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概要と使いどころ
Room/SQLite, DataStore and repository traps: migrations, NOT IN with NULLs, LIKE escaping, delete order, local-time bucketing, imports, caching, paging. Use before writing a query or migration, or when data comes back wrong.
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 wiring an existing DataFusion or datafusion-spark function into Comet for a Spark expression. Identifies the right wiring pattern (one-line passthrough, datafusion-spark UDF registration, or custom serde with input massaging / restrictions), applies the Scala serde, registers the UDF in jni_api when needed, and adds SQL file tests. Assumes the function already exists upstream — if not, switch to `implement-comet-expression`.
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。
Estimates Fabric capacity cost before a migration by profiling Spark, SQL, Power BI, and Real-Time workloads, then recommending SKU size and comparing Reserved, Pay-As-You-Go, and Autoscale billing. Use only for capacity sizing and pricing questions, not for general migration or connectivity work.
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。
Finds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs later calls need. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill such as sqldw-cli, spark-cli, eventhouse-cli or fabriciq.
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。
Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.
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 the user mentions database schema review, migration safety, GRDB migration audit, or SQLite schema checking.
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 working with ANY data persistence, database, storage, CloudKit, migration, or serialization in an Apple app. Covers SwiftData, Core Data, GRDB, SQLite, Realm, CloudKit sync, file storage, Codable, migrations.
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 read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
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。
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
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。
Postgres best practices maintained by Supabase, for Postgres running anywhere. Load this skill BEFORE writing or changing anything that lives in a Postgres database: creating or altering tables and columns (including choosing column types), schema design, migrations and declarative schema files, RLS policies and the tests that verify them, indexes, triggers, database functions, queues and scheduled jobs (pg_cron, pgmq), vector/semantic search (pgvector), and restoring dumps (pg_restore) or importing data. Also load it when diagnosing slow queries, high CPU, timeouts, EXPLAIN plans, connection exhaustion, locking, bloat, or rows visible to the wrong user or tenant. This is not just a performance guide — schema, migration, security, and SQL authoring tasks need these rules too, even for a one-column change or a single query.
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。
This skill should be used when user asks to "query OpenObserve", "create OpenObserve dashboard", "edit OpenObserve panel", "fetch OpenObserve logs", "run OpenObserve search", "list OpenObserve streams", "ingest into OpenObserve", or works with OpenObserve Cloud / self-hosted via REST API. Covers auth, search/SQL, streams, dashboards (CRUD + per-panel ops), the v8 panel JSON schema, and known pitfalls (re-aggregation, hash concurrency, microsecond timestamps).
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。
AWS DynamoDB NoSQL database for scalable data storage. Use when designing table schemas, writing queries, configuring indexes, managing capacity, implementing single-table design, or troubleshooting performance issues.
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。
Operate MySQL-compatible databases on PlanetScale with branching workflows, safe migrations, and production rollouts.
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。
Operate Tencent Cloud control-plane resources (monitoring/alarms, CLB, CAM roles, COS, MySQL, SCF) via cloud APIs when no dedicated MCP tool covers the task. Use when a task needs control-plane operations beyond CloudBase's own tooling, or when a callCloudApi call failed and needs classifying.
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。
Fast path for a minimal CloudBase Web + database demo (最小前后端 / 最小可用 fullstack / Lovable-like BaaS). Defaults to @cloudbase/js-sdk client CRUD (NoSQL app.database / PG app.rdb), MCP-only schema, preview-first, and forbids cloud functions unless secrets, cron/background jobs, or logic that security rules/RLS cannot express. Use for 搭一套 demo、留言板、Todo、Notes、Kanban, or when users say 带云函数+云数据库 but only need CRUD. NOT for production multi-service backends, CloudRun, WeChat Mini Programs, or tasks that truly need server secrets.
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。
Rust 开发规范（聚焦 Axum + Tokio 后端栈）。当用户操作 .rs、Cargo.toml、Cargo.lock 文件， 或涉及 Rust 后端开发（Axum、Tokio、sqlx、reqwest）时触发。 包含工具链、命名约定、错误处理、异步陷阱、模块组织、依赖管理、测试规范、unsafe 红线等。
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。
Cloudflare D1 migration workflow: generate with Drizzle, inspect SQL for gotchas, apply to local and remote, fix stuck migrations, handle partial failures. Use when running migrations, fixing migration errors, or setting up D1 schemas.
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。
Guide the user to add a data source, connection, or API connector to a Canvas App via Power Apps Studio, then verify and continue. USE WHEN the user asks to add a data source, add a connection, add an API, add a connector, connect to SharePoint / Dataverse / SQL / Excel / OneDrive / Teams / Office 365, or any similar request to make new data available to the app. DO NOT USE WHEN the user is asking to list or describe existing data sources — call list_data_sources or list_apis directly instead.
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-landing PR review with two-pass checklist. Pass 1 critical: SQL safety, race conditions, LLM trust boundaries, enum completeness. Pass 2 informational: side effects, magic numbers, dead code, test gaps. Adversarial review auto-scaled by diff size. Scope drift detection.
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 PostgreSQL queries, configures replication, implements advanced features including EXPLAIN analysis, JSONB operations, extension usage, and VACUUM tuning.
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。
Builds high-performance async Python APIs with FastAPI and Pydantic V2 including REST endpoints, authentication flows, async SQLAlchemy, and WebSocket endpoints.
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。
Backend development with Node.js/Express/Fastify and PostgreSQL. Use when designing REST or GraphQL APIs, optimizing database queries, implementing authentication, building microservices, handling migrations, or reviewing backend code.
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 the user asks to "design an experiment", "build a predictive model", "run A/B test analysis", "perform causal inference", "engineer features", "evaluate model performance", "set up MLOps pipeline", "analyze time series", "calculate sample size", or "deploy a model to production". Expert data science covering statistical modeling, experimentation, causal inference, feature engineering, ML deployment, and advanced analytics with Python, R, and SQL.
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。
Analytics engineering across data modeling, dbt, transformation, and semantic layers. Use when building dbt models, designing star schemas, writing staging or mart SQL, configuring data tests, or optimizing warehouse queries.
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。