「based」の検索結果

Cut through the model soup to buy the right appliance — the features that actually matter for you, the ones that are marketing, and when to buy. Use when asked which [fridge/washer/dishwasher] should I buy, help me choose an appliance, what features do I need, or is this appliance worth it. Produces a needs-based feature shortlist (must-have vs nice-to-have vs marketing fluff), fit and capacity checks, reliability and running-cost considerations, warranty/extended-warranty guidance, and timing tips — flagging to verify current models, prices, and specs before buying.

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。

Octane bindings for TanStack DB. useLiveQuery hook with dependency arrays (8 overloads: query function, config object, pre-created collection, disabled state via returning undefined/null). useLiveSuspenseQuery for Octane Suspense with Error Boundaries (data always defined). useLiveInfiniteQuery for cursor-based pagination (pageSize, fetchNextPage, hasNextPage, isFetchingNextPage). usePacedMutations for debounced Octane state updates. Return shape: data, state, collection, status, isLoading, isReady, isError. Import from @octanejs/tanstack-db (re-exports all of @tanstack/db).

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。

Help users navigate product failure by diagnosing root causes, evaluating conviction, and executing successful pivots or turnarounds based on proven industry frameworks.

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。

Help users transition from linear funnel thinking to a loop-based growth model that identifies and optimizes the core engines of their 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。

Help users build robust infrastructure for measuring, monitoring, and iterating on AI product performance using human, code-based, and LLM-as-a-judge methodologies.

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。

Help users define precise liquidity metrics, set competitive take rates based on value delivery, and manage the delicate balance between supply-side health and platform profitability.

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。

Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.

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 educational practice questions from lecture notes to test student understanding. Use when users request practice questions, exam preparation materials, study guides, or assessment items based on lecture content.

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。

Behavioral guardrails for Codex coding work based on common user complaints. Use when Codex is asked to implement, modify, debug, review, test, or operate on a codebase and should avoid unsafe scope expansion, stale edits, fake completion, brittle edits, shallow debugging, superficial patch-on fixes, one-off special-case code, short-term design choices that hurt maintainability, blindly following incorrect user assumptions, poor dependency choices, generic product or UI output, over-mocked tests, noisy approvals, verbose status reports, or leaking internal reasoning into project artifacts or user-facing UI.

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 fast, high-impact 15-30 second brand and product marketing videos from an official company website, verified brand sources, or user-uploaded product imagery. Use for launch films, website motion videos, AI/SaaS demos, product promos, and Vibe Motion/Remotion productions that require exact logos, authentic imagery, English-first copy, product-feature storytelling, strong visual impact, smooth transitions, and deterministic frame-based animation.

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。

Transforms knowledge sources into an Obsidian StudyVault. Two modes: (1) Document Mode — PDF/text/web sources → study notes with practice questions. (2) Codebase Mode — source code project → onboarding vault for new developers. Mode is auto-detected based on project markers in CWD.

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。

Intent-based search router with trust scoring. Routes queries to optimal channels (Context7 docs, native web search, gh/glab code search, Serena local) and attaches domain trust labels. Use for search, find, lookup, reference, docs, code search, and web research.

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。

Intent-based observability + traceability router across layers, boundaries, and signals. Routes to vendor-specific skills via category taxonomy; owns transport tuning, meta-observability, incident forensics. Use for observability, traceability, telemetry, APM, RUM, metrics, logs, traces, profiles, SLO, incident forensics, tracing architecture work.

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。

C++ coding standards based on the C++ Core Guidelines (isocpp.github.io). Use when writing, reviewing, or refactoring C++ code to enforce modern, safe, and idiomatic practices.

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 only after explicit Codex `$superloopy:superloopy-frontend` or Claude Code `/superloopy:superloopy-frontend` invocation for supported screen-based application UI across browser-hosted Web, interactive deployed content-led Web, desktop, mobile/tablet, embedded/hybrid, Qt, custom-rendered, or mixed targets, such a task started with a leading `loopy` or `루피`, or an active Superloopy loop explicitly routing it here. Do not activate from UI, frontend, desktop, mobile, or framework vocabulary alone, or for TV, wearable, XR, automotive, game UI, TUI, static media/document artifacts, or non-UI work.

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。

SERP-based semantic topic clustering for content architecture planning. Groups keywords by actual Google SERP overlap (not text similarity), designs hub-and-spoke content clusters with internal link matrices, and generates interactive visualizations. Optionally executes content creation if claude-blog is installed. Use when user says "topic cluster", "content cluster", "semantic clustering", "pillar page", "hub and spoke", "content architecture", "keyword grouping", or "cluster plan".

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。

Autonomous content pipeline - scout announcements in your field, triage by trend momentum and personal angle, produce posts/blogs/videos in your voice with ledger-based dedup, hard volume caps, and screenshot-verified publishing

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。

Spin up a new loop (domain) in a file-based knowledge base — bootstrap the substrate if it's missing, gather the loop's charter, scaffold domains/<loop>/README.md, then do ONE real test run and record it in the loop's Timeline and LOG.md. Use when the user says "set up a new loop", "create a domain", "start a new beat/workstream", or names a recurring job they want the agent to own.

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。

INVOKE THIS SKILL when building, testing, or deploying Managed Deep Agents in LangSmith. Walks a user through their first agent end to end — interviewing them about what they want to build, mapping it onto what MDA can actually do, then scaffolding and deploying it. Covers the file-based project layout; define_deep_agent / defineDeepAgent; instructions, skills, memory, identity, tools, MCP servers, connections, middleware, sandboxes, schedules, channels, and evals; the mda CLI; GitHub deployments; and Context Hub.

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。

Minimalist UX/Interaction Audit Expert that deconstructs complex interactions through cognitive load and operational efficiency lenses. Use this skill when you need to perform a UX walkthrough audit on a Figma prototype or web interface, evaluating usability based on principles like fewer clicks, less UI elements, no hidden logic, and self-explanatory design.

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 starting or navigating any American Anthropologist (AA) manuscript as the entry point. Routes to the right AA sub-skill based on lifecycle stage, subfield (sociocultural, archaeology, biological, linguistic), and section type (Research Article, Vital Topics Forum, World/Public/Multimodal Anthropologies, Review Essay). It dispatches; it does not draft content.

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Use when framing the system and describing the model for an Agricultural Systems (AgSy) manuscript — defining system boundaries, components, hierarchical levels, and feedbacks, then choosing, describing, and calibrating the model (process/simulation, whole-farm, bio-economic, agent-based, or integrated assessment). This is the distinctive core of an AgSy paper. Structures the model and system; it does not run code for you.

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 as the entry point for any Agricultural Systems (AgSy) manuscript. Routes to the right AgSy sub-skill based on where you are in the lifecycle and which article type (research paper, short communication, perspective, comment, review) fits. AgSy is a systems-science journal, so the router's first job is to confirm there is a real systems question — interactions, hierarchical levels, trade-offs — not a single-factor field trial. It dispatches; it does not draft content.

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 designing or auditing AAMAS experiments - self-play and population-based training, opponent selection, equilibrium and regret metrics, game-theoretic simulations, ablations, seeds, hyperparameters, compute, and claim-to-evidence fit - with emphasis on experiments that probe the interaction rather than chase a single-agent leaderboard.

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。