「edge」の検索結果

Expert knowledge for Azure Arc development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Arc-enabled Kubernetes, Arc SQL/data services, Agentic Retrieval APIs, resource bridge, or Edge Volumes, and other Azure Arc related development tasks. Not for Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Virtual Machines (use azure-virtual-machines), Azure Virtual Network (use azure-virtual-network), Azure Policy (use azure-policy).

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 knowledge for Azure AI Search development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building indexes, skillsets, indexers, vector/semantic search, or RAG knowledge bases with Azure AI Search, and other Azure AI Search related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Cosmos DB (use azure-cosmos-db).

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 knowledge for Azure AI Anomaly Detector development including troubleshooting, best practices, limits & quotas, configuration, and deployment. Use when tuning Docker-based Anomaly Detector, ACI or IoT Edge deployments, univariate/multivariate APIs, or service limits, and other Azure AI Anomaly Detector related development tasks. Not for Azure AI Metrics Advisor (use azure-metrics-advisor), Azure Monitor (use azure-monitor), Azure Machine Learning (use azure-machine-learning).

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Build and maintain a Karpathy-style LLM knowledge base — a self-compiling Obsidian markdown wiki where an Agent ingests raw sources, compiles cross-linked concept/entity/summary pages, answers queries against the corpus, lints the graph for health, and audits in-context human feedback filed from Obsidian or the local web viewer. Use when (1) scaffolding a new knowledge base for any research topic, (2) ingesting articles/papers/PDFs/web pages into raw/, (3) compiling or restructuring wiki articles from existing raw material, (4) answering questions against the wiki and filing durable answers back, (5) running lint passes for dead links / orphan pages / coverage gaps / audit shape, (6) processing human feedback from the audit/ directory and applying corrections. Not for general note-taking, daily journals, or non-wiki Obsidian use.

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Wave methodology knowledge for the buddy agent — what each wave does, its inputs and outputs, and how to route questions.

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。

Monthly, on the last weekday. Folds a month of the creative ledger against the daily metrics ledger to score angle, format, hook, and offer framing rather than individual files, tests every line currently in the creative doctrine against what the month actually measured, and rewrites the doctrine where the evidence disagrees with the assumption. It records the fatigue curve it observed, retires what has not earned, and changes nothing where the month holds too few rows to tell one angle from another.

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Monthly, on the last weekday. Re-tests every segment in strategy/icp.md against the evidence in this kit's own ledgers, then rewrites the targeting itself where the evidence disagrees with the assumption the segment was written on. It records every change in strategy/CHANGELOG.md. It sends only where you released the channel, never re-queues anyone, never edits the contacted ledger, and never writes a number it did not read out of a named file.

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。

Design or audit a repo-local markdown knowledge graph with wiki links, source-code backlinks, drift checks, and searchable sections. Use when AGENTS.md/CLAUDE.md is too flat for a large codebase or when a custom harness needs durable structured project memory.

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。

Identify dangerous API footguns, surprising default behaviors, and sharp edges in codebases and dependencies. Adapted from Trail of Bits. Use during code review to catch APIs that are easy to misuse, configurations that surprise, and abstractions that leak.

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Knowledge graph integration for token-efficient codebase understanding. Uses codebase-memory MCP for AST indexing, dependency graphs, and smart context selection. 6-71x token savings vs raw file reading.

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-page deep crawling - documentation sites, wikis, knowledge bases

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 or update continuity ledger for state preservation across clears

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。

Deployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.

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Project knowledge for developing and maintaining @cablate/mcp-google-map. Architecture, Google Maps API guide, GIS domain knowledge, and design decisions. Read this skill to onboard onto the project or make informed development decisions.

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Flowcharts are composed of **nodes** (geometric shapes) and **edges** (arrows or lines). The Mermaid code defines how nodes and edges are made and accommodates different arrow types, multi-directional arrows, and any linking to and from subgraphs.

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Integration skill for Lovable.dev projects. Activates when working with: - Lovable.dev projects with GitHub sync - Supabase Edge Functions that need deployment - Database migrations for Lovable Cloud - Projects with supabase/ directory structure - Any mention of "Lovable", "deploy edge function", "apply migration" Provides exact Lovable prompts for backend operations that can't be done via GitHub alone.

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。

Display knowledge base status and recent learnings

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 Open Knowledge Format (OKF) knowledge base from existing docs, notes, or a repo. Use to scaffold an OKF wiki.

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 a plan, spec, or the current conversation into a set of tracer-bullet tickets, each declaring its blocking edges, published to the configured tracker (edges as text in one file per ticket locally, or native blocking links on a real tracker).

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 doing ANY task involving Supabase. Triggers: Supabase products (Database, Auth, Edge Functions, Realtime, Storage, Vectors, Cron, Queues); client libraries and SSR integrations (supabase-js, @supabase/ssr) in Next.js, React, SvelteKit, Astro, Remix; auth issues (login, logout, sessions, JWT, cookies, getSession, getUser, getClaims, RLS); Supabase CLI or MCP server; schema changes, migrations, declarative schemas, security audits, Postgres extensions (pg_graphql, pg_cron, pg_vector); debugging and troubleshooting errors or unexpected behavior on Supabase projects (HTTP errors, Postgres errors, RLS surprises, permission denied, schema cache issues, timeouts, Edge Function crashes, Realtime drops, Storage failures) and reading or querying logs (Logs Explorer, ClickHouse).

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。

Graph and network analysis toolkit. Four graph types (directed, undirected, multi-edge), centrality, shortest paths, community detection, generators, I/O (GraphML, GML, edge list), matplotlib viz. For large graphs (100K+ nodes) use igraph or graph-tool; for GNNs use PyG.

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。

This skill should be used when the user wants to record bookkeeping entries (仕訳), import transaction data from CSV files, receipts, or invoices, or manage their general ledger. Trigger phrases include: "仕訳を入力", "仕訳登録", "CSVを取り込む", "レシートを読み込む", "請求書を取り込む", "帳簿を付ける", "経費を記録", "売上を記録", "仕訳を修正", "仕訳を検索", "仕訳を削除", "取引を登録", "帳簿の初期化".

Provides AWS CloudFormation patterns for Amazon Bedrock resources including agents, knowledge bases, data sources, guardrails, prompts, flows, and inference profiles. Use when creating Bedrock agents with action groups, implementing RAG with knowledge bases, configuring vector stores, setting up content moderation guardrails, managing prompts, orchestrating workflows with flows, and configuring inference profiles for model optimization.

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Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.

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。