「dap」の検索結果

Create and render Table v9 with the lit adapter. Route reusable createTableHook components, Virtual integration, and framework setup; use table-state for reactive ownership.

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 and render Table v9 with the preact adapter. Route reusable createTableHook components, Query and Virtual integration, and framework setup; use table-state for reactive ownership.

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 and render Table v9 with the alpine adapter. Route reusable createTableHook defaults, and framework setup; use table-state for reactive ownership.

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 and render Table v9 with the ember adapter. Route reusable createTableHook defaults, and framework setup; use table-state for reactive ownership.

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 and render Table v9 with the react adapter. Route reusable createTableHook components, Query and Virtual integration, and framework setup; use table-state for reactive ownership.

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 and render Table v9 with the vue adapter. Route reusable createTableHook components, Query and Virtual integration, and framework setup; use table-state for reactive ownership.

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 and render Table v9 with the svelte adapter. Route reusable createTableHook components, Query and Virtual integration, and framework setup; use table-state for reactive ownership.

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 and render Table v9 with the octane adapter. Route reusable createTableHook components, and framework setup; use table-state for reactive ownership.

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 and render Table v9 with the angular adapter. Route reusable createTableHook components, Query and Virtual integration, and framework setup; use table-state for reactive ownership.

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。

Choose Table v9 state ownership, atoms, initialization, updates, and resets. Load for controlled slices or state coordination; use the adapter state skill for reactive reads.

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 and render Table v9 with the solid adapter. Route reusable createTableHook components, Query and Virtual integration, and framework setup; use table-state for reactive ownership.

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。

Manage context-mode GitHub issues, PRs, releases, and marketing with parallel subagent army. Orchestrates 10-20 dynamic agents per task. Use when triaging issues, reviewing PRs, releasing versions, writing LinkedIn posts, announcing releases, fixing bugs, merging contributions, validating ENV vars, testing adapters, or syncing branches.

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 demand generation campaigns, optimizes paid ad spend across LinkedIn, Google, and Meta, develops SEO strategies, and structures partnership programs. Use when planning demand gen strategy, growth marketing, advertising campaigns, PPC optimization, lead generation, pipeline generation, or marketing budgets. Covers multi-channel acquisition (Google Ads, LinkedIn Ads, Meta Ads), CAC analysis, MQL/SQL workflows, attribution modeling, technical SEO, and co-marketing partnerships. Default calibration profile is a Series A+ B2B SaaS scaling internationally (EU/US/Canada, hybrid PLG/Sales-Led) — adapt benchmarks for other stages and motions rather than skipping the skill.

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。

Framework for rolling out organizational changes without chaos. Covers the ADKAR model adapted for startups, communication templates, resistance patterns, and change fatigue management. Handles process changes, org restructures, strategy pivots, and culture changes. Use when announcing a reorg, switching tools, pivoting strategy, killing a product, changing leadership, or when user mentions change management, change rollout, managing resistance, org change, reorg, or pivot communication.

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。

Discover, find, compare, audit, repair, adapt, and design repeatable AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs. Use when a user asks to analyze a codebase for potential loops, mine coding-thread history for work done more than once, turn repeated engineering work into a loop, find or recommend a published loop, create a recurring agent workflow or automation cadence, turn an outcome into a bounded copy-ready loop, or review an existing loop for weak checks, unsafe authority, unbounded repetition, stale state, or unclear stopping behavior.

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。

Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; agent instructions read selected project planning context when invoked. Automatic recovery reads project planning files only. Explicit session-catchup.py --metadata reads same-project local agent session records and emits aggregate counts only; --replay may emit bounded nonce-framed excerpts. This adapter registers no lifecycle or Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

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。

Persistent file-based planning for multi-step AI-agent work. Keeps task_plan.md, findings.md, and progress.md on disk; Kiro skill instructions and steering state read selected project planning context. Recovery reads project planning files and their timestamps only, not agent transcript stores. This adapter registers no Stop hook, never requests continuation, and never runs commands declared in Markdown. The skill has no network upload path. Use for research or work needing 5+ tool calls.

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。

Validates a project's Google Mobile Ads (GMA) SDK integration for iOS, Android, or Unity projects. Use when conducting a full pre-launch audit of an app that integrates GMA SDK or when validating any individual GMA SDK integration checks, such as when validating ad unit IDs and ad formats, SKAdNetwork IDs, mediation adapter SDK version compatibility, or ad preloading.

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。

How to CONNECT Microsoft Teams (`kortix channels connect --platform teams`, prints a tenant-admin consent URL) and how to answer in Teams as a teammate. Covers the live Adaptive Card stream (`teams step` with --detail/--output/--source, `teams send` to finalize the answer), sending and downloading files (the consent-card upload flow + `teams download`), reading teams/channels/members through connector calls, asking the user, and the tone the bot should use. Load this when the turn is triggered from Teams (the prompt mentions a Teams tenant/conversation, or `$MS_TEAMS_CONVERSATION_ID` is set in the env), or when the user asks how to do anything in Teams.

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 any of 240+ specialized prompt patterns natively across Extraction, Summarization, Analysis, Creation, Improvement, Security, Rating. Common: extract_wisdom, create_threat_model, analyze_claims, improve_writing, review_code, mermaid, youtube_summary. CLI used only for YouTube transcript (-y) and URL fallback (-u). Two workflows: ExecutePattern, UpdatePatterns. USE WHEN fabric, fabric pattern, run fabric, update patterns, threat model, analyze claims, improve writing, review code, mermaid, STRIDE, sigma rules. NOT FOR multi-agent investigation (Research) or content-adaptive extraction (ExtractWisdom).

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。

Multi-agent web research with mandatory URL verification, confidence-tagged output, and four depth modes (quick to deep investigation). USE WHEN research, do research, quick research, extensive research, deep investigation, find information, investigate, extract alpha, analyze content, retrieve content, AI trends, enhance content, extract knowledge, web scraping, YouTube extraction, map landscape, competitive analysis, find it, find this, find this product, identify this, what is this, what's that thing, track down, locate, help me find, I can't find X online, can't find it online, source this — never substitute raw WebSearch/WebFetch for a multi-source find/identify/investigate request. NOT FOR people/company/entity deep background, academic papers (use ArXiv), JSON entity extraction, or content-adaptive wisdom extraction (use ExtractWisdom).

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。

Content-adaptive wisdom extraction that reads content first, detects which wisdom domains are present, and builds custom sections around them, with five depth levels and mandatory contrarian takes; pulls YouTube via fabric and articles via WebFetch. USE WHEN extract wisdom, analyze video, analyze podcast, extract insights, key takeaways, summarize interview, distill content. NOT FOR static Fabric extract_wisdom pattern (use Fabric).

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 registering iii functions, binding triggers, selecting sync/void/enqueue invocation, creating workers, inspecting the live worker registry, installing registry workers, authoring custom triggers, moving channel data, or adapting external HTTP functions across TypeScript, Python, and Rust.

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。

Svelte 5 adapter for the json-render devtools inspector. Use whenever the user wants to add, configure, or troubleshoot @json-render/devtools-svelte in a Svelte json-render app, including spec inspection, state editing, action and stream timelines, catalog browsing, or DOM picking.

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。