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概要と使いどころ
AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations.
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Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.
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Evaluates and negotiates job offers. Decodes base, bonus, equity, sign-on, level and terms with unknowns kept unknown and each offer in its own currency, benchmarks against checkable pay data, finds true leverage, drafts one counter as email and call script, role-plays the recruiter, and handles the salary-expectations number, deadlines and extensions, "best and final", competing offers, counteroffers and written acceptance or decline. Country-aware (India CTC, 13th-month pay, public-sector scales). Use when the user says "I got an offer of...", "is this offer good", "should I negotiate", "how do I counter", "compare my offers", "what is my equity worth", "they asked my salary expectations", "they want an answer by Friday", "can I ask for more time", "should I accept" or "practice the negotiation call". Uses Reactive Resume MCP tools when connected. For logging the offer, its deadline and withdrawal notes, job-application-manager; for other interview answers, interview-prep.
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The design law of a slide page — canvas geometry, the text height table, the type scale, contrast pairs, spacing rhythm, and which element type carries which content. Load it when patching slide elements one at a time, when a fix is about colour or contrast, when text has to grow or shrink inside a box, when a page feels crowded or empty, or when replacement content needs real rich-text structure. It is the standard `page-clone` and `pro-editing` edit against, not a procedure of its own — those two decide which pages to touch and in what order, this one decides what a good page looks like when you put it back.
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Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
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Build and scale developer-led adoption through ecosystem programs. Use when deciding open vs curated ecosystems, building developer programs, scaling platform adoption, or designing student program pipelines.
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Build and scale partner ecosystems that drive revenue and platform adoption. Use when building partner programs from scratch, tiering partnerships, managing co-marketing, making build-vs-partner decisions, or structuring crawl-walk-run partner deployment.
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。
Govern Power Automate flows and Power Apps at scale using the FlowStudio MCP cached store. Classify flows by business impact, detect orphaned resources, audit connector usage, enforce compliance standards, manage notification rules, and compute governance scores — all without Dataverse or the CoE Starter Kit. Load this skill when asked to: tag or classify flows, set business impact, assign ownership, detect orphans, audit connectors, check compliance, compute archive scores, manage notification rules, run a governance review, generate a compliance report, offboard a maker, or any task that involves writing governance metadata to flows. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app
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Use this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
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Deploy Microsoft Sentinel as a cloud-native SIEM/SOAR by configuring multi-cloud data connectors (AWS, Azure, GCP), writing KQL detection and hunting queries, and building automated Logic Apps response playbooks. Use when establishing a centralized SOC for multi-cloud environments, migrating from a legacy SIEM, or performing petabyte-scale threat hunting; not for AWS-only setups where Security Hub/GuardDuty suffice or for endpoint EDR needs.
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Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
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Builds an automated malware submission and analysis pipeline that collects suspicious files from endpoints and email gateways, submits them to sandbox environments and multi-engine scanners, and generates verdicts with IOCs for SIEM integration. Use when SOC teams need to scale malware analysis beyond manual sandbox submissions for high-volume alert triage.
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Generate patent-office-compliant figures via the PatentFig AI API — patent line art from text (PNG or SVG), vectorize drawings to SVG/DXF/vector PDF, AI-upscale images, and convert to filing-ready TIFF/PDF/PNG. Use when the user asks for patent drawings, patent figures, invention diagrams, USPTO/EPO-compliant line art, vectorizing a drawing for CAD, or preparing figures for a patent filing. Requires a PATENTFIG_API_KEY environment variable.
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When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," or "SEO health check." For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup.
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。
Email has the highest ROI of any marketing channel. $36 for every $1 spent. Yet most startups treat it as an afterthought - bulk blasts, no personalization, landing in spam folders. This skill covers transactional email that works, marketing automation that converts, deliverability that reaches inboxes, and the infrastructure decisions that scale. Use when: keywords, file_patterns, code_patterns.
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。
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
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Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
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Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
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。
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.
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。
Fast structured generation and serving for LLMs with RadixAttention prefix caching. Use for JSON/regex outputs, constrained decoding, agentic workflows with tool calls, or when you need 5× faster inference than vLLM with prefix sharing. Powers 300,000+ GPUs at xAI, AMD, NVIDIA, and LinkedIn.
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Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.
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。
Reserved and on-demand GPU cloud instances for ML training and inference. Use when you need dedicated GPU instances with simple SSH access, persistent filesystems, or high-performance multi-node clusters for large-scale training.
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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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。
High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.
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。