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Plan and execute user research including research planning, recruiting, interview design, qualitative synthesis, and translating findings into product decisions. Use this skill whenever the user wants to plan user research, design interviews, recruit participants, conduct discovery, run formative research, or synthesize qualitative findings. Triggers on user research, UX research, user interviews, discovery research, generative research, formative research, qualitative research, user insights, research synthesis, recruitment, interview guide, jobs to be done. Also triggers when product decisions are being made without user input and the user wants to fix that.

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 Apex code, builds Lightning Web Components, optimizes SOQL queries, implements triggers, batch jobs, platform events, and Salesforce DX CI/CD 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。

Writes Spark jobs, debugs performance issues, configures cluster settings for distributed data processing, DataFrame transformations, and structured streaming analytics.

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。

Generates code that fine-tunes a base model using SageMaker serverless training jobs. Use when the user says "start training", "fine-tune my model", "I'm ready to train", or when the plan reaches the finetuning step. Supports SFT, DPO, RLVR, and RLAIF trainers, including RLVR Lambda reward function and RLAIF custom prompt 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。

When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build personas," "customer personas," "jobs to be done," "JTBD," "what do customers say," "what are customers struggling with," "Reddit mining," "G2 reviews," "review mining," "digital watering holes," "community research," "forum research," "competitor reviews," "customer sentiment," or "find out why customers churn/convert/buy." Use for both analyzing existing research assets AND gathering new research from online sources. For writing copy informed by research, see copywriting. For acting on research to improve pages, see cro.

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 native WebMCP tools to edit the project open in Timeline Studio. Inspect clips and available assets, preview and apply multi-track, caption style/position and project framing edits; sample rendered media; run local voiceover, transcription, relighting analysis and depth-video jobs; insert media; undo; save; or export video with progress and cancellation. Use for the live browser editor, not local project-file editing.

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 Stream Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building ASA jobs with Event Hubs/Kafka, Cosmos/SQL outputs, ML/UDF queries, autoscale, or IoT Edge clusters, and other Azure Stream Analytics related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure Data Factory (use azure-data-factory), Azure Event Hubs (use azure-event-hubs), Azure Functions (use azure-functions).

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 Sentinel development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring data connectors, KQL analytics rules, Logic Apps playbooks, data lake jobs, or SAP integration, and other Azure Sentinel related development tasks. Not for Azure Defender For Cloud (use azure-defender-for-cloud), Azure Security (use azure-security), Azure Monitor (use azure-monitor), Azure External Attack Surface Management (use azure-external-attack-surface-management).

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 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 Microsoft Discovery development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building Discovery shared sessions, Bookshelf indexes, secure RBAC/CMK setups, REST jobs, or Docker-based tools, and other Azure Microsoft Discovery related development tasks. Not for Azure Portal (use azure-portal), Azure Resource Graph (use azure-resource-graph), Azure Monitor (use azure-monitor), Azure Cost Management (use azure-cost-management).

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 Batch development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring Batch pools, autoscale, private networking, CMK encryption, or CI/CD for HPC/render jobs, and other Azure Batch related development tasks. Not for Azure Container Instances (use azure-container-instances), Azure Kubernetes Service (AKS) (use azure-kubernetes-service), Azure Functions (use azure-functions), Azure Virtual Machines (use azure-virtual-machines).

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。

Parallelize independent sub-jobs across fresh-context subagents instead of one bloated context. Use when a goal branches into many independent pieces.

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。

Design taste evaluation framework — DVF primary filter, Apple/Google/Jobs design principles as explicit scoring criteria, weighted decision matrix, and option ranking for the DIVERGE wave

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。

JTBD methodology for extracting real jobs behind feature requests — job statements, abstraction layers, first-principles extraction, ODI outcome statements, and opportunity scoring

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。

Conducts Jobs-to-be-Done analysis, UX journey design, and requirements gathering through interactive discovery. Use when starting feature analysis, defining user stories, or creating acceptance criteria.

