「drive」の検索結果

AI-driven image generation and editing tool for creating high-quality product images. Triggered when users ask to generate images, create product pictures, edit photos, text-to-image, image-to-image, replace backgrounds, transform styles, composite products into scenes, swap models, or create any type of AI-generated visual content. Even if the user does not explicitly say "AI image," this skill should be triggered whenever the request involves creating, modifying, or transforming images.

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Amazon product discovery and potential bestseller mining via Nexscope data. Triggered when users mention product mining, potential bestsellers, high-conversion product selection, click growth analysis, market growth opportunities, keyword-based product selection, FBA profit screening, niche market product discovery, seller origin filtering, Nexscope data, FBA profitability screening, keyword-based product selection. Even if users do not explicitly mention "Nexscope", this skill should be triggered whenever the task involves keyword-driven Amazon product selection based on conversion rates, click volume, and profitability metrics.

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 Nexscope data to reverse-lookup traffic keywords for any Amazon ASIN, including organic ranking, ad ranking, search volume, traffic share, organic/paid scores, ABA TOP3 click concentration, click conversion rate, year-over-year search volume changes, and weekly/monthly time windows. Trigger when the user mentions ASIN keyword analysis, ASIN reverse lookup, traffic keyword research, organic ranking query, ad ranking query, keyword position tracking, Nexscope keyword data, competitor keyword peeking, viewing which keywords drive traffic to a product, analyzing keyword performance for a specific ASIN, time windows by week/month/recent N days, ASIN reverse keyword lookup, traffic keywords, organic ranking, ad ranking, search volume, Nexscope keywords, competitor keyword reverse lookup, click concentration, click-to-purchase conversion, week-over-week search volume. Even if the user does not explicitly mention "Nexscope", if their need involves finding keywords associated with a specific Amazon product (ASIN), this skill should also be triggered.

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。

Cloud-based AI swarm deployment and event-driven workflow automation with Flow Nexus platform

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。

Ask the AgentDB bandit which RL algorithm / skill / pattern fits the current task best. Use at task start when there are multiple plausible approaches and you want the data-driven pick.

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。

Diagnose and fix mini-program failures through reproducible, evidence-driven root-cause analysis across source, state, asynchronous timing, caches, mock data, build versions, platform behavior, devices, cloud calls, and external services. Use when users report white screens, crashes, stale or jumping values, missing data, intermittent behavior, permission failures, performance stalls, device-only defects, API or cloud-function errors, source-versus-build mismatches, or regressions whose cause is not yet established. Freezes the observation environment, ranks competing hypotheses, runs discriminating checks, creates a failing regression test before repair, covers analogous states, and never hides a known failure with longer waits, swallowed errors, forced success, or cosmetic patches.

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 bounded features and fixes in existing or new mini-program codebases while preserving repository rules, user-owned changes, approved behavior, assets, framework conventions, and evidence boundaries. Use when users ask to write or modify mini-program code, implement a confirmed specification or architecture, add a scoped feature, remove behavior semantically, update internal documentation required by a change, or carry out a well-defined small fix that does not require root-cause investigation. Establishes a baseline and change boundary, applies test-driven small steps, distinguishes source from generators and build artifacts, verifies the affected contract, and never reports source completion as device validation, formal acceptance, upload, or release.

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 a real browser through Aside: open a page, read it, click through a flow, take screenshots, check console errors. (gstack)

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。

Evaluate a teacher-drafted central historical question for its capacity to drive genuine historical inquiry. Use when assessing whether a question will generate real evidence-weighing or produce shallow responses.

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。

Run metric-driven optimization loops. Use when improving measurable outcomes such as search relevance, clustering quality, build performance, prompt quality, or scored behavior through experiments.

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。

Establishes a project's quality bar as a written contract and stops agents quietly lowering it. Interviews the user on which dimensions matter, supplies sane default thresholds when they have no number in mind, records everything in CONSTRAINTS.md, and watches the diff for a weakened bar — new @ts-ignore or eslint-disable suppressions, skipped or deleted tests, assertions stripped out, unimplemented stubs, thresholds edited down. Use when no quality bar is written down, when the user says "set up constraints" or "define our standards", when the user wants dimensions they care about — accessibility, web performance, coverage — set up as enforced constraints, when an agent keeps silencing checks or skipping tests to get to green, when you need a coverage or performance threshold and don't know what number to pick, or when an agent writes more code than anyone will read.

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。

Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production auth, security-sensitive logic, a high-stakes migration, irreversible operations), or any time a confident output would be cheaper to verify now than to debug later.

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。

Creates specs before coding. Use when starting a new project, feature, or significant change and no specification exists yet. Use when drafting a PRD or requirements document with objectives and scope, or when requirements are unclear, ambiguous, or only exist as a vague idea. Use when a single requirement spans several independently testable capabilities and needs decomposing into a capability map of modules before specifying.

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。

Grounds every implementation decision in official documentation. Use when you want to verify an approach against the official docs before implementing it, or when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.

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。

Lightweight, script-driven variant of data2motion for turning data into a smooth, on-brand animated chart (one self-contained HTML) — built for a TEXT-ONLY model that cannot see its own output. You never write HTML/CSS/SVG/animation; you extract the data, pick a chart, fill a small JSON spec, and run one build script that owns 100% of the look and the motion, so the result cannot drift from the house template style or lose its smoothness. Use to turn a number/stat/table/CSV/paragraph into a quick animated chart, data reveal, or KPI when you want guaranteed template fidelity with minimal reasoning and no image feedback.

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 test-driven development for behavior-changing feature or fix work, and whenever the user mentions TDD, test-first, red-green-refactor, tracer bullets, integration tests, or public-interface behavior tests. Skip for docs-only, path-only rename, formatting-only, or purely mechanical chores unless explicitly requested.

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 an implementation test-first, red before green, against a claimed task's frozen criterion. Use when implementing a task in a repository with a .ank/ directory.

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。

Transform ideas into detailed visual specifications through scenario-driven design

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[project] OPC-Starter 测试驱动开发规范。管理和执行 Playwright E2E 测试、演示模式测试、单元测试的完整工作流。适用于新增功能时补充测试用例、运行认证/注册/演示测试、维护测试规划文档。当用户提到测试、E2E、Playwright、演示、用例、质量、补充测试时使用。

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。

Pressure-test an idea through persona-driven interrogation until it hardens, proves out, or dies cheaply. Use when the user says 'forge an idea', 'pressure-test this idea', 'stress-test my thinking', or 'harden this idea'.

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。

Root cause analysis for bugs and unexpected behavior. Traces errors through code, uses structured reasoning, and hands off to fix when cause is found. Core of the debug↔fix mesh. When the diagnosed cause is a memory leak in a long-running process, escalates to perf for cost-impact framing (leaks drive OOM-restart → cold-start → autoscaler spend, often 20-40% bill inflation).

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。

Investigate and resolve Course Management Platform CloudWatch alert issues end to end. Use when Codex is asked to inspect a CMP GitHub alert issue, pull dev or production AWS logs, diagnose 5xx responses or failed background work, fix an incident with test-driven development, validate a fix in dev, or deploy and verify an authorized production fix.

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。

Run this Django app locally against a throwaway database and drive its pages over HTTP to observe a change working. Use when verifying student/cadmin page behavior end-to-end.

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。