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概要と使いどころ
Canonical rules for writing and reviewing Rust code in Shift's crates. Use whenever you add or change `.rs` code, especially error handling (`ok_or`, `ok_or_else`, `map_err`, `?`, `CoreError`, `StoreError`, `SlugError`, `BridgeError`), entity lookups by id, checked arithmetic and integer conversions, or helpers that several call sites repeat. Load `rustdoc` as well when the change adds or edits doc comments.
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。
Add or revise source-level Rustdoc for Shift Rust APIs. Use before writing or editing documentation comments in `.rs` files, especially for public or crate-visible APIs, domain structures, compiler adapters, persistence boundaries, trait implementations, unsafe code, or any API where ownership, units, coordinate spaces, mutation, I/O, sparse behavior, failure modes, panics, or concurrency are easy to misunderstand.
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 receiving critical feedback on an analysis or manuscript, before implementing suggestions, especially if feedback seems unclear or methodologically questionable - requires verification, not performative agreement or blind changes
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 analyze, categorize, or extract actionable insights from customer feedback across multiple sources, especially feature requests.
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 has raw customer interview transcripts, survey responses, support tickets, or other qualitative data and needs to extract actionable insights.
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 host a tech event, meetup, workshop, or community gathering — especially using Luma. Also use when the user mentions "host an event", "meetup", "workshop", "Luma event", or "community event".
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。
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); 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。
Keep a running implementation-notes.md during a build, logging every deviation from the plan and every discovered edge case. Use whenever implementing against an agreed plan or spec, especially in long autonomous sessions.
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 an implementation plan that leads with the decisions the user is most likely to change, and buries the mechanical work at the bottom. Use when planning is requested before a build, especially after brainstorming or an interview.
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 generation for construction workflows: RFI responses, submittal transmittals, meeting notices, change order notifications. Professional templates with context-aware 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。
Analyze BIM clash detection results and suggest resolutions. Prioritize clashes, identify patterns, assign responsibility, and track resolution 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。
Complete RFI (Request for Information) management system. Create, track, route, and analyze RFIs with automatic notifications and response deadline 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。
Generate realistic coach/player interview responses for wins, losses, controversies, injuries. Authentic coachspeak and player personalities.
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 comprehensive operational runbooks for any system or process. Reads codebase, infrastructure config, and deployment scripts to produce structured runbook.md files formatted for on-call engineers. Use when you need operations documentation, incident response guides, deployment procedures, or disaster recovery 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。
Uses MCP Connectors to read Gmail inbound leads, score them by ICP fit, draft personalized responses, and log qualified leads to your CRM. Turns your inbox into an automated pipeline.
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 lost deal notes to identify recurring objection patterns. Create objection response playbooks from won deals.
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。
Score deal velocity based on email response times, meeting frequency, and stakeholder engagement. Predict which deals will close vs stall.
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。
Analyzes a deal in progress and generates a comprehensive closing strategy. Researches the target company, maps the buying committee, builds objection responses, creates competitive positioning, and outputs a tactical deal-playbook.md with next-best-actions and a mutual close plan.
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, explains, debugs, and optimizes BI calculations - Power BI / Fabric DAX measures and calculated columns, Tableau calculated fields (FIXED/INCLUDE/EXCLUDE LOD expressions, table calculations), and Looker LookML measures, dimensions, and derived tables. Use it whenever the request involves DAX, CALCULATE, a Power BI measure, a Tableau calc or LOD, LookML, or a business metric such as YoY, YTD, rolling 12 months, running total, percent of total, distinct customers, ranking, new vs returning customers, closing balances, or basket analysis. Use it especially when someone says a measure "shows the wrong total", "is blank", "ignores my slicer/filter", "repeats the same value on every row", or "is slow" - those are filter context, context transition, LOD order-of-operations, or fanout bugs this skill is built to diagnose. Produces the formula, the context it evaluates in, and a hand-checkable test with expected numbers.
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 comprehensive API documentation including endpoint descriptions, request/response examples, authentication guides, error codes, and SDKs. Creates OpenAPI/Swagger specs, REST API docs, and developer-friendly reference materials. Use when users need to document APIs, create technical references, or write developer documentation.
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。
Compose a premium, animated landing page section by section in Next.js + Tailwind + Motion (Framer Motion) — built on a real design system, with editorial copy and motion that feels human-crafted, not template-generated. Use when building or rebuilding a landing page, marketing site, or hero/feature/pricing/CTA sections that need to look production-ready and bespoke from day 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。