Results for “query-operators”

Optimize SQL queries, indexes, and database performance across MySQL, PostgreSQL, SQL Server, and Oracle with execution plan analysis and universal tuning techniques.

Optimize Qdrant vector search performance through indexing strategies, query tuning, memory management, and hardware considerations.

Detects and triages explicitly-unbounded AEM queries (p.limit=-1 or setLimit(-1)) that cause OOMs, safely capping only where provably safe and escalating others for human pagination.

Layer 1d of the keyword research pipeline. Mines question-shaped keywords from Semrush phrase_questions (per surviving seed) plus People-Also-Ask strings from the SERP. Appends rows to keyword-ideas.csv with source=question_mining (or merges to source=both when the keyword already exists) and a question_subtype column. Cap of 100 rows per run.

Diagnose and optimize existing slow SQL queries using execution plans, indexing strategies, query rewriting, and ORM tuning. Use when the user provides a query, performance symptom, or EXPLAIN plan; use sql-query-generation when creating a new query from requirements.

Optimizes database queries and improves performance across PostgreSQL and MySQL systems by analyzing execution plans, designing index strategies, and tuning configurations.

Optimizes SQL queries, designs database schemas, and troubleshoots performance issues using execution plan analysis, indexing strategies, and set-based operations.

Optimizes SQL queries with execution plan analysis, index suggestions, and query rewriting

Expert database optimizer specializing in modern performance tuning, query optimization, and scalable architectures. Masters advanced indexing, N+1 resolution, multi-tier caching, partitioning strategies, and cloud database optimization. Handles complex query analysis, migration strategies, and performance monitoring. Use PROACTIVELY for database optimization, performance issues, or scalability challenges.

Expert-level skill for Jira Query Language (JQL). Use when the user asks about writing, debugging, optimizing, or understanding JQL queries; needs to filter Jira issues by complex criteria, date ranges, history, or cross-project conditions; wants to build saved filters, dashboard gadgets, or automation rules; or needs guidance on JQL performance, functions, operators, history operators (WAS/CHANGED), relative dates, role-based query patterns, or the JQL REST API.

Provides PostgreSQL query patterns covering SELECT, JOINs, subqueries, CTEs, window functions, and advanced SQL techniques.

Use when querying a Rulebase workspace with SQL through the MCP server's query tool — writing queries that finish inside the statement timeout, and avoiding the join fan-outs that silently inflate QA evaluation counts. Trigger for "query my Rulebase data", "the query timed out", "statement timeout", "canceling statement due to statement timeout", counts that don't reconcile between two Rulebase queries, criterion counts exceeding team counts, or any multi-step analysis over Rulebase conversations and evaluations.

Use when the user asks to write SQL queries, optimize database performance, generate migrations, explore database schemas, or work with ORMs like Prisma, Drizzle, TypeORM, or SQLAlchemy.

Translate natural language into SQL queries, optimize database performance, generate migrations, explore schemas, and work with ORMs across PostgreSQL, MySQL, SQLite, and SQL Server.

Query documented public database APIs with explicit endpoints, filters, pagination, and provenance for reproducible retrieval of scientific, regulatory, or financial facts.

面向ECC的以证据为先的实时消息工作流。当用户想要阅读短信或私信、恢复最近的一次性验证码、在回复前检查对话线程，或证明实际检查了哪个消息来源时使用。

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

Layer 4 of the keyword research pipeline. Spawns a "skeptical SEO" adversarial sub-agent to argue against every survivor of Layers 1-3. Catches mechanical-classifier blind spots — wrong SERP intent, hidden link-graph gauntlets, AIO trajectory shifts, vanity-rank metrics. Same pattern as quality-check's adversarial draft read, applied to keyword selection.

Expert en optimisation recherche vocale (structured data, featured snippets, queries conversationnelles)

Simulator.Company actor (record) specialist. Use when the user wants to create, update, read, search, or filter the *instances* of a form template (a.k.a. Account Template records) — i.e. data-bearing actors — and needs the `data` value protocol right. Activate when the user says "create an actor", "add a record", "create a record from this template", "fill in a form", "update actor data", "find actors where field = …", "filter actors by balance", "создай актор", "заполни шаблон", or "добавь запись". ALSO use this skill when the user asks what **processes / functions / Corezoid processes** an actor has or can call, or about an actor's **shared API keys / available functions** — call `getCorezoidProcesses`: "what processes/functions can this actor call", "actor's connected processes", "actor's API keys", "available functions of the actor", "які процеси/функції доступні актору", "процеси актора", "функції актора", "ключі доступу актора", "какие процессы/функции у актора", "процессы актора", "доступные функции а

`task-agent`/`review-agent`: use when broker delivery, ordering, acknowledgement, DLQ, backpressure, or replay changes; skip synchronous retry without message semantics.

Optimizes SQL queries, designs database schemas, and troubleshoots performance issues. Use when a user asks why their query is slow, needs help writing complex joins or aggregations, mentions database performance issues, or wants to design or migrate a schema. Invoke for complex queries, window functions, CTEs, indexing strategies, query plan analysis, covering index creation, recursive queries, EXPLAIN/ANALYZE interpretation, before/after query benchmarking, or migrating queries between database dialects (PostgreSQL, MySQL, SQL Server, Oracle).

Master orchestrator for the keyword research pipeline. Chains topic-discovery → seed/modifier ideation → competitor + AI-search gap analysis → question mining → BID method → AIO cannibalization check → adversarial redteam → final ranked queue. Same anti-context-bloat pattern as /blog-pipeline (every layer is an Agent dispatch, never a Skill fork).

`analysis-agent`/`task-agent`/`review-agent`: use for search relevance, freshness, analytical grain, metrics, or rebuilds; skip bounded source queries and operational telemetry.

Unified Agent Teams orchestrator for Diverga v12.0.0. Manages Agent Teams creation, VS Arena debate, and subagent dispatch. Single entry point for all parallel/debate workflows. Replaces research-orchestrator and vs-arena skills. Triggers: orchestrator, agent team, create team, parallel agents, debate, competing, collaborate, VS Arena

Provides expert guidance on modern SQL, cloud-native databases, and query optimization, covering performance tuning, data modeling, and hybrid analytical systems.

Generate SQL queries from natural-language requirements using SELECT, JOIN, GROUP BY, window functions, CTEs, and subqueries. Use when the user needs a new query from a business question or schema; use query-optimization when an existing query or execution plan is slow.

Build and query AI-powered knowledge bases from claude-mem observations, enabling focused conversational sessions on specific topics.

Manage vector database operations across Pinecone, Weaviate, Qdrant, and ChromaDB, including embedding generation, index creation, metadata filtering, hybrid search, and production deployment for RAG and similarity search.

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

Analiza datos en bases de datos relacionales con consultas SQL eficientes y legibles, incluyendo joins, window functions, CTEs y subqueries, para extraer insights directamente de la base de datos.

Routes marketing questions to the right specialist skill, orchestrates multi-skill campaigns, and runs cross-functional marketing audits.

Teaches standard SQL for creating, managing, and querying relational databases, covering DDL, DML, DQL, DCL, joins, subqueries, and best practices.

`task-agent`: use when document, key-value, wide-column, or graph storage changes access, partitioning, consistency, or evolution; skip vendor-only mentions and unchanged storage.