or push changes for review. Triggers on: create a pr, create-pr, submit a pr, open a pull request, submit for review, make a pr, gh pr create.
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 knowledge for Azure Carbon Optimization development including troubleshooting, security, and integrations & coding patterns. Use when using Carbon Service REST API, Python exports, RBAC roles, emissions data quality, or dashboard issues, and other Azure Carbon Optimization related development tasks. Not for Azure Cost Management (use azure-cost-management), Azure Impact Reporting (use azure-impact-reporting), Azure Monitor (use azure-monitor), Azure Policy (use azure-policy).
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。
Web search using You.com Search API with high-quality, cited results — runs keyless out of the box; YDC_API_KEY unlocks higher limits and real-time web crawling
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Improve the agent itself, or audit its recent performance - better skills, prompts, workflows, and config, plus a quality/reliability/memory-hygiene review of what it did and what failed
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。