「rag」の検索結果

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 Cloud Hsm development including troubleshooting, best practices, limits & quotas, security, and integrations & coding patterns. Use when configuring Cloud HSM auth/networking, PKCS#11 apps, X.509 cert storage, capacity limits, or cluster issues, and other Azure Cloud Hsm related development tasks. Not for Azure Dedicated HSM (use azure-dedicated-hsm), Azure Payment Hsm (use azure-payment-hsm), Azure Key Vault (use azure-key-vault), Azure Attestation (use azure-attestation).

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。

Read your Robinhood Agentic brokerage account via the Robinhood Trading MCP - portfolio, buying power, positions, and order history - and place a single operator-instructed trade. OAuth Connect via the dashboard MCP panel.

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。

Detect suspected manipulation on prediction markets over the past 3 days by cross-referencing price/volume/comment anomalies with multilingual local-press coverage

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。

5 concrete real-life actions, leverage-scored against open loops with specificity and anti-fluff gates

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。

Amazon listing builder and optimizer for sellers. Two modes: (A) Create — build keyword-optimized listings from scratch using keyword lists + product characteristics + AI copywriting, (B) Optimize — audit existing listings, find keyword gaps, score across 8 dimensions, and rewrite with missing keywords. Integrates with amazon-keyword-research for keyword input. Works on 12 Amazon marketplaces. No API key required. Use when: (1) creating a new Amazon listing from keywords, (2) auditing an existing listing for SEO and conversion, (3) checking keyword coverage in title/bullets/description, (4) generating listing copy with target keywords and tone, (5) comparing listings against competitors, (6) preparing a listing for launch or relaunch.

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。

Expand a hard-to-build niche list from ~10 known-good seed companies into a full qualified TAM. Seed fingerprinting (how do good-fit companies ACTUALLY show up in the company database's filters) → lookalike generation (Prospeo company_lookalike, Exa findSimilar, Parallel.ai entity search) → filter mining with a precision/volume scorecard → wide pull with auto-sharding → cheap-AI qualification → live-website verification → client transparency report. Use when a vertical list feels "too small" (medical groups, hedge funds, Medicare brokerages), when known good-fit companies don't show up in keyword searches, or when someone says "expand this list", "the TAM should be bigger", "find more companies like these".

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。

Pre-send quality scorecard for any lead list. Grades duplicate rate, title diversity, bad-title patterns, catch-all domain density, ICP fit, email verification coverage. Outputs a letter grade + action items BEFORE you send. Catches bad lists before they burn inboxes. Run after email enrichment, before Smartlead upload.

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。

Writes the character-performance layer of a video prompt as behavior under pressure, not displayed emotion — objective, obstacle, tactics, beats, subtext, listening, body/status/proxemics, and mandatory eye life. Produces a reusable 150–220-word acting master profile per character plus a per-scene rewrite of it, and a locked voice prompt. Use whenever a prompt needs acting, performance, or emotion direction; whenever characters read wooden, dead-eyed, or 'AI'; whenever a character must stay themselves across many shots; or when the user asks for character behavior, mannerisms, tics, a gait, or how someone reacts. Pairs with higgsfield-facs (muscle-level AU codes) and higgsfield-seedance (the prompt the paragraph goes into).

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。

Write high-quality JUnit 5 tests with AssertJ assertions. Use when user says "add tests", "write tests", "improve test coverage", or when reviewing/creating test classes for Java code.

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。

Maintain skills wiki health - check links, naming, cross-references, and coverage

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。

Evidence-first development — surround the implementation with an executable spec and a gauntlet of constraints (tests, types, coverage, mutation) so line-by-line review becomes optional. Use when the user explicitly asks for high-assurance or evidence-first work ("reliable", "TDD", "prove it works", "I won't read the code"), or when the change touches high-stakes domains (money, auth, data loss, concurrency, public API). For routine changes where the user just wants normal tests, write good tests directly instead of invoking this 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。

LlamaIndex 官方用户文档离线知识库，用于检索并回答 LlamaIndex Python 框架的安装、RAG、数据加载、索引、检索与查询、Agent、Workflow、模型、Embedding、向量库、评估、可观测性、部署、LlamaCloud 和 LlamaParse 等问题，也可生成有文档依据的示例代码与排障建议。当用户提到 LlamaIndex、llama-index、LlamaParse、VectorStoreIndex、QueryEngine、Retriever、AgentWorkflow 或相关集成用法时使用；纯源码贡献、内部实现审查或与 LlamaIndex 无关的通用 AI 问题不使用。

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 for authorized security testing of desktop thick clients including local storage, update channels, IPC, traffic, and client-side trust boundaries.

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 for authorized security assessment of LLM applications and AI agents, including prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.

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 validation or test coverage is missing on a phase that already shipped. Audits or reconstructs VALIDATION.md, fills the gaps and generates the missing test files.

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 for a slideshow, presentation, pitch deck, or interactive deck, or to convert an existing page into a deck. Authors a HyperFrames deck with fragments, branching and presenter mode; unclear -> /hyperframes.

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。

Your software-engineering brain trust. Named personas across 11 cells covering cloud architecture, reliability/SRE/observability, data and storage, security, FinOps, languages and runtimes, systems programming, web and frontend…

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 implementing GPT chat, streaming, function calling, embeddings for RAG, images, audio or batch jobs, or troubleshooting 429 rate limits and API or TypeScript errors. Stateless OpenAI patterns that prevent 16 documented errors.

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。

PM Studio command center. Use for a quick overview of available commands and document inventory. Use when someone says "pmstudio", "show commands", "what can I generate", or "project status". Do NOT use for detailed completeness audits — use /pmstudio-nfr instead. Shows document coverage dashboard and recommends which /pmstudio-* command to run next.

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 asked to generate tests for a file, module or component, to raise coverage, or to bootstrap a suite for untested code. Detects the project's test framework and conventions first, then writes unit, integration and edge-case tests.

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 building AI features into a product: LLM integration, RAG pipelines, guardrails, streaming, AI UX, prompt engineering, or AI cost control. Treats prompts as code and validates every model output.

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 project you just shipped into a short, polished, shareable launch video (an "ad") using HyperFrames. Use when someone says "/coco-ads", "make a launch video", "brag about this", "turn this into a promo", "make an ad for this", or wants to share what they built. Reads the project code directly — no live URL or screenshots needed. Renders locally.

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。

用 MinerU 将复杂PDF文档转换为LLM友好的Markdown/JSON格式。适用于：(1) PDF转Markdown/JSON，(2) 提取PDF中的文本、表格、公式、图像，(3) 解析学术论文、技术文档、商业报告，(4) 为RAG应用准备文档数据，(5) 批量处理PDF。触发关键词："PDF解析"、"PDF转Markdown"、"提取PDF表格/公式"、"MinerU"、"parse PDF"等。不用于：PDF的阅读/填表/签名/拆分合并（用宿主pdf工具）、Word/PPT等非PDF格式解析、只需读几页内容的场景（直接读即可，不必转换）。

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。