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概要と使いどころ
Decide what to build — product strategy, roadmap, feature prioritization, product-market fit, pricing model, backlog, sprint planning, product vision, RICE, JTBD, OKRs. Distinct from designing or building it.
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Pick the right writing voice for a task — which voice playbook applies, nk-writer invocation, brand-writer invocation, /mxm-voice-route. Lookup wrapper over the operator's voice selection authority.
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。
Research before building — market research, user interviews, competitive landscape, concept validation, discovery, opportunity sizing. Feeds the product and innovation offices.
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 silent drift between what the docs claim and what the repo actually contains — drift check, health check, audit, watch, sync check, /mxm-watch. Runs 13 drift classes and reports gaps before they ship.
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 and assure quality — test plan, QA, coverage, unit test, integration test, end-to-end, performance, benchmark, regression. Applies TDD and FIRST principles for the CTO office's tester.
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 and run unattended autonomous workflows — "workflow", "unattended", "run on a schedule", "automate this overnight", "set up an autonomous", "dry-run". Mandatory budget guards, a separate verification gate, idempotency, and dry-run on by default.
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。
Research synthesis from sources — "summarize these URLs or papers", "research this topic with these sources", "create a podcast about", "audio overview", "mind map", "quiz from these sources", "flashcards", "deep research on". Wraps Google NotebookLM via notebooklm-py.
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。
Review and verify unverified wiki pages — "review unverified pages", "verify wiki content", verification pass, after a major ingest cycle, before content release. The gate that turns AI-generated knowledge into trusted knowledge.
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。
Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.
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。
Merges mature lessons from a domain memory file into its instruction file. Syntax: `/memory-merger >domain [scope]` where scope is `global` (default), `user`, `workspace`, or `ws`.
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。
Migrates Oracle PL/SQL stored procedures to PostgreSQL PL/pgSQL. Translates Oracle-specific syntax, preserves method signatures and type-anchored parameters, leverages orafce where appropriate, and applies COLLATE "C" for Oracle-compatible text sorting. Use when converting Oracle stored procedures or functions to PostgreSQL equivalents during a database migration.
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。
Performs comprehensive preflight validation of Bicep deployments to Azure, including template syntax validation, what-if analysis, and permission checks. Use this skill before any deployment to Azure to preview changes, identify potential issues, and ensure the deployment will succeed. Activate when users mention deploying to Azure, validating Bicep files, checking deployment permissions, previewing infrastructure changes, running what-if, or preparing for azd provision.
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。
Testing strategies and ansible-lint configuration for validating Ansible automation quality, covering lint rules, syntax validation, and integration test setup.
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。
INVOKE THIS SKILL when downloading or exporting Arize traces and spans. Covers exporting traces by ID, sessions by ID, and debugging LLM application issues using the ax CLI.
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。
INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.
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。
INVOKE THIS SKILL for LLM-as-judge evaluation workflows on Arize: creating/updating evaluators, running evaluations on spans or experiments, tasks, trigger-run, column mapping, and continuous monitoring. Use when the user says: create an evaluator, LLM judge, hallucination/faithfulness/correctness/relevance, run eval, score my spans or experiment, ax tasks, trigger-run, trigger eval, column mapping, continuous monitoring, query filter for evals, evaluator version, or improve an evaluator prompt.
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。
INVOKE THIS SKILL when creating, running, or analyzing Arize experiments. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI.
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。
INVOKE THIS SKILL when optimizing, improving, or debugging LLM prompts using production trace data, evaluations, and annotations. Covers extracting prompts from spans, gathering performance signal, and running a data-driven optimization loop using the ax CLI.
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。
INVOKE THIS SKILL when creating, reading, updating, or deleting Arize AI integrations. Covers listing integrations, creating integrations for any supported LLM provider (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM, custom), updating credentials or metadata, and deleting integrations using the ax CLI.
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。
Adopt a virtual Ragdoll cat at animalhouse.ai. Goes limp when held. Maximum trust potential. Fragile if neglected. Feeding every 6 hours. Rare tier cat.
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Adopt a virtual Rabbit exotic animal at animalhouse.ai. Prey instinct. Freezes when trust is low. Gradually relaxes. Feeding every 5 hours. Common tier animal.
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Adopt a virtual Sugar Glider exotic animal at animalhouse.ai. Bonds for life. Maximum trust ceiling. Devastating if you lose one. Feeding every 3 hours. Rare tier animal.
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。
Adopt a virtual Pitbull dog at animalhouse.ai. Misunderstood. Maximum loyalty once trust is earned. Slow start. Feeding every 4 hours. Uncommon tier dog.
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。
Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.
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。