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概要と使いどころ
Use when working with database architecture, MariaDB/MySQL tuning, indexing strategies, slow queries, or multi-connection patterns — even when the user just says 'this query is slow'.
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 reviewing authorization end-to-end — route → gate → policy → query scope → response filter — before changes to permissions, tenants, ownership, or admin flows.
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。
Before finishing any code (endpoint, query, migration, render, file, infra, dependency, test) — the senior pre-ship checklist of invisible cross-cutting controls AI omits, with backstop greps
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。
Configure e execute buscas web locais usando a API SERP do Bright Data com o pipeline unfancy-search (expansão de query, recuperação de SERP, reranking com RRF).
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。
Domine SQL moderno com bancos de dados cloud-native, otimização OLTP/OLAP e técnicas avançadas de query. Especialista em tuning de performance, modelagem de dados e sistemas analíticos híbridos.
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。
Consulte o banco de dados de grafo de código para entender relacionamentos de componentes, dependências e impacto de mudanças. Use quando o usuário pedir para "encontrar chamadores", "verificar dependências", "o que usa isso", "mostrar relacionamentos", "encontrar serializadores", ou ao ler código e precisar entender o que depende de um componente antes de fazer modificações.
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。
Search the Canvas Notebook workspace with qmd. Use when the user asks to find files, search workspace content, or locate related notes/documents. Default to mode=search (BM25), use vsearch (vector) only as fallback, avoid query (hybrid) by default as it is expensive. Triggers: find files, search workspace, locate notes, Finde, suche, workspace search, content search, qmd.
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 about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: "What GitNexus tools are available?", "How do I use GitNexus?"
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 search over Qdrant Cloud collections using the Python client. Covers vector search, filtered search, prefetch+RRF fusion, group API, recommend API, discovery API, batch queries, scroll, and payload indexing. Use when building search features, adding new query types, or working with qdrant-client.
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 designing or implementing search, filtering, sorting, query bars, and result states in Apple-platform apps — keep search, filter, and sort as separate jobs with clear no-result states.
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。
INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by create-issue, create-issue-from-issue-number, and update-issue. It formats an implementation-ready GitHub Issue body, runs the pre-posting checklist, executes `gh issue create` or `gh issue edit`, and optionally posts a 確認事項 follow-up comment. Invoke this skill ONLY from one of the three parent skills via the Skill tool, after the parent has completed analysis. If a user asks to 'format an issue body' or similar, route them to the appropriate parent skill (/create-issue, /create-issue-from-issue-number, or /update-issue) rather than invoking this one directly.
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。
INTERNAL/HELPER skill — do NOT invoke directly from a user query. This is the shared formatter/poster used by answer-issue-questions and breakdown-issues. It formats a scope GitHub Issue body (label cc-triage-scope, used before code analysis), runs the pre-posting checklist, and executes `gh issue create`. Invoke this skill ONLY from one of the parent skills via the Skill tool, after the parent has finalized the task breakdown. If a user asks to 'create a scope issue' or similar, route them to the appropriate parent skill (/breakdown-issues for fresh breakdowns, /answer-issue-questions for derived sub-tasks) rather than invoking this one directly.
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。
Extract clean markdown or text content from specific URLs via the Tavily CLI. Use this skill when the user has one or more URLs and wants their content, says "extract", "grab the content from", "pull the text from", "get the page at", "read this webpage", or needs clean text from web pages. Handles JavaScript-rendered pages, returns LLM-optimized markdown, and supports query-focused chunking for targeted extraction. Can process up to 20 URLs in a single call.
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 managing Alibaba Cloud Simple Application Server (SWAS OpenAPI 2020-06-01) resources end-to-end, including querying instances, starting/stopping/rebooting, executing commands (cloud assistant), managing disks/snapshots/images, firewall rules/templates, key pairs, tags, monitoring, and lightweight database operations.
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。
Validate database schema, migrations, indexes, and query behavior with structured pass/fail reporting.
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 Exasol JDBC-based virtual schemas for federated read-only access to external databases such as PostgreSQL, Oracle, MySQL, SQL Server, and DB2. Covers database-source adapter selection, `CREATE VIRTUAL SCHEMA`, `ALTER VIRTUAL SCHEMA`, `EXPLAIN VIRTUAL`, metadata refresh, and query-side troubleshooting.
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。
Exasol database interaction via the exapump CLI and Exasol SQL. Covers SQL queries and DML/DDL such as `SELECT`, `INSERT`, `UPDATE`, `DELETE`, `MERGE`, `CREATE TABLE`, `ALTER TABLE`, and `DROP TABLE`, `CREATE CONNECTION` connection objects without import or export intent, schema inspection, table design, `exapump sql` and `exapump profile`, query profiling, analytics, and Exasol-specific SQL behavior outside exasol-import and exasol-export.
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。
Deploy and use the Exasol Text AI Extension (TXAIE) with notebook-connector. Covers `deploy_license`, `initialize_text_ai_extension`, the `PYTHON3_TXAIE` language container, the Extraction API classes `StandardExtractor`, `NamedEntityExtractor`, `TopicClassifierExtractor`, `PipelineExtractor`, and `BranchExtractor`, named-entity, zero-shot classification, and feature extraction runs, default-model installation, and result-table querying such as `TXAIE_AUDIT_LOG`.
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。
Exasol User Defined Functions (UDFs) and Script Language Containers (SLCs). Covers `CREATE SCRIPT`, SCALAR and SET functions, variadic scripts with `EMITS(...)` and `default_output_columns`, the `ExaIterator` and `ExaMetadata` APIs, Python, Java, Lua, and R scripts, Lua execute scripts and `pquery`, BucketFS file access, GPU-accelerated UDFs, `ALTER SESSION SET SCRIPT_LANGUAGES` activation, and building custom Script Language Containers with `exaslct`, `exaslpm`, and `packages.yml`.
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 Exasol document-file virtual schemas for federated read-only access to object and file storage — S3, Google Cloud Storage, Azure Blob Storage, and Azure Data Lake Storage Gen2 document files. Covers document-file adapter family selection, the EDML `MAPPING` definition, `CREATE VIRTUAL SCHEMA` usage, refresh workflows, and query-side troubleshooting.
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。
Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.
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。
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
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。
Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.
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。
Query a running Convex app's logs + health in natural language (official MCP): failures, slow/expensive functions, deploy causality — scoped, evidence-backed, with a dashboard deep link.
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。