「to」の検索結果

Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

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。

Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.

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。

End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template. Handles moving existing apps to Azure without rewriting or with minimal changes. WHEN: bring your app to Azure, plan my app, cost to run, is my code ready to deploy, deploy my app to the cloud, deploy all my services, what Azure services do I need, plan my Azure deployment, deploy my new app to Azure, one-click deploy, I have an app and want it on Azure, migrate my app to Azure, help me get started, build an app, no code yet, starter project. DO NOT USE FOR: use azd for deployment(use azure-deploy), optimizing existing costs (use cost-optimization), code readiness checks only (use azure-app-onboard-prereq).

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。

Clones and audits competitor repositories into evidence-based intelligence with file:line citations. Use to track competitors, add a competitor repo, review competitor code, compare capabilities, build a competitor landscape, or check for code updates: 竞品分析 / 竞品 / competitor scan / analyze competitor / compare with X / latest competitor code. Not for market research without code (use deep-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。

Extract a brand's visual and verbal identity into an applicable guideline kit — tokens, voice rules, and do/don't pairs — then apply it consistently to any artifact. Use when asked to apply brand guidelines to a document/deck/page, to extract a brand kit from existing materials or a website, to keep AI-produced artifacts on-brand, or to write lightweight brand guidelines for a startup. Produces a compact brand kit (visual tokens + voice rules + application examples) and/or an artifact restyled to it. For a creator's personal voice use creator-brand-kit; for building new UI systems use frontend-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。

Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.

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。

Automate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol. Use when the user needs to interact with an Electron app, automate a desktop app, connect to a running app, control a native app, or test an Electron application. Triggers include "automate Slack app", "control VS Code", "interact with Discord app", "test this Electron app", "connect to desktop app", or any task requiring automation of a native Electron application.

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。

Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory. Use when a user or agent mentions large files, big data, out-of-memory errors, OOM, scaling up, tall arrays, datastores, or needs to process multiple tabular files. Covers the decision between datastore + tall, datastore + transform, and parallel execution. Also use when a user or agent has working in-memory code (readtable, parquetread) that runs out of memory and needs a migration path. Also covers building custom datastore classes for proprietary or non-standard formats — use when the task requires subclassing matlab.io.Datastore, implementing a custom reader, building an extensible datastore, or integrating a new file format with tall arrays or parallel computing. Do NOT use for MAT files (use matfile instead).

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。

Post a comment (or a formal PR review) to a GitHub issue or pull request in mariadb-operator/mariadb-operator. Use whenever the user wants to publish something to GitHub for this repo — "comment on issue #123", "post this as a PR comment", "leave a note on #456", "reply on the PR" — and especially when they want the output of the mariadb-operator-pr-review skill delivered to GitHub instead of just shown in chat, e.g. "review PR #1234 and post it as a comment" or "post the review results to the PR". Always confirms the exact comment text with the user before publishing — never posts autonomously.

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。

Search all Japanese NLP resources (libraries, models, datasets, tutorials, dictionaries, Hugging Face). Accepts keywords or natural language questions in any language. Use whenever the user asks which Japanese NLP resource to use, or wants to find one: tokenizers / morphological analyzers, BERT or LLM models, embeddings, NER, text classification, datasets / corpora, dictionaries, tutorials, or Hugging Face models. Trigger phrases include '日本語の形態素解析ライブラリ', 'おすすめの日本語tokenizer', '日本語BERTモデル', '日本語の感情分析データセット', '日本語LLM 一覧', 'which Japanese embedding model', 'Japanese NER library'.

Compare several Japanese NLP libraries, models, or datasets for a keyword (a specific tool name, or a function/task like '形態素解析') across a handful of criteria chosen for that comparison, rendered as a ○/△/✕ table. Use when the user wants a side-by-side comparison of multiple Japanese NLP tools/libraries/datasets, not just the single best one. Trigger phrases include 'X と Y と Z を比較して', '形態素解析ライブラリを比較', 'MeCab と Sudachi どっちがいい', 'どのツールを使うべき', 'compare japanese tokenizers', 'mecab vs sudachi vs janome', 'which embedding model should I use', '日本語NERライブラリの比較表', 'pros and cons of japanese OCR tools'. For a single ranked list use search; for alternatives to one specific tool (or contribution candidates) without a multi-axis table, use discover.

Generate brand storytelling and narrative video prompts for Seedance 2.0 on Higgsfield. Use whenever the user wants to create a brand story video, company origin story, mission video, about us video, brand film, corporate narrative, founder story, brand anthem, company culture video, or emotional brand content. Triggers on: brand story, brand video, company story, origin story, about us, mission video, brand film, brand anthem, corporate video, company culture, founder story, brand narrative, or any request for brand/company storytelling video. Use even for "tell our company story" or "make a video about our mission.

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。

Expert patterns for migrating 2D games to 3D including node type conversions, camera systems (third-person, first-person, orbit), physics layer migration, sprite-to-model art pipeline, and control scheme adaptations. Use when porting 2D projects to 3D or adding 3D elements. Trigger keywords: CharacterBody2D to CharacterBody3D, Area2D to Area3D, Camera2D to Camera3D, Vector2 to Vector3, collision_layer migration, sprite to MeshInstance3D, 2D to 3D conversion.

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。

Expert patterns for migrating 2D games to 3D including node type conversions, camera systems (third-person, first-person, orbit), physics layer migration, sprite-to-model art pipeline, and control scheme adaptations. Use when porting 2D projects to 3D or adding 3D elements. Trigger keywords: CharacterBody2D to CharacterBody3D, Area2D to Area3D, Camera2D to Camera3D, Vector2 to Vector3, collision_layer migration, sprite to MeshInstance3D, 2D to 3D conversion.

