「vin」の検索結果

When the user needs a technical SEO audit, on-page optimization guidance, or help resolving crawlability, indexation, or performance issues that affect search rankings.

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 identify at-risk accounts, understand why customers are leaving, reduce churn rate, build health scores, design save plays, or create win-back campaigns.

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 an analysis is complete and verified, and you need to decide how to report it and archive the work for reproducibility

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。

Perform value engineering analysis using CWICR data. Identify cost-saving alternatives while maintaining function and quality.

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。

Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.

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。

Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-backed), custom PyFunc, custom ResponsesAgent (LangGraph + UC Function/Vector Search); UC feature tables + FeatureLookup + point-in-time joins + Lakebase online store; declarative Feature Views (create_feature, DeltaTableSource, RollingWindow/SlidingWindow/TumblingWindow, materialize_features, streaming Kafka features). NOT for: endpoint ops (databricks-model-serving), MLflow evaluation (databricks-mlflow-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。

Pre-tax vs Roth analysis, charitable giving optimization, capital gains timing, deduction maximization. Not a substitute for CPA.

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。

Takes a manual business workflow description and designs the automated version. Maps current steps, handoffs, decision points, and bottlenecks. Designs automated flow with triggers, conditions, actions, and error handling. Outputs workflow-automation.md with before/after Mermaid diagrams, tool recommendations, implementation steps, and time savings estimate.

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 test coverage gaps and generate tests to improve coverage. Use when improving test coverage, finding untested code, or writing missing tests.

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 and roll out a price increase or packaging change without triggering churn. Segments the base by price sensitivity, models revenue and churn scenarios, decides grandfathering and notice strategy, and produces the full comms kit (customer email, in-app notice, sales talk track, support FAQ, objection handling). Use when raising prices, repackaging tiers, or moving customers to new plans.

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 landing pages for conversions, performance, and SEO. Use when improving landing pages, increasing conversions, or optimizing page performance.

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, appends, joins, and deduplicates multiple CSV, TSV, and Excel files (including multi-sheet workbooks) with pandas - mapping mismatched column names to one schema, normalizing keys, resolving conflicting values, tracking which file each row came from, and verifying the row math. Use when the user wants to combine spreadsheets or exports, stack monthly files, consolidate contact or lead lists, VLOOKUP-style join two sheets on an ID or email, or dedupe records across sources, even if they only say "put these together" or "clean up these lists into one".

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。

Debug Docker containers, fix Dockerfile issues, optimize images, and troubleshoot docker-compose. Use when having Docker problems, container issues, or optimizing Docker builds.

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 before writing code against a library, SDK, API, or framework whose current version you are not certain of — especially fast-moving ones. Trigger when an import fails, a method does not exist, a config key is rejected, a deprecation warning appears, or the user says the code was written against an old version.

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 timelines for house down payment, retirement, college fund. Monthly savings targets, investment strategies, milestone 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。

Designs relational and document database schemas from requirements or an existing app - tables and collections, keys, relationships, constraints, indexes driven by real query patterns, ERDs, and reversible migration scripts - with PostgreSQL (including Supabase) as the default and MySQL, SQLite, and MongoDB supported. Use when the user wants to model data for a new app or feature, review or fix an existing schema, choose keys or indexes, plan multi-tenancy, normalize or denormalize, or write CREATE TABLE statements or migrations, even if they just describe their app and ask "how should I store this". For moving data between database providers, use database-migrator.

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 spending patterns and find savings. 50/30/20 rule, subscription audit, debt payoff strategies, emergency fund roadmap.

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。

Mine ALL past Claude conversations to build a living 'User Manual About You'. Extract writing style, business context, goals, preferences, and patterns. Make all other skills smarter with context.

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。

Swift Charts patterns — marks, axes, selection, styling, composition, Chart3D, accessibility, and Audio Graph. Use when building or improving charts in SwiftUI.

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。

Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization 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。

Use when building, auditing or freezing a journal submission package from the canonical manuscript. Detects drift between the source and the per-journal submission copy, builds byte-preserving packages with manifests, and records current, stale or frozen status.

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 a manuscript or response-to-reviewers letter reads as AI-written. Scans for 27 AI writing patterns and rewrites flagged passages, preserving technical accuracy and bounding how much text changes. Not general copy-editing; that is /polish-language.

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。

Listen with earshot whenever you must judge, choose, trim, verify or compare sound you cannot hear - picking a sound effect, music or ambience from candidates, finding the cleanest moment in a recording, checking that audio is real and not code-synthesized, reviewing a soundtrack or a video's audio (what happens when, sync to picture, loudness, mix), A/B-ing a mix change, or making a sound in code and improving it by listening (earshot forge). Use it before telling anyone how audio sounds.

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 diverse solution candidates with category-spanning ideation methods and rotation. Use when stuck on a design or fighting repetitive LLM output.

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。