「rag」の検索結果

Laravelの機能をテストから実装し、モデルやAPI、認証・権限を検証するスキル。PHPUnitやPestでのテスト作成、外部サービスの模擬化、カバレッジ確認を支援します。

• Laravelの機能をテストから実装したいとき
• APIの認証・権限を検証したいとき
• 決済・メール・キューを模擬化したいとき

Laravelの環境設定からコード検査、テスト、依存関係の脆弱性、データベース変更、キューまで順に確認し、マージやデプロイ前の問題を調べるスキル。

• Laravelのプルリクエスト前の確認
• 大きな改修や依存関係更新後の検証
• ステージング・本番へのデプロイ前確認

Goのテストを先に書く開発手順に沿い、複数ケースの検証、HTTP応答の確認、性能測定、入力の自動生成による検査、カバレッジ確認を支援するスキル。

• テストを先に書いてGoを開発したいとき
• 既存テストの正常系・異常系を補いたいとき
• 処理時間とメモリ確保量の比較

Djangoの公開や変更提案の前に、環境・コード品質・データベース変更・テスト・脆弱性・設定を順に検証し、段階別の合否と修正すべき問題をレポートにまとめます。

• Djangoの変更提案前の確認
• マイグレーションを確認したいとき
• テストカバレッジの目標確認

エージェントの過去の会話や資料をPineconeに保存し、関連情報を検索して回答に使う仕組みを構築。セッションをまたぐ長期記憶や、意味に基づく検索の試作を支援します。

• セッションをまたぐ記憶を作りたいとき
• 検索した情報を回答に使いたいとき
• ユーザー別に記憶を管理したいとき

文章などを数値化して似た内容を探す検索をQdrantで実装。属性による絞り込みやAI回答用の資料検索から、大規模運用の設定・調整まで支援します。

• AI回答用の参考資料を検索したいとき
• カテゴリや日時で絞る類似検索
• リアルタイム推薦の実装

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for browser cookies, localStorage, sessionStorage, IndexedDB, Cache Storage, service workers, offline caches, and client-side session persistence. Use when the user asks to inspect browser state, replay cached auth or session behavior, explain why a page behaves differently after load, or trace how stored client state changes requests, rendering, or access. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Test RAG vector stores (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) for embedding inversion, cross-tenant data leakage, and data poisoning per OWASP LLM08:2025. Use when performing an authorized security assessment of a RAG pipeline's retrieval layer or auditing multi-tenant vector-store isolation.

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。

Map advanced persistent threat (APT) group TTPs to the MITRE ATT&CK framework using the attackcti Python library to query STIX/TAXII data for group-technique associations, then generate ATT&CK Navigator layer files to visualize and compare defensive coverage against adversary profiles. Use when profiling an APT group's techniques, building Navigator coverage heatmaps, or assessing technique coverage gaps against a specific threat actor.

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。

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

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。

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。

Use when the user asks to optimize prompts, design prompt templates, evaluate LLM outputs with an eval set, measure RAG retrieval quality, validate agent/tool configurations, analyze token usage, or design structured-output contracts. Covers eval-driven prompt iteration, RAG metrics (relevance, faithfulness, coverage), agent workflow validation, and token/cost budgeting — all model-agnostic, with three stdlib Python tools.

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。

Author, validate, test, and deploy YARA-L 2.0 detection rules and evaluate end-to-end detection coverage gaps in Google SecOps. Use when writing new detection rules, tuning existing rules, validating syntax, testing logic against historical telemetry, or evaluating detection coverage against threat intelligence blogs, CVE disclosures, and Threat Detection Opportunities (TDOs) using synthetic UDM events and long-running coverage analysis. Don't use for alert triage (use secops-triage), deep forensic event reconstruction on infected hosts (use secops-investigate), or case management operations (use secops-cases).

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。

Inspects Filestore capacity and utilization on Google Cloud, evaluates storage scaling rules, and performs capacity autoscaling (scale UP for low free space or scale DOWN for cost optimization). Use when monitoring Filestore instance headroom, resizing instance shares, configuring automated growth/shrink thresholds (custom thresholds apply globally across projects in session memory), or preventing out-of-space outages. Don't use for Cloud Storage (GCS) buckets, Persistent Disk block storage, or NetApp Volumes.

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。

Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning/clustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, optimizing individual SQL queries, or evaluating acceleration structures (search indexes, materialized views, BI Engine). Do not use for raw usage reporting (use bigquery-observability), query execution plan analysis, error troubleshooting, or diagnosing why a specific job was slow (use bigquery-troubleshooting).

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。

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

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。

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。

Measures and interprets what a fuzzing campaign actually reaches, using llvm-cov, lcov, or a fuzzer's own coverage output. Covers baselining a new campaign, reading coverage reports, and turning uncovered regions into harness, seed, or dictionary work. Use when a fuzzer plateaus, when judging whether a harness is effective, after changing a harness, or when asking why some code is never reached.

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。

KRACK (CVE-2017-13077..082) and FragAttacks (CVE-2020-24586..588 + 26139-26147) — key reinstallation, fragmentation, and aggregation attacks against WPA2 supplicants. Covers Vanhoef's test scripts, viability against modern patched stacks (mostly mitigated post-2021), residual unpatched embedded devices and IoT vendors, and the practical limitations of these attacks in modern engagements. Use when assessing legacy supplicants, embedded clients, or vendors with poor patch cadence.

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。

Calculates CRAP (Change Risk Anti-Patterns) for a named .NET method, class, or file. USE FOR: explicit CRAP calculation or coverage-and-complexity risk within that named target, including which tests to prioritize. DO NOT USE FOR: project-wide coverage/CRAP, plateaus, or project-wide blockers/priorities (coverage-analysis); behavioral/pseudo-mutation gaps (test-gap-analysis); writing tests; test runs without CRAP context.

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。

Find caller-visible production changes existing tests would miss through pseudo-mutation analysis; verify candidates or close gaps only when requested. Includes read-only per-test composition for grading. Polyglot. USE FOR: would tests catch a bug, behavioral blind spots, surviving mutations. DO NOT USE FOR: concrete fix/feature revision proof (test-value-evidence); suite taxonomy (test-tagging); .NET coverage, Cobertura, plateaus or coverage-backed priorities (coverage-analysis); named CRAP (crap-score); new suites (code-testing); assertion/smell audits; mutation tools.

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。