「embed」の検索結果

Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).

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。

Phase 2 of building a Claude Managed Agent — turn a validated build sheet into exact API payloads and a resumable BYOK curl launch script, then launch (environment → agent → session → kickoff) using the founder's OWN Anthropic key. Use when the user says "launch it", "deploy the agent", "create the agent now", or when the orchestrator routes phase=stage-launch. payload_generator.py emits the four ordered payloads; launch_script_writer.py writes launch.sh that reads $ANTHROPIC_API_KEY at runtime and never embeds it; payload_validator.py runs a pre-launch check including an API-key-leak scan. No tool in this skill makes network calls — the user runs launch.sh themselves. Distinct from interview (planning) and grade-iterate (the outcome loop).

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。

The durable documentation set that makes an AI-built (vibe-coded) app reviewable before shipping. A small core every app needs — architecture, user/permission flows, permissions, variables/secrets, and a test-coverage map — plus conditional docs added only when they apply: emails, scheduled work, SEO, and embedded agents/automation. Defines what each doc must capture and how a reviewer or auditor uses it. Use when documenting a codebase for handoff, mapping user journeys and trust-boundary crossings, planning test coverage, or preparing for a security or performance audit.

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。

Break one epic into implementable stories embedding TR-ID, ADR guidance, acceptance criteria. Reads the control manifest. After /create-epics.

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。

Lightweight spec for small changes — tuning adjustments, minor mechanics. Embeds directly into stories; skips full GDD.

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。

Attach a screenshot/image/video to a PR or issue, or embed an image in a GitHub comment.

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 creating or editing documentation pages in docs/content/guides/ - covers YAML frontmatter, page structure, framework-specific example embedding, writing style, and sidebar registration

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。

Implement, configure, and secure Sign In With Google (SiwG) using Google Identity Services (GIS / `https://accounts.google.com/gsi/client`) across web architectures. Use when creating Google sign-in buttons, implementing Google One Tap with FedCM, integrating GIS in React/Next.js/Angular/HTML, verifying ID tokens on backend runtimes (Python/Node.js/Go/Java), enforcing Google Workspace domain restrictions (`hd`), securing client-side ID tokens via WebCrypto nonces or encrypted IndexedDB, embedding in cross-origin iframes via the Intermediate Iframe API (`https://accounts.google.com/gsi/intermediate`), configuring Content Security Policy (CSP), COOP, and Permissions Policy (`identity-credentials-get`) headers, or handling sign-out and revocation. Don't use for legacy Google Sign-In (`gapi.auth2`), Google Cloud IAM service accounts, or Android/iOS native Credential Manager.

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。

Discovers requirements, and generates architectural, design, and deployment guidance for a retrieval-augmented generation (RAG)-capable enterprise search system in Google Cloud. Use when users need a vector-enabled SQL database as the store and index for the embedding vectors, an open model and open-source inferencing framework, and Kubernetes containers to host all the application components. DON'T use this skill for fully-managed RAG, or SaaS search services, or when a non-SQL 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。

Generates a Gemini LiveAPI client service class in the user's chosen programming language. Use when the user wants to build, scaffold, or integrate a client that connects to the Gemini Enterprise LiveAPI websocket endpoint, handles session setup/resumption, bearer token refresh, and sending/receiving `ClientMessage`/`ServerMessage` protos. Don't use for general (non-live, non-bidirectional) Gemini API usage such as one-shot `generateContent`, embeddings, image/video generation, or fine-tuning — use the `gemini-api` skill for those.

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。

SQLite expert for WAL mode, query optimization, embedded patterns, and advanced features

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。

Turn an exploration (threads, findings, charts) into a single Markdown report — note, blog post, executive summary, KPI dashboard, slide brief, or multi-section analytical report, with embedded charts.

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。

Embed Mermaid diagrams in generated wiki pages. Use whenever documenting a runtime or request flow, a call sequence, a state machine or lifecycle, a data model or entity relationships, or non-trivial control flow, since these are clearer as a diagram than as prose. Also use when an update run touches a page that already contains a mermaid fence, or a page that contains a text fence a previous run degraded.

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。

Explain, draft, or validate Obsidian Flavored Markdown syntax: properties, wikilinks, embeds, callouts, tags, comments, highlights, block references, math, and Mermaid. Use when the user explicitly requests Obsidian note formatting or syntax help, not for general Markdown or broad vault operations.

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。

High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.

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 LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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 an agent needs to build or operate the embedded KrillinAI CLI, choose a supported command, or interpret its JSON, manifest, subtitle, dubbing, render, cover, speech, and voice outputs.

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 developing firmware for microcontrollers, implementing RTOS applications, or optimizing power consumption. Invoke for STM32, ESP32, FreeRTOS, bare-metal, power optimization, real-time systems, configure peripherals, write interrupt handlers, implement DMA transfers, debug timing issues.

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 working in the embedded browser and you want the cheapest reliable interaction loop.

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 validating or debugging a workflow in the embedded browser and you need a reproducible, evidence-first loop.

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。

Required reading whenever any chart_* tool is available. Teaches the one-tool embedding contract (call chart_render → live chart appears in chat AND a downloadable PNG lands in the queen session dir), the ECharts (data viz) vs Mermaid (structural diagrams) decision, the BI/financial-grade aesthetic baseline (no chartjunk, restrained palette, proper typography, single message per chart), and the canonical spec patterns for the 12 most-common chart types. Skipping this leads to 1990s-Excel charts, missing downloads, and the agent writing markdown image links by hand instead of letting chart_render drive the 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。

Discovery skill for the Native SDK, the complete toolkit for building native desktop applications. Apps are authored in TypeScript + declarative Native markup (.native) by default and compiled to native code with no JS runtime in the binary; Zig cores are an explicit alternative, and WebViews are the optional web-content path. Use when the user asks what the Native SDK is, how to build a Native SDK app, author native UI, scaffold an app, configure app.json or legacy app.zon, add bridge commands, embed web content, package an app, test a running app, or automate a Native SDK app.

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。

Fallback to run_shell with embedded Python when execute_code_sandbox fails due to e2b unavailability

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。