1,224 件 ・ 関連度順
概要と使いどころ
Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.
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。
Convert Metabase questions, models, and metrics into ktx Semantic Layer source definitions. Covers result-metadata to KSL column type mapping, FK/PK detection, near-duplicate deduplication, pre-aggregation decomposition, join-graph connectivity, and how to react to priorProvenance from earlier ingest syncs. Load when the WorkUnit contains `cards/<id>.json` files under a Metabase bundle.
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。
Synthesize durable ktx wiki pages and semantic-layer sources from staged Notion pages, databases, data-source rows, and clustered Notion evidence. Load when a WorkUnit contains Notion raw files or Notion evidence chunks.
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。
Capture semantic-layer and knowledge updates from a live database schema snapshot.
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。
ktx's semantic layer - a structured catalog of sources (tables/views), measures, joins, and segments expressed as YAML. Covers the schema and how to query it via `sl_query`. Use when the task involves querying pre-defined metrics (ARR, churn, retention, LTV, MAU) or reading SL source YAML to understand the catalog. Capture is handled by the `sl_capture` skill (memory-agent only).
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。
Extract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.
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。
ktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational conventions, and cross-references between wiki pages and semantic-layer sources. Loaded by the post-turn memory-agent only. The research agent reads wiki via `wiki_read`/`wiki_search` but does not write it.
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。
Builds an Azure portal deep link to a specific blade/menu item for supported Microsoft.ContainerService, Microsoft.Kubernetes, and Microsoft.Compute resources using the aks-search-direct-mid semantic search API. WHEN: 'find where in the Azure portal to do something', 'search the portal for an operation', 'construct a portal link from a search query and a resource'.
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 for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
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。
Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, configure AI backend, token metrics, AI cost control, convert API to MCP, import OpenAPI to gateway.
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。
Gate a prompt universe for schema and provenance completeness, target or campaign contamination, evidence entailment, naturalness, one-concept clarity, architecture consistency, aided status, answer leakage, and semantic duplicates. Use after realistic prompt generation and before human panel selection.
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 natural, controlled prompt variants from a target-blind design brief and prompt architecture while preserving approved jobs, acts, journeys, constraints, roles, locales, proximity bands, and evidence language. Use after architecture design and before contamination or semantic QA.
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 enrich durable data catalogs using the native DS_CATALOG_V1 Markdown contract, declared entity relationships, privacy citation fields, and stable relationship IDs. Use when inventorying engagement data, recording semantic relationships, or preparing a catalog for ERD rendering.
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。
Reviewer rejection workflow and strict lockout semantics
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。
Mem0 memory CLI: add memories from messages, semantic search, read or delete memories, poll async events. Trigger phrases: mem0, memory layer, remember this.
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。
Find and remove AI slop — the generic, machine-default visual and copy tics of vibe-coded products — from a web project. Use when the user asks to "kill AI slop", "de-slop", "remove the AI look", "make this not look AI-generated", or clean up a landing page / UI / docs that feels templated. Detects and fixes the catalogue of tells: indigo→violet gradients, gradient-clip headlines, the default semantic palette, one-hue status boxes, atmospheric gradients, serif-italic emphasis, highlighted keywords, AI copywriting voice ("not just X — it's Y"), emoji everywhere, glowing status dots, wobbling spinners, colored-left-border callouts, pastel icon tiles, glassmorphism, over-rounding, oversized shadows, borders that die at corners, badge & pill spam, AI-drawn SVG icons, kickers over every heading, flat type hierarchies, invented stat rows, 01/02/03 section markers, cards nested in cards, the default Inter/Space Grotesk look, and more. Works on HTML/CSS, React/Vue/Svelte/Astro, Tailwind, PHP, and Markdown copy.
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 to establish shared project language, or project work exposes a conflicting, renamed, or deprecated domain term that needs active semantic modeling. Routine small tasks stay on the fast path.
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 on first install of this plugin, when MO2 or xEdit is not yet detected, when starting a new modpack project, or when the user says 'set up', 'install', 'bootstrap', 'configure', or 'initialize' the BGS modding environment. Orchestrates: MO2 detection and control-plane install, MO2 visible launch, optional xEdit download from BB-84C/TES5Edit, optional bgs-translator/xtl PyPI install, dev-log and release-changelog initialization, and end-to-end semantic smoke 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。
29 production-ready scripts for iOS app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and simulator lifecycle management. Optimized for AI agents with minimal token output.
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。
Plan, design, author, preview, publish, and manage Power BI reports across requirements, page design, local PBIR/PBIP edits, validation, screenshots, Fabric upload/download, and rebinding. Use for report lifecycle work; use semantic-model-authoring for model or DAX changes and fabriciq for data questions. Triggers: plan Power BI report, design report page, edit PBIR, preview PBIP, publish report, rebind report
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。
Maintain a brand kit — the canonical brand context an ad or content pipeline reads (positioning, audience, voice, standing instructions, brand-type, value-props, colors), plus manage the product list and attach product photos. Use when someone says "update my brand kit", "set my brand voice/audience", "add a product to my brand", or hands you a folder of product shots to attach. Platform-agnostic: it teaches the field model, partial-update/clear semantics, override behavior, caps and validation, and the hero-image and product-matching rules — independent of any specific backend.
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。
管理本地 Markdown/Obsidian 知识库：素材入库、健康检查、增量索引、关键词与 semantic-lite 检索、逐字原文资料包、归档和 MCP 连接。用于搜索知识库、整理资料及为 Agent 准备可定位的引用。
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。
Optimizes Simulink models for Embedded Coder generated code. Use when asked to optimize or improve generated code, or reduce code metrics for a Simulink model. Targets: execution time, memory footprint (RAM, ROM, stack, data copies), code size, MISRA compliance, or any semantically similar generated-code metric. Works iteratively — measures baseline, suggests changes, applies, and re-measures to confirm improvement. Triggers can be prompts similar to: optimize generated code runtime, reduce runtime, shrink code size, improve code efficiency, reduce memory usage, speed up generated code, follow MISRA compliance and so on. CAUTION: Do NOT attempt to optimize Simulink models for generated code efficiency without following this skill — the iterative measurement, gating, and rollback workflow is essential for safe optimization.
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。
Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.
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。