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概要と使いどころ
Ask which skill or flow fits your situation. A route through AOS's skills for work that travels idea to ship, plus a catalogue of the available options for lookup. Use when unsure where to start, or when picking between overlapping choices.
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 REST and GraphQL API design principles to build intuitive, scalable, and maintainable APIs that delight developers and stand the test of 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。
When creating, updating, and merging code in a collaborative version control environment.
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。
Apply platform accessibility best practices to SwiftUI, UIKit, and AppKit code. Essential companion to any SwiftUI, UIKit, or AppKit skill — always use together. Use whenever writing, editing, or reviewing ANY SwiftUI views, UIKit view controllers, AppKit views/window controllers, or platform UI — even when the user doesn't mention accessibility. Also use when the user mentions VoiceOver, Voice Control, Dynamic Type, Reduce Motion, screen reader, a11y, WCAG, accessibility audit, Nutrition Labels, accessibilityLabel, UIAccessibility, NSAccessibility, assistive technologies, or Switch Control. Not for server-side Swift, non-UI packages, or CLI tools.
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。
Manual testing + bug filing for Enterprise / SaaS products. Parses a Feishu test doc (scope + repo + bitable URL), observes staging via claude-in-chrome, captures DOM/console/network/screenshot evidence, then dual-tracks each finding — Feishu Bitable record (with attachments) + GitHub issue (<REPO>#<ISSUE_NUMBER> format — English-primary, Chinese collapsed, R2-hosted screenshots, matching type label). Enforces dedup across bitable + GitHub issues + PRs, and mandatory P0/P1/P2 priority prompt when unclear. Trigger whenever the user says "test and report", "test and report side-by-side", "start system testing", "experience staging", "collaborate on bug filing", "collect feedback", a shared Feishu testing doc, a bitable URL + finding, or any UX/bug needing both bitable + GitHub tracking. Do NOT use for writing product code, PR review, CI debugging, or general "how do I use this product" questions — those go to normal dev / PR-review / support flows.
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。
Improve unclear UX copy, error messages, microcopy, labels, and instructions to make interfaces easier to understand. Use when the user mentions confusing text, unclear labels, bad error messages, hard-to-follow instructions, or wanting better UX 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。
Implements data loss prevention policies using Microsoft Purview to protect sensitive information across Exchange Online, SharePoint, OneDrive, Teams, endpoint devices, and Power BI. The analyst configures sensitivity labels with encryption and content marking, creates DLP policies using built-in and custom sensitive information types with regex patterns, Dağıt:s endpoint DLP rules to control file operations on Windows and macOS devices, and monitors policy effectiveness through Activity Expl...
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 database administrator specializing in high-availability systems, performance optimization, and disaster recovery. Masters PostgreSQL, MySQL, MongoDB, and Redis with focus on reliability, scalability, and operational excellence.
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。
Automated WCAG 2.1 AA accessibility testing with axe-core and Lighthouse CI. Auto-detects frontend framework (React, Next.js, Vue, Angular, Svelte, Astro, Flutter, React Native), discovers all routes and interactive components, installs Playwright + axe-core for page-level scanning and jest-axe/vitest-axe for component-level testing. Generates tests for color contrast (4.5:1), alt text, form labels, ARIA attributes, heading order, landmark regions, focus visibility, keyboard navigation (tab order, focus traps, modal focus management, skip-to-content), screen reader compatibility (aria-live regions, error announcements, toast notifications), and Flutter Semantics validation (48dp touch targets, semanticLabel). Reports violations by severity (critical, serious, moderate, minor) with WCAG criterion references. Use when adding a11y testing, auditing accessibility compliance, fixing contrast issues, or validating keyboard and screen reader support.
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 the user asks about ARIA, ARIA roles, ARIA attributes, aria-label, aria-labelledby, aria-describedby, aria-expanded, aria-hidden, aria-live, aria-required, aria-invalid, "WAI-ARIA", "ARIA Authoring Practices", "APG pattern", "accessible name", "accessible description", live regions, custom widgets, dialogs, menus, tabs, accordions, comboboxes, tooltips, tree views, carousels, or is building any custom interactive component that requires ARIA roles and keyboard behavior beyond native HTML elements.
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。
Adds accessibility annotations (ARIA labels, roles, alt text) to make web content accessible. Use when user asks to "add accessibility", "make accessible", "add aria labels", "wcag compliance", or "screen reader support".
