「rag」の検索結果

Use when working with Codeclimate — code Climate quality management. Covers maintainability analysis, test coverage tracking, issue detection, GPA scoring, and repository configuration. Use when managing Code Climate projects, reviewing maintainability ratings, tracking test coverage trends, or configuring analysis engines.

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 with Cloudflare R2 — cloudflare R2 object storage management via the wrangler CLI and Cloudflare API. Covers buckets, objects, usage metrics, CORS policies, and lifecycle rules. Use when managing R2 storage or reviewing bucket configurations.

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 with Bunny Cdn — bunny.net CDN management covering pull zones, storage zones, edge rules, cache configuration, and bandwidth analytics. Use when managing Bunny CDN pull zones, analyzing traffic patterns, configuring edge rules, monitoring cache hit ratios, or managing Bunny storage for origin content.

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 with Aws Efs — aWS EFS file system management and performance analysis. Covers file system inventory, mount targets, access points, throughput modes, lifecycle policies, replication configurations, and storage metrics. Use when inspecting EFS file systems, debugging mount issues, reviewing performance settings, or optimizing storage costs.

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 with Backblaze B2 — backblaze B2 cloud storage management via the b2 CLI. Covers buckets, files, lifecycle rules, keys, and usage statistics. Use when managing B2 storage or reviewing bucket configurations and costs.

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Use when working with Aws Fsx — aWS FSx file system management and health analysis. Covers FSx for Lustre, Windows File Server, NetApp ONTAP, and OpenZFS file systems, volumes, backups, data repository associations, and storage capacity metrics. Use when inspecting FSx file systems, debugging performance issues, reviewing backup configurations, or analyzing storage utilization.

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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).

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Expert en systèmes de mémoire pour agents IA (court terme, long terme, épisodique, sémantique, vector retrieval, RAG)

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LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

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6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

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RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval 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。

Micro-decision analysis. Catalog the border radii, spacing scales, type sizes, and state coverage that most people miss. Use when auditing design systems, evaluating craft quality, or training granular perception.

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Consistent radii, pixel-perfect alignment, state coverage, and the details that signal design quality. Use when polishing designs, auditing quality, or evaluating whether a design is ready to ship.

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RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval 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。

Turn the current project website into a short, polished, shareable launch video using Hyperframes. Post-shipping release hook. Use when someone says "/brag", "make a launch video", or wants to showcase what they shipped.

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Use this skill when writing unit tests, mocking dependencies, testing pure functions, or measuring coverage with Vitest. Trigger words: ユニットテスト, 単体テスト, Vitest, モック, テストを書く, カバレッジ, describe, it, expect, vi.mock.

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

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Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

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Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

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Firebase gives you a complete backend in minutes - auth, database, storage, functions, hosting. But the ease of setup hides real complexity. Security rules are your last line of defense, and they're often wrong. Firestore queries are limited, and you learn this after you've designed your data model. This skill covers Firebase Authentication, Firestore, Realtime Database, Cloud Functions, Cloud Storage, and Firebase Hosting. Key insight: Firebase is optimized for read-heavy, denormalized data. I

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Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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。

Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle.

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。

Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing.

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Azure Blob Storage SDK for Rust. Use for uploading, downloading, and managing blobs and containers.

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。