「ecto」の検索結果

Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.

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。

Vector database expert for embeddings, similarity search, RAG patterns, and indexing strategies

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。

MCP server integrating Slither + Aderyn + SWC patterns into Claude Code for smart contract auditing. Use when analyzing Solidity files, running DeFi-specific detectors, or generating invariants. 10 MCP tools, 86 SWC detectors, DeFi preset pack, CI/CD workflow.

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。

Two-way sync between a local paper directory and an Overleaf project, so ARIS audit/edit workflows stay on the local copy while collaborators edit in the Overleaf web UI. Use when user says "同步 overleaf", "overleaf sync", "推送到 overleaf", "connect overleaf", "Overleaf 桥接", "pull overleaf", "push overleaf", or wants to bridge their ARIS paper directory with an Overleaf project.

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 installing or configuring the WizTelemetry Data Pipeline (vector) extension for KubeSphere, which provides data collection, transformation, and routing for observability data including logs, auditing, events, and notifications

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 Brilliant Directories tasks via Rube MCP (Composio). Always search tools first for current schemas.

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。

Imports FBX meshes into a CARLA source build as spawnable static.prop.* blueprints — pass a directory whose subdirectories name the semantic tags, or a single FBX with its tag, and the props are imported into content, registered, added to the prop factory and verified by spawning. Use when the user asks to "add a prop to CARLA", "import an FBX as a prop", "add street furniture/benches/signs", or needs a custom mesh spawnable from the Python API.

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 building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

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。

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

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。

Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.

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。

Designs and implements production-grade RAG systems by chunking documents, generating embeddings, configuring vector stores, building hybrid search pipelines, applying reranking, and evaluating retrieval quality. Use when building RAG systems, vector databases, or knowledge-grounded AI applications requiring semantic search, document retrieval, context augmentation, similarity search, or embedding-based indexing.

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 guide on developing new Apache Beam I/O connectors correctly, including core IO transforms, SchemaTransforms, URN proto definitions, Managed API integration, cross-language expansion service, and testing.

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 resolve file access issues by first verifying the directory structure.

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 resolve file access issues by verifying directory structure, then reading multiple files in parallel batches for efficient auditing, with built-in guidance for validating TypeScript import patterns.

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 resolve file access issues by verifying directory structure, reading multiple files in parallel batches, running combined shell property checks (head/wc-l) in the same iteration as directory listing, and validating TypeScript import patterns — all with built-in guidance to minimize total iteration count.

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。

Safely append CSS styles from one file to another, with per-selector grep verification after every append to catch silent truncation that tail or wc-l alone would miss

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。

Resolve file access failures by changing to the correct working directory before command execution

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。

Active Directory attack methodology for internal network red team engagements. Covers reconnaissance (BloodHound, PowerView, ADExplorer), credential abuse (Kerberoasting, ASREProasting, NTLM relay, LLMNR/NBT-NS poisoning), privilege escalation (ACL abuse, GPO abuse, unconstrained/constrained delegation), lateral movement (Pass-the-Hash, Pass-the-Ticket, Overpass-the-Hash, WMI/WinRM/PsExec), persistence (Golden/Silver/Diamond Tickets, DCSync, DCShadow, AdminSDHolder, Skeleton Key), forest trust attacks, ADCS abuse (ESC1-ESC15), and modern MDI/Defender for Identity evasion. Use when assessing on-prem AD, hybrid AD/Entra ID environments, or ADCS deployments.

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 you are a seat on the oversight pod (a standing monitor-mode rig that keeps OTHER rigs healthy), configuring or running the drift detectors, or choosing whether to intervene vs escalate. Covers the pull-not-poll posture, the v0 detectors (premature-park, process-drift, off-task, token-burn), the intervention ladder (orchestrator-ping -> refocus -> human escalation), and the cheap+deep model economics. NOT for orchestrating your OWN rig (orchestration-team) and NOT the intervention primitive mechanics (watchdog).

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 the PM-to-Director transition across preparing, interviewing, landing, and recalibrating. Use when leadership scope is changing and you need practical coaching.

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。

Draws flat vector illustrations as SVG cards (480x360): slim, long-limbed people acting out one design or coding moment with one hero prop. Identified by no outlines on big shapes, thin white fold strokes as the only shading, navy only for hair, sneakers and a few hairlines, small heads (about 1:7) with big hands and white sneakers, and sparse floating confetti with no ground line, in lavender, indigo, sunshine yellow, coral and mint. Covers the measured palette, stroke numbers, character construction (tapered limbs, separated fingers, thumb side), poses, props, composition and a tested parts kit. Use when asked for flat illustrations, flat vector people, outline-free or no-outline characters, modern or corporate flat art, empty states, onboarding screens, 404 or error pages, coming-soon pages, blog headers, feature spots or marketing cards.

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。

Build, extend, or audit production-linked browser map editors for Three.js and isometric games. Use when Codex needs to create a private director view, derive a versioned editor document from authored placements, add outliner, layer, selection, drag, snap, inspector, or camera controls, expose enemy aggro, leash, or patrol overlays, implement local draft import, export, undo, and reset, secure an editor route with IP and password sessions, or validate and release a map editor without mutating live gameplay data.

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。

Deep-crawl any website from start URLs, return per-page LLM-ready text/markdown/HTML plus metadata (title, description, author, language, canonical URL, OG) and in-scope outbound links. Use when user mentions deep crawl website, recursive crawl, crawl a whole site, scrape entire website, scrape docs site, scrape documentation, scrape knowledge base, scrape blog, build RAG corpus, build vector database from website, knowledge base for chatbot, GPT knowledge files, llms.txt, sitemap crawl, BFS crawl, scrape with depth or page limit, include exclude URL globs, remove boilerplate, strip navigation header footer, website to markdown, website to text, multi-page extraction, bulk page scraping, clean markdown from URL, docs site to markdown corpus, site to clean corpus. Also applies to building RAG pipelines, indexing a customer site, syncing docs into a vector store, generating training corpora from any docs hub, or expanding a single start URL into a clean corpus of every reachable in-scope page.

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 before filesync calls for project directories, global-shared inputs, non-task shared files, listing shared paths, or troubleshooting missing files. Do not use for task-level sync; taskflow delegate_task and check_task handle task directory sync internally.

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。