「anti」の検索結果

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.

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Databricks Vector Search endpoints and indexes for RAG and semantic search; covers index types, search modes, end-to-end RAG patterns

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Technical due diligence for M&A, investment, or acquisition. Reads a target company's codebase and generates a comprehensive tech DD report with architecture assessment, tech debt quantification, scalability analysis, security posture, team capability inference, build system quality, test coverage, deployment maturity, and open source license risks. Outputs tech-dd-report.md formatted like a real investment memo with risk ratings, remediation costs, and go/no-go recommendation.

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Market salary research, accomplishment quantification, negotiation scripts, total compensation analysis, timing strategy.

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。

Turn sales history and current stock counts into reorder points, order quantities, and a this-week purchase list -- so a product business stops stocking out of winners and stops burying cash in losers. Handles seasonality, supplier lead times, and the dead-stock cleanup.

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Convert a light-only site or app to a real dark mode -- semantic token mapping, elevation-based surfaces, recalibrated shadows and imagery -- not a naive inversion. Handles Tailwind, CSS variables, and styled-components, plus the toggle, persistence, and no-flash boot.

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API reference: HealthKit. Query for HKHealthStore, HKQuantitySample, workouts, health data read/write.

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 checking your own manuscript before submission from a reviewer's perspective. Returns anticipated Major/Minor comments with fixes, including numerical, citation and leakage checks, with an optional multi-reviewer panel. Someone else's paper is /peer-review.

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Use when finding papers or building a reference list. Searches PubMed, Semantic Scholar and bioRxiv/medRxiv, includes only references verified through an API, and generates BibTeX. Auditing an existing reference list is /verify-refs.

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 multi-source research across GitHub, HN, Reddit, arXiv, and Semantic Scholar. Use when surveying a technical topic across multiple channels.

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。

Evaluates test suites for coverage gaps, TDD/BDD compliance, and anti-patterns. Use when auditing test quality or before a major release.

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Improves code quality across duplication, efficiency, and architectural fit. Use when code passes tests but quality is poor or before a major release.

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 changesets with risk scoring, categorization by type and impact, and release note preparation. Use when extracting insights from raw change data.

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-implementation gate covering think-first, simplicity, surgical edits, and verifiable goals. Use when starting implementation to verify the approach.

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。

Scores feature worthiness and enforces branch-size limits against overengineering. Use when evaluating whether a feature belongs in the current scope or branch.

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。

Defines testing quality metrics, coverage thresholds, and anti-patterns. Use when establishing test gates or validating a test suite's coverage targets.

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。

Audits changes for additive bias and Iron Law compliance. Use when reviewing completed work before merging or after AI-assisted implementation.

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。

Models a business in its own language. Use when the domain has real business rules to capture.

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。

Tạo AI Skill mới từ ý tưởng hoặc quy trình công việc. Kết hợp phỏng vấn thông minh, pattern detection, và quantitative evaluation để tạo skill chất lượng production. Dùng khi user nói "tạo skill", "biến quy trình thành skill", "make a new skill", "tự động hóa công việc này", hoặc mô tả quy trình lặp lại mà họ muốn AI tự làm. Cũng dùng khi user muốn cải thiện, test, hoặc optimize skill hiện có.

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Use DocArray for multimodal Pydantic-style documents, typed DocList and DocVec batches, serialization and local storage, FastAPI payloads, and vector retrieval indexes.

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。

Route Baichuan2 chat inference, deployment/quantization, and fine-tuning workflows.

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。

Routes substantive ML, AI, data, scientific-computing, and software-engineering requests to the smallest useful set of managed repository skills. Invoke proactively when a request names or implies a package, framework, model family, dataset, modality, workflow, backend, deployment target, evaluation method, or implementation approach that may benefit from repository guidance, even if no repository is named. Narrow progressively from area to family to repository root: inspect only the one or two most likely area pages; compare candidates by capability, task surface, model/data format, training versus inference versus evaluation intent, runtime constraints, and root-skill description; then open only the selected root and relevant sub-skills, references, or scripts. Select multiple repositories only when each adds a distinct capability. Do not load the whole collection, treat dependencies or incidental integrations as capabilities, choose by name alone, or force a match when no exact taxonomy family applies.

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Route AIMET install, PyTorch/ONNX quantization, GenAILab, model access, cluster/Pod, Qualcomm SDK, optimization, export, and repository tasks to focused operating 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。

Semantic memory and context - store and retrieve information with embeddings for similarity search. Use for long-term memory, context recall, and knowledge persistence.

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。