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。
Technical research methodology with YAGNI/KISS/DRY principles. Phases: scope definition, information gathering, analysis, synthesis, recommendation. Capabilities: technology evaluation, architecture analysis, best practices research, trade-off assessment, solution design. Actions: research, analyze, evaluate, compare, recommend technical solutions. Keywords: research, technology evaluation, best practices, architecture analysis, trade-offs, scalability, security, maintainability, YAGNI, KISS, DRY, technical analysis, solution design, competitive analysis, feasibility study. Use when: researching technologies, evaluating architectures, analyzing best practices, comparing solutions, assessing technical trade-offs, planning scalable/secure 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。
Execute model inference on GPU cloud providers. Handles code generation, deployment, execution, and result collection across HF Inference API/Endpoints, Colab, Modal, beam.cloud, Vast.ai, and RunPod. Use when running models on GPU, deploying to cloud, executing notebooks, or troubleshooting GPU execution failures. Triggers on "run on GPU", "execute model", "deploy to modal", "colab notebook", "beam deploy", "HF inference", "HF endpoints", "vast", "runpod".
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 or edit Japanese articles, reports, guides, and public-facing drafts so the argument is clear and the prose reads naturally. Preserve the author's meaning, evidence, and voice when revising. Use for requests to write from a topic or notes, remove AI臭 / AIっぽさ, improve awkward wording or structure, or format notes as 箇条書き, チーム共有, 社内共有, a decision note, handoff, Slack post, or Teams post. For existing prose, ask Gemini through Antigravity CLI to rewrite naturally by default, then audit against the source; use the current model if unavailable. Do NOT use for ordinary Japanese answers, progress updates, technical explanations, code review, English-only prose that is not a bullet-formatting request, or an explicitly incompatible format.
Collects and synthesizes open-source intelligence (OSINT) about threat actors, malicious infrastructure, and attack campaigns using publicly available data sources, passive reconnaissance tools, and dark web monitoring. Use when investigating external threat actor infrastructure, performing pre-engagement reconnaissance for authorized red team assessments, or enriching CTI reports with publicly available adversary context. Activates for requests involving Maltego, Shodan, OSINT framework, SpiderFoot, or infrastructure reconnaissance.
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 collaborative forensic incident timelines using Timesketch to ingest, normalize, and analyze multi-source event data for attack chain reconstruction and investigation documentation.
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 at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
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。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL 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。
Pharmacokinetic (PK) analysis of concentration-time data — non-compartmental analysis (NCA) for Cmax, Tmax, AUC (0-t and 0-∞), terminal half-life, clearance (CL), volume of distribution (Vd), MRT, and absolute bioavailability (F). Also one-compartment fitting. Use when you have plasma/serum drug concentrations over time after a dose and need PK parameters, or to compute bioavailability from IV + oral AUCs. NOT for ADMET property prediction from structure (use tooluniverse-admet-prediction).
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 ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab 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。
Designs and analyzes stable-isotope-resolved metabolomics (SIRM / isotope tracing / fluxomics) experiments that measure metabolic ACTIVITY via 13C/15N/2H tracers, distinct from steady-state pool profiling. Covers tracer choice, isotopologue vs isotopomer, mass-isotopomer distributions (MID), fractional enrichment, the mandatory natural-abundance + tracer-purity correction (IsoCor, AccuCor), and the metabolic/isotopic steady-state vs non-stationary (INST-MFA) distinction. Use when feeding a labeled tracer and interpreting labeling patterns, correcting raw isotopologue intensities, computing or plotting an MID, or deciding tracing vs abundance profiling. For absolute pool concentration and MRM mechanics see metabolomics/targeted-analysis; for constraint-based genome-scale flux (FBA, not empirical tracing) see systems-biology/flux-balance-analysis; for feature detection see metabolomics/xcms-preprocessing; for pathway enrichment that ignores the pool-vs-flux caveat see metabolomics/pathway-mapping.
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。
Generate a PDF packing slip and a PNG shipping label from the same order data in a single fulfillment workflow.
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。
Generate a compact PDF shipping label with sender and recipient addresses, barcode, and tracking number.
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。
Generate a branded PDF report with custom client logo placeholder, colors, and content sections for white-label agency delivery.
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。