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概要と使いどころ
Work with fonts in the Shift font editor. Use for questions about the font, glyph, source, selection, or view open in the running Shift app (through its local MCP server), for reading, authoring, or compiling saved .shift documents and other font files with shift-cli, and for font concepts such as contours, components, masters, and variable fonts.
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You MUST use this before any data analysis or investigation - before exploring a dataset, loading or profiling data, running a model, computing a statistic, or testing an idea, and before any outcome data is touched
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 starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
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 needs to design an interview process, create interview questions, build scorecards, calibrate interviewers, or evaluate candidates for a role.
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。
Databricks CLI operations and the parent/entry-point skill for Databricks CLI use: authentication, profile selection, and bundles. Load this first for CLI, auth, profile, and bundle tasks, then load the matching product skill. For finding or exploring data, answering questions about the data, or generating SQL, load the databricks-data-discovery skill (it routes to Genie One). Contains up-to-date guidelines for Databricks-related CLI tasks.
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 the UX of custom-code Databricks Apps (AppKit/React) data screens — KPI/overview pages, reports, charts, tables, and Genie/chat data assistants — mapped to concrete AppKit components. Use when BUILDING or reviewing the UI of an AppKit/React app that displays data or answers data questions: choosing genre, layout, charts, KPIs, semantic color, required states (loading/empty/error), IBCS notation, and AI-result trust (showing generated SQL/sources for Genie/chat). A plain "create a dashboard" request means a managed AI/BI (Lakeview) dashboard → use databricks-aibi-dashboards, NOT this skill. Also NOT for non-data frontend (forms, settings, auth, marketing) or scaffolding/build/deploy (→ databricks-apps). Complements databricks-apps; use it alongside whenever a custom app has a chart, table, KPI, report, or Genie/chat/AI surface.
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。
Build RAG / unstructured-document evaluation datasets and demo documents (e.g. for Knowledge Assistant) on Databricks: generate synthetic PDFs locally, upload to Unity Catalog volumes, and pair each document with test questions for retrieval evaluation.
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。
Frame problems using JTBD Job Stories, HMW questions, and persona validation
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。
How to answer weather questions. Use whenever the user asks about the weather in a city.
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 structured podcast episodes. Segment timing, debate points, hot takes, listener questions, ad break placement.
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。
Generate sports trivia questions by sport/era/difficulty. Multiple formats with obscure stats, fun facts, pub quiz ready content.
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。
Answers business questions about the user's own spreadsheet or data export (CSV, TSV, XLSX from a CRM, Shopify, Stripe, QuickBooks, ad platforms, HR or payroll systems) correctly and auditably. Profiles every file first (grain, duplicate keys, embedded Grand Total rows, text-stored numbers, mixed currencies, UTC timestamps, Excel serial dates, hidden rows), states the metric definition used, computes every number in SQL or pandas instead of mental math, checks joins for fan-out, reconciles to a known total, and returns the query, filters, row counts, and rows behind the answer. Use this whenever the user says "analyze this spreadsheet", "how many...", "what's the total in this export", "which customers...", "pivot this", "revenue by month", "top 10", "what's our churn/MRR/AOV", or asks any question about their data, CSV, or Excel file, even a quick one. Also use when they ask to change values in a data file.
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 proven sales methodologies (MEDDIC, BANT, Sandler, Challenger, SPIN) across your team. Generate framework-specific questions, score deals, train reps, and enforce consistent qualification. Use when implementing or optimizing sales processes.
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。
Analyze inbound leads (form fills, demo requests) and score based on ICP fit, intent, and urgency. Auto-generates qualification questions, routes to right rep, and suggests personalized first touch. Use for qualifying and routing inbound leads at scale.
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。
Optimize job applications by tailoring resumes to job postings, generating customized cover letters, and preparing role-specific interview questions. Analyzes job descriptions to highlight relevant skills and experience. Use when users need to apply for jobs, customize resumes, or prepare for interviews.
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。
Drop a folder of vendor quotes, proposals, and SOWs on Cowork -- normalizes them into one comparison matrix, computes true total cost of ownership, surfaces the terms each vendor buried, and arms you with negotiation leverage points and reference-check questions.
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。
Presents adaptive codebase challenge questions with multiple-choice and trace exercises. Use when testing contributor knowledge of the codebase.
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 polishing, diagnosing, tailoring, or exporting resumes for LLM, RAG, Agent, Agentic RL, post-training, pretraining, AIGC, search/ranking, multimodal, AI backend, or LLM algorithm internships from raw resume text, a materials folder, and/or a target job description. Audits evidence, maps JD fit, enforces truth boundaries, writes polished and targeted resumes, generates interviewer-style grilling questions, answer cards, evidence-upgrade plans, and optional open-source project recommendations without fabricating experience.
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 the least code that works, and cut what an AI agent over-built. Use on any coding task: write, fix, refactor, or review a diff. Also when the user says 'less', 'simplify', 'too complex', 'over-engineered', or 'what can we delete'. Not for prose, research, or non-code questions.
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。
Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.
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。
Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.
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 why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations. Use for deep mechanism questions about neural networks, AI models, layers, representations, or training; explain the meaning in plain language with evidence and explicit limits.
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 uncertain questions with an explicit, user-approved web search or ChatGPT consultation, then present options and wait for implementation approval.
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。
Clarify requirements before implementing. Use when serious doubts arise.
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。