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。

Conduct full competitor strategy breakdowns across SEO, ads, social, email, pricing, and positioning. Use when the user asks to analyze competitors, benchmark against rivals, understand competitive landscape, find competitor weaknesses, or build a competitive matrix. Trigger phrases include "competitor analysis", "competitive analysis", "who are my competitors", "competitor research", "competitive landscape", "benchmark competitors", "competitor ads", "competitor SEO", "competitor pricing", "SWOT analysis", "competitive matrix".

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 and inspects image objects stored in Volcengine TOS. Use this skill only when the task explicitly involves a TOS bucket/object key, TOS image processing, TOS-to-TOS save-as output, or Volcengine TOS image process syntax such as image/info, image/resize, image/format, image/watermark, image/draw, image/blindwatermark, or image/understanding. Do not use this skill for ordinary uploaded screenshots, local images, UI screenshot analysis, generic OCR, face detection, or visual question answering unless the user clearly says the image is a TOS object or asks to process/save it through TOS.

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。

TikTok热门搜索，一次输入关键词同时获取热门视频、相关用户与相关话题三类数据。基于红狐数据，视频支持排序与发布时间、地区筛选，用户支持粉丝数与认证筛选，话题附浏览量、使用次数与直达链接，输出榜单表格、爆款视频拆解、达人推荐与话题趋势解读，支持翻页与 HTML 报告导出。适用于 TikTok 出海选题调研、竞品扫描、达人发现时使用。触发词：TikTok热门搜索、TikTok视频搜索、TikTok用户搜索、TikTok话题搜索、TikTok关键词搜索、TikTok搜索、TikTok搜视频、TikTok搜用户、TikTok找达人。

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。

TikTok主页视频批量提取 — 输入TikTok主页链接/handle，自动拉取账号主页全部作品并解析无水印下载链接，支持批量一键下载到本地。适用场景：竞品账号内容拆解与逐帧学习、收藏喜欢的创作者全部作品避免被删、批量搬运素材二次剪辑创作、保存自己账号作品做本地备份防丢失、课程/教程类视频离线囤货反复看、甲方要求提供无水印源文件、达人合作前快速拉取对方作品做背调分析。触发词：TikTok主页下载、TikTok视频下载、TikTok批量下载、TikTok无水印下载、下载TikTok作品、TikTok账号视频提取、TikTok主页视频批量提取、TikTok去水印、TikTok视频备份、竞品视频下载。

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。

TikTok视频下载 — 粘贴 TikTok 视频链接，一键解析返回无水印视频下载链接，支持单个或批量链接解析，自动校验 TikTok 链接有效性。当用户需要下载 TikTok 视频、保存 TikTok 视频、获取 TikTok 视频直链、批量下载 TikTok 视频时使用。触发词：TikTok视频下载、TK视频下载、TikTok视频解析、下载TikTok视频、抖音国际版视频下载、批量下载TikTok视频。

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。

smina molecular docking CLI. AutoDock Vina fork with customizable scoring functions, native SDF/MOL2/PDB ligand input, autoboxing, local energy minimization, and per-atom score breakdowns. Pipeline: receptor PDBQT prep -> ligand prep (RDKit/OpenBabel) -> dock via autobox or explicit grid -> rescore/minimize with custom scoring -> rank poses by affinity. Choose smina over Vina when you need custom scoring terms (--custom_scoring), local optimization of an existing pose (--local_only), per-atom contributions (--atom_term_data), or SDF/MOL2 ligands without manual PDBQT conversion. For unknown binding sites use diffdock; for the Python-bindings/Vinardo workflow use autodock-vina-docking.

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。

Runs an evidence-based investigation of a bug, failure, or unexpected behavior with investigate and publishes the resulting investigation report to a user-specified Confluence location. Use when the user wants something debugged, diagnosed, or root-caused AND the findings posted to a Confluence space or page. Requires a configured Atlassian MCP server. Does not investigate to a local file only — use investigate. Does not publish an arbitrary existing markdown file — use markdown-to-confluence. Does not document an already-understood feature to Confluence — use project-documentation-to-confluence. Does not plan or specify a new feature to Confluence — use plan-a-feature-to-confluence. Does not publish to Jira — use work-items-to-jira.

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。

Provide authenticated cookies for TikTok (www.tiktok.com). Use this skill whenever the user needs TikTok cookies for scraping tools, downloaders, or needs to authenticate with TikTok services. Trigger when the user mentions TikTok, tiktok cookies, or wants to use tools that require TikTok login cookies.

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。

Runtime per-test healing with evidence: multi-attribute selector healing, environment-aware diagnosis, flake classification, quarantine management, and confidence-scored auto-repair. Goes beyond simple locator fallbacks to cover action-level healing, data healing, and observable repair workflows. Use when: "flaky test," "test stability," "self-healing locator," "broken locator recovery," "unreliable test," "quarantine flaky test." Not for: bulk regenerating selectors after a planned UI refactor — use selector-drift-recovery (this skill heals ONE test at runtime; that one rewrites many tests offline). Not for: classifying/clustering CI failures into bug reports — use ai-bug-triage. Related: playwright-automation, selector-drift-recovery, ci-cd-integration, qa-metrics, ai-bug-triage.

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。

Detox is a gray-box end-to-end testing framework for React Native apps: it runs Jest tests against the app on an iOS simulator or Android emulator and waits for the app to go idle before each step. Use when the user wants to set up Detox, write or fix React Native E2E tests, debug a test that hangs or cannot find an element, or run Detox in CI. Also use when the user mentions "detox," "React Native E2E," "gray-box testing," "detox build," or "Wix Detox." For black-box testing of any mobile app, see appium; for simple YAML UI flows, see maestro.

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。