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。

Runs once by hand on the first day and once a month after that. On the first run it researches the business from its own public surfaces, reads the member's own already published posts, and builds the voice file from real samples rather than from an idea of a voice, then writes the plan, creates the proof inventory, seeds the opening slots, and registers the recurring jobs. On every monthly run it re reads the evidence the kit produced and rebuilds only what the evidence contradicts. It publishes only where you released the channel, never replies, spends only where you released it, and it never enters a credential.

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。

Runs once by hand on the first day and once a month after that. On the first run it researches the business from its own public surfaces before asking anything, writes the strategy folder, seeds the launch board, builds the tailored command center, reconciles the schedule table, and registers the recurring jobs. On every monthly run it re-reads the evidence the kit produced, applies what changed, reconciles drift, and rebuilds. It holds every outbound action unless you released the channel, and it never enters a credential.

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。

Runs once by hand on the first day and once a month after that. On the first run it researches the business from its own public presence, writes the charter with a source and a date beside every conclusion, discovers the AI Employees actually installed on this machine within a bounded search, writes the map every other routine in this kit reads, seeds the priorities and the watchlist once, builds the fleet dashboard, and registers this Employee's own seven jobs. On every monthly run it re-runs discovery and reconciles the map against what is really there. It never writes into another Employee's folder, never registers a job it does not own, sends only where you released the channel, spends only where you released it, and never touches a credential.

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。

Runs once by hand on the first day and once a month after that, light browser lane. On the first run it researches the business from its own published surfaces before asking anything, writes the strategy folder, creates the ledgers, seeds the opening cards, builds the single page dashboard, reconciles the schedule table, and registers the eight recurring jobs. On every monthly run it re-reads a month of the kit's own evidence, applies what changed, adds a tab for a channel that gained volume, rebuilds, and reconciles every registered job against the table. It holds every outbound action unless you released the channel, and it never enters a credential.

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。

Monthly, browser conditional. On its first run it discovers the member's properties from the sites they name, writes the three strategy files, creates every ledger and folder the kit reads, files the opening cards, and registers the eight scheduled jobs. Every month after that it re-reads the evidence rather than its own previous conclusions, rebuilds the topic map and the internal link map, corrects any property fact it can prove wrong, and records every change with the path of the evidence that forced it. It holds publishing and spending unless you released the channel, and it never touches a credential.

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。

Runs once by hand on the first day and once a month after that, on the first weekday, light browser lane. On the first run it researches the business from its own public surfaces before asking anything, writes the strategy folder, seeds the pipeline, reconciles the schedule table, and registers the recurring jobs. On every monthly run it re-reads the evidence the kit produced, applies what changed to the files it owns, and carries every member written setting across verbatim. It holds every outbound action unless you released the channel, and it never enters a credential.

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。

Runs once by hand on the first day and once a month after that. On the first run it researches the business from its own public surfaces before asking anything, reads the account structure once, writes the plan folder, seeds the board, creates the creative doctrine, builds the dashboard, and registers the recurring jobs. It asks about exactly two things, the monthly ceiling and the daily cap, because those are the two numbers that decide how much money can leave. On every monthly run it re reads the evidence, corrects what the ledgers prove wrong, and reconciles drift.

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 needs to understand customers deeply enough to write copy and pick targeting — consumer versus shopper, JTBD, layered persona, internal monologue, pain map, objection list, journey map, and a customer language bank pulled from real reviews and DMs. Trigger on 'customer insight', 'buyer persona', 'who is my customer', 'what do they actually want', 'jobs to be done', 'why do they hesitate before buying'. Also use when the user cannot explain who the product is for. Not for — intake questions when an agency signs a new client, see `20-client-intake-brief-global`; paid audience targeting setup, see `51-audience-research-global`; competitor study, see `08-competitor-research-global`.

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。