Results for “aggregation”

Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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World-class application logging - structured logs, correlation IDs, log aggregation, and the battle scars from debugging production without proper logsUse when "log, logging, logger, debug, trace, audit, structured log, correlation id, request id, log level, winston, pino, bunyan, log4j, logging, observability, debugging, monitoring, tracing, structured-logs, correlation, aggregation" mentioned.

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Interactively gather parameters for `python -m cli.inference.throughput_optimizer`, generate a deployment simulation command, explain assumptions, ask for execution confirmation, then run the simulation and summarize the best parallel strategy. Use when the user wants to evaluate a model on one or more hardware profiles, compare hardware targets, compare aggregation versus disaggregation versus PD ratio optimization, choose TP/EP/MOE-DP search ranges, or obtain recommended concurrency, throughput, TTFT, and TPOT results from throughput modeling.

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Work with MongoDB (document database, BSON documents, aggregation pipelines, Atlas cloud) and PostgreSQL (relational database, SQL queries, psql CLI, pgAdmin). Use when designing database schemas, writing queries and aggregations, optimizing indexes for performance, performing database migrations, configuring replication and sharding, implementing backup and restore strategies, managing database users and permissions, analyzing query performance, or administering production databases.

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Generates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.

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Use this skill when creating, configuring, or troubleshooting FSC Relationship Groups — including Household, Professional Group, and Trust group types; member role assignment via AccountContactRelation FSC fields; Primary Group designation; and group-level wealth aggregation rollups. NOT for standard account relationships, Contact-Account relationships outside FSC, NPSP household configuration, or Financial Account role setup (use admin/financial-account-setup).

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Deploy MISP (Malware Information Sharing Platform) to aggregate, correlate, and distribute threat intelligence feeds from multiple sources for centralized IOC management and automated SIEM integration.

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変更が波及する全モジュール・全フローを特定し、集計ルール、状態遷移、符号反転、 コピペ差分、表示/帳票/APIの整合性を監査するスキル。Impact Mapで変更核から 影響範囲を可視化し、Consistency Matrixで横断整合を検証し、Copy Propagation Review戦略でコピペ実装を効率的にレビューする。 Use when one change affects multiple modules, reports, flows, or copied implementations. Audits aggregation totals, state transitions, sign inversion, and cross-module consistency.

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Create an Estuary derivation that continuously aggregates metrics (daily totals, running counts, min/max, lifetime revenue) using reduction annotations. Use for real-time dashboards, continuous materialised views, and replacing batch ETL aggregations with streaming. Derivations add complexity and cost — confirm the user wants a derivation before reaching for this. Use when user says "aggregation derivation", "derivation to sum daily totals", "running-count derivation", "transformation for min/max per group", "derive lifetime metrics", "aggregate-by-date derivation", or "real-time-dashboard derivation".

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Use when working with Cube Dev — cube.dev semantic layer management — monitor deployments, data models, pre-aggregations, query performance, and API health. Use when inspecting cube schemas, debugging slow queries, reviewing pre-aggregation builds, or auditing API usage.

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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.

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Expert en Elasticsearch (indexing, mappings, aggregations, Kibana, DSL queries)

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Use this skill when writing aggregation pipelines, grouping, joining collections, or optimizing slow MongoDB queries. Trigger words: 集計, aggregate, パイプライン, $group, $lookup, $match, 結合, グループ化, 遅いクエリ, explain.

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Deploy MISP (Malware Information Sharing Platform) to aggregate, correlate, and distribute threat intelligence feeds from multiple sources for centralized IOC management and automated SIEM integration.

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Deploy MISP (Malware Information Sharing Platform) to aggregate, correlate, and distribute threat intelligence feeds from multiple sources for centralized IOC management and automated SIEM integration.

The English summary is being prepared. Showing the original description.

Helps design ClickHouse tables, write analytical queries, and improve slow queries. Covers loading data in batches, preparing reusable summaries, and monitoring performance for large datasets, real-time dashboards, and time-series analysis.

• Designing tables for large-scale analytics
• Improving slow aggregation queries
• Loading large datasets in batches

Analyze Excel and CSV files to summarize statistics, filter records, compare groups, and join data across sheets or files. Export results as CSV, JSON, or Markdown tables for further work or sharing.

• Compare monthly sales and top-selling products
• Combine order records with customer data
• Check missing values and statistical summaries

Analyze CSV, Parquet, and JSON files to answer questions through grouping, joins, cleaning, time series, and charts. Chooses DuckDB or Polars and a suitable processing location based on the task and available memory.

• Summarizing and joining data files
• Exploring trends and distributions
• Cleaning data before analysis

Turn a business use case into a Dataverse solution using Python. Get table and relationship designs, implementation code, usage instructions, and recommendations for error recovery, logging, monitoring, and performance based on the expected workload.

• Designing customer and document management solutions
• Building data migration or scheduled synchronization jobs
• Implementing order processing and approval workflows

Analyze threat intelligence feeds to extract indicators of compromise, observable clues of possible attacks, and their campaign context. Assess source quality, standardize formats, remove duplicates, and enrich indicators for distribution to security monitoring systems.

• Assessing the quality and relevance of threat intelligence feeds
• Standardizing indicators into STIX 2.1
• Removing duplicates and adding attack campaign context

Helps design ClickHouse tables, write analytical queries, and build data ingestion workflows. Covers batch inserts, precomputed summaries, and performance monitoring for teams working with large datasets and time-based activity analysis.

• Designing tables for large analytical datasets
• Improving slow aggregation queries
• Analyzing user retention and conversion steps

Write and improve SQL for major data warehouses, accounting for differences in their syntax. Use it to build analytical queries, translate existing SQL, investigate errors, and review slow queries for performance improvements.

• Translate SQL from PostgreSQL to BigQuery
• Review slow queries for performance improvements
• Calculate retention rates and funnel conversion

Turn a plain-language data request into SQL tailored to your database. Build queries that combine tables, filter records, and calculate summaries, with explanations of each step, performance considerations, and suggestions for adjusting dates or other conditions.

• Create SQL for order counts by status
• Analyze retention by signup month
• Query large tables with date-based partitions

Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.

• Aggregating and deduplicating multiple records
• Transforming complex API responses
• Fixing data access and return format errors