「intel」の検索結果

Expert knowledge for Azure Translator development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using translate/detect/dictionary APIs, Custom Translator, glossaries, document jobs, or Docker containers, and other Azure Translator related development tasks. Not for Azure AI Language (use azure-language-service), Azure Speech in Foundry Tools (use azure-speech), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Immersive Reader (use azure-immersive-reader).

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 Language development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using CLU, custom/health NER, CQA, sentiment/PII APIs, or Language/TA/health containers, and other Azure AI Language related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Document Intelligence (use azure-document-intelligence), Azure Speech in Foundry Tools (use azure-speech), Azure Translator (use azure-translator).

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 Face development including troubleshooting, best practices, decision making, limits & quotas, security, and integrations & coding patterns. Use when using Face detection, identification, verification, liveness, PersonGroup/Directory, or Face API quotas, and other Azure AI Face related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI Custom Vision (use azure-custom-vision), Azure AI Video Indexer (use azure-video-indexer), Azure AI Document Intelligence (use azure-document-intelligence).

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 Vision development including decision making, limits & quotas, configuration, integrations & coding patterns, and deployment. Use when using Image Analysis, Read OCR containers, smart-crop thumbnails, background removal, or video frame analysis, and other Azure AI Vision related development tasks. Not for Azure AI Custom Vision (use azure-custom-vision), Azure AI Video Indexer (use azure-video-indexer), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Immersive Reader (use azure-immersive-reader).

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 Content Understanding in Foundry Tools development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when building Content Understanding analyzers, RAG document flows, agentic workflows, REST/SDK calls, or GA migrations, and other Azure Content Understanding in Foundry Tools related development tasks. Not for Content Safety in Foundry Control Plane (use azure-content-safety), Azure Speech in Foundry Tools (use azure-speech), Azure AI Vision (use azure-ai-vision), Azure AI Document Intelligence (use azure-document-intelligence).

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。

SEO and competitive intelligence via the SemRush API. Use when asked to research competitors, analyze domains, find keyword opportunities, check backlinks, or estimate traffic. Trigger phrases: "competitor analysis", "domain overview", "keyword research", "backlink check", "traffic estimate", "SEO intelligence", "semrush", "competitive research".

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。

Scan social platforms for top-performing content by engagement before you create anything. Use this skill when the user wants to see what content is winning in a niche, find viral content patterns, research what's working on YouTube/TikTok/X/Reddit, benchmark engagement, discover content gaps, or says "what content is working for [topic]", "show me top performing content about [keyword]", "what's trending in [niche]", "find viral content about [product]", "content research for [keyword]", "what gets views in [niche]", "engagement analysis for [topic]", "scout the competition", "what videos are getting the most views about [keyword]", "social listening for [topic]", "trending content in [niche]", "top content analysis", "what hooks work for [keyword]", "content intelligence", "find winning formats".

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 a small random Hugging Face model that preserves the original architecture and can serve as a local Optimum Intel repository test fixture.

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。

Add and validate support for a Hugging Face model architecture in the Optimum Intel OpenVINO backend, including exporter configuration, patching, repository tests, and documentation.

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。

Cyber threat intelligence and OSINT analysis toolkit. Runs structured investigations and delivers analyst-grade intelligence products with sourced, trust-scored findings. Use for OSINT and CTI cases, digital-footprint and exposure review, domain/subdomain/DNS/certificate recon, web-infrastructure pivoting (favicon hashes, tracker IDs, TLS certs, phishing-kit fingerprinting, campaign clustering), username/email/phone enumeration, breach and infostealer-log triage, image forensics, geolocation, crypto-wallet and IBAN/bank-account tracing, darknet search, M365/Azure and SaaS tenant recon, China/Sinophone recon (ICP filings, PRC corporate registries, Baidu/FOFA/Quake/ZoomEye), vulnerability and ransomware lookup, threat modeling, PII redaction, and structured reporting. Commands include /case, /sweep, /query, /webpivot, /username, /phone, /email-deep, /breach-deep, /icp, /cn-corp, /iban, /stealer-log, /exposure, /threat-model, /report, /brief, /redact, /apikeys.

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 & Analytics Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

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。

Sales, GTM & Marketing Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

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。

Trading Super Intelligence Team. Named real-world personas across 8 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

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。

Risk & Compliance (GRC) Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

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。

Strategy Super Intelligence Team. Named real-world personas across 7 cells. Built local-first (LM Studio) and validator-gated. Illustrative composites; see DISCLAIMER.md.

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。

Swarm intelligence team skill — ACO-driven multi-agent exploration with hybrid LLM coordinator + Python optimization controller. Coordinator generates swarm-config from user task, then runs K iterations of N parallel ants guided by pheromone state. Universal task space via config (nodes + scoring rule). Triggers on "team swarm", "swarm intelligence", "蚁群".

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。

Competitive intelligence - extract features, pricing, tech stack from competitor sites

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。

IOC extraction, threat intelligence correlation, MITRE ATT&CK mapping, hunt hypothesis generation, and detection rule creation

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。

Structured competitive analysis with feature matrices, SWOT, positioning maps, and UX review. Covers research frameworks, pricing comparison, review mining, and visual deliverables. Use for: market research, competitive intelligence, investor decks, product strategy, sales enablement. Triggers: competitor analysis, competitive analysis, competitor teardown, market research, competitive intelligence, swot analysis, competitor comparison, market landscape, competitor review, competitive landscape, feature comparison, market positioning

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。

Trigger for: finding federal opportunities; capture and bid screening; competitor or incumbent intelligence; recompete pipelines; teaming partner research; agency, customer, or market intelligence; federal labor-rate or pricing context; or refreshing prior GovCon research. Always begin with the complete workflow menu and obtain the user's selection before research, preflight, file generation, web search, or MCP calls. Produce sourced chat findings or an optional validated GovCon Growth Brief without making a bid decision from public data 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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent 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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent 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。

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent 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。

Monitors competitor websites, pricing, content changes, hiring patterns, and product updates. Generates intelligence reports with strategic implications and trend analysis. Stores history for longitudinal tracking.

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。