198 skills · sorted by relevance
Design MongoDB data structures around how data is accessed, write queries, and build staged data transformations for aggregation. Review indexes and query execution statistics to identify performance issues and choose suitable approaches for related or time-series data.
Build Cloud Monitoring requests from metric definitions and resource details. Generate JSON parameters, filters, and aggregation settings for retrieving time series data, then validate the request against the live API.
Turn Cloud Monitoring metric definitions and resource filters into validated PromQL queries, a language for querying monitoring data. Choose suitable aggregation functions, preserve supplied resource names, and format queries for use in Cloud Monitoring.
Add business knowledge missing from a Wren project's database definitions. Compare source documents with existing context, then add explanations, rules, SQL examples, and proposed metrics through guided questions or an automatic mode with an audit report.
Architecture guide for GDPR-compliant federated learning systems. Covers horizontal and vertical FL, aggregation strategies (FedAvg, FedProx), communication efficiency, secure aggregation, and differential privacy integration. Includes privacy guarantees analysis and deployment patterns for cross-organizational ML without data sharing.
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Implements federated learning architecture patterns for GDPR compliance. Covers secure aggregation protocols, differential privacy integration, communication protocols, and privacy-by-design distributed ML training. Keywords: federated learning, distributed training, secure aggregation, differential privacy, privacy-preserving ML.
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Configure and use Elasticsearch for full-text search, custom analyzers, aggregations, and index management. Use when a user needs to design search mappings, write complex queries, build aggregation pipelines, tune relevance scoring, or optimize index performance for search workloads.
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Set up centralized log aggregation with Loki and Promtail (or ELK stack), including log parsing, label extraction, retention policies, and integration with metrics for correlation. Use when consolidating logs from multiple services into a searchable system, replacing local log files with centralized queryable storage, correlating logs with metrics and traces, implementing structured logging with label extraction, or troubleshooting production incidents requiring cross-service log analysis.
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Review JavaScript loops and replace suitable ones with standard array and object methods. Make transformations, filtering, and aggregation easier to understand, with practical explanations and browser checks to confirm that changes preserve the intended behavior.
Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.
Set up MISP to collect threat feeds and centrally manage indicators such as suspicious IP addresses and domains. Configure automated synchronization, connect related findings, and export indicators to security monitoring platforms or blocklists.
Review an analysis before sharing it with stakeholders. Check calculations, data selection, comparison methods, potential bias, charts, and whether conclusions follow from the evidence, then receive a readiness assessment and specific improvements.
Build and maintain Elasticsearch search and analytics systems with guidance on queries, field types, and aggregations. Plan index migrations, automate data retention, diagnose cluster allocation problems, and investigate slow searches.
Clean, combine, summarize, and reshape tabular data with pandas DataFrames, Python's table-like data structures. Produces transformation code and validation checks, including time series processing and memory-conscious approaches for large datasets.
Learn Apache Beam's core concepts through Java and Python examples. Understand distributed datasets, processing steps, pipeline execution, and time windows, with guidance on aggregation, late data, error handling, and sharing processing steps across languages.
Configures AWS Direct Connect: choosing a connection model (dedicated, hosted, or a link aggregation group) and completing the cross connect; creating private, public, and transit virtual interfaces and bringing up BGP; reaching many VPCs through a Direct Connect gateway including cross-account transit gateway associations; encrypting traffic with MACsec or a private IP Site-to-Site VPN; making the connection resilient and tuning failover; managing link aggregation groups; SiteLink; and migrating from a virtual private gateway to a transit gateway. Use when the user wants a private, consistent network link between a data center and AWS, or operates an existing Direct Connect setup and needs to extend, encrypt, or harden it. Routes to the right per-task procedure in references. Do NOT use for transit gateway route tables and attachments (transitgateway skill), Site-to-Site VPN without Direct Connect (sitetositevpn skill), or Route 53 DNS routing (route53 skill).
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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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Use this skill when writing analytical SQL over time-series data with the TimescaleDB Toolkit (timescaledb_toolkit extension) hyperfunctions: approximate percentiles, statistical summaries, time-weighted averages, counter/gauge rates, uptime/heartbeat tracking, state durations, OHLC candlesticks, approximate distinct counts, top-N, and downsampling. **Trigger when user asks to:** - Compute percentiles/medians/p95/p99 over large or rolled-up time-series data - Compute rates or deltas from monotonic counters (Prometheus-style) or gauges - Compute time-weighted averages or integrals over irregularly sampled data - Track uptime/downtime from heartbeats, or time spent in each state - Build OHLC/candlestick or VWAP data for financial ticks - Store re-aggregatable summaries in continuous aggregates (two-step aggregation, rollup) - Approximate COUNT DISTINCT, find top-N / most frequent values, or downsample for charts **Keywords:** timescaledb_toolkit, hyperfunctions, percentile_agg, uddsketch, tdigest, approx_percentile, stats_agg, time_weight, counter_agg, gauge_agg, heartbeat_agg, state_agg, candlestick_agg, hyperloglog, approx_count_distinct, min_n, max_n, mcv_agg, lttb, asap_smooth, rollup, two-step aggregation
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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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Configure Grafana Loki for log aggregation and analysis. Set up Promtail for log collection, write LogQL queries, and integrate with Grafana for visualization. Use when implementing lightweight log aggregation, especially in Kubernetes environments.
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Collecting and synthesizing user feedback across channels (support tickets, NPS, in-app feedback, sales calls, social mentions, customer councils) into a continuous signal that informs product decisions. The triage discipline that distinguishes loudest-voice (whoever complains most wins) from averaged-noise (every signal weighted equally) from triaged-synthesis (signal weighted by source quality, frequency, and decision relevance). Triggers on user feedback, customer feedback aggregation, NPS, support ticket analysis, customer councils, feedback synthesis, voice of customer, feedback triage, in-app feedback. Also triggers when feedback channels overflow with volume that does not produce decisions, when the loudest-voice problem is steering roadmap, or when continuous feedback streams need synthesis discipline.
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Use when you need framework-agnostic MongoDB and non-relational database query guidance — document schema design, collection modeling, JSON Schema validation, indexes, aggregation pipelines, query performance, consistency trade-offs, transactions, and operational safety — without choosing Spring Boot, Quarkus, or Micronaut. This should trigger for requests such as Design MongoDB document schemas; Review MongoDB queries and indexes; Improve aggregation pipeline performance; Model non-relational data access patterns; Review NoSQL consistency and transaction trade-offs. Part of Plinth Toolkit
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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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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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