「aces」の検索結果

Automate Asana tasks via Rube MCP (Composio): tasks, projects, sections, teams, workspaces. Always search tools first for current schemas.

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。

Scrape e-commerce data for pricing intelligence, customer reviews, and seller discovery across Amazon, Walmart, eBay, IKEA, and 50+ marketplaces. Use when user asks to monitor prices, track competi...

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。

Create or refresh FriendlyBet Story of the World Cup image assets and app overlays. Use when generating new 9:16 World Cup story artwork, replacing story-assets PNGs, writing image-generation prompts, checking real player shirt numbers, placing Hebrew/English story captions, or validating that story text does not cover player faces or get cropped in WhatsApp previews.

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。

Review FriendlyBet privacy, RLS security, auth, recovery codes, session storage, data minimization, public snapshots, share surfaces, legal/gambling wording, real-money risk, and compliance-sensitive product changes. Use when a task changes data access, user identity, public sharing, money-like language, security posture, or legal risk.

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。

Scaffold project/workspaces, apply base structure and configs, and verify dev server startup gate

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 the confirmed design direction to all visual surfaces. This skill now bridges the pipeline's placeholder system with the `impeccable` skill's PRODUCT.md and DESIGN.md files. If `{{DESIGN_DIRECTION}}` still appears as a literal placeholder, run `/impeccable teach` to establish design context.

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。

Bootstrap a new Gradle module in any Kotlin Android project. Walks the user through choosing one of three module types — library module (`com.android.library`), feature module (regular Android library that hosts a feature/UI, install-time only), or dynamic feature module (`com.android.dynamic-feature`, on-demand/conditional/instant delivery). Use whenever the user wants to add a module, modularize code, extract a util/data/ui layer, create a new feature, set up Play Feature Delivery, or asks "should this be a library or feature module?". Discovers the project's existing conventions (plugin aliases, namespaces, sibling modules, version catalog) instead of hardcoding any. Pushes back on bad combos (e.g. utility code in a dynamic feature). Stops at scaffolding; app-level wiring is out of scope.

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 SOLID principles to write flexible, maintainable, and testable code. Use when designing classes, interfaces, and module boundaries. Covers Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion with practical TypeScript examples and detection heuristics.

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。

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

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 an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without

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。

Runbook skill for failures: cron error, PA failure, cascade, gateway disconnect, semantic DB stale. Walks: detect → classify → diagnose → notify → log. Replaces ad-hoc failure handling. Triggers: "cron failed", "X is broken", "cascade", "incident", "gateway down", "PA failure".

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。

Turns data/commitments.jsonl (flat list) into a who-owes-what-to-whom graph. Surfaces overdue chains and cross-PA dependencies that the flat list hides. Triggers: "what's overdue", "what did X promise", "commitment graph", "who's blocking whom".

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 Confluence Deep — deep Confluence management covering spaces, pages, content analytics, permissions, and workspace health. Use when performing deep audits of Confluence usage, analyzing content freshness, reviewing space permissions, or assessing documentation coverage across an Atlassian Confluence instance.

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 Chronosphere — chronosphere cloud-native observability platform for metrics, tracing, and alerting at scale. Covers PromQL-based metric queries, trace analysis, monitor management, dashboards, and control plane configuration. Use when querying Chronosphere metrics, investigating service performance, managing alerting rules, or analyzing distributed traces.

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 Bitbucket Pr Reviews — bitbucket pull request review management including default reviewers, merge checks, branch permissions, and build status integration. Use when configuring or automating Bitbucket PR review workflows across repositories and workspaces.

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 Cargo — cargo Rust build and package management. Covers build configuration, workspace management, feature flags, build profiles, cross-compilation, and build script analysis. Use when managing Cargo workspaces, configuring build profiles, debugging compilation issues, or optimizing build performance.

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 Bazel — bazel build system management. Covers BUILD file analysis, target querying, dependency graphs, remote execution, caching configuration, and build performance profiling. Use when managing Bazel workspaces, debugging build failures, querying targets, or optimizing build performance.

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。

Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.

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。

Drive the user's desktop in the background — clicking, typing, scrolling, dragging — without stealing the cursor, keyboard focus, or switching virtual desktops / Spaces. Cross-platform: macOS, Windows, Linux. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.

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 and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.

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 WCAG 2.2 compliant interfaces with mobile accessibility, inclusive design patterns, and assistive technology support. Use when auditing accessibility, implementing ARIA patterns, building for screen readers, or ensuring inclusive user experiences.

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 how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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。

Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.

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 and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

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。