13 plugins from public Claude Code marketplaces. Each one bundles skills, slash commands, agents, hooks or MCP servers that install together.
Showing 13 plugins that match these filters.
agent-powerupsyeaight7/agent-powerups1
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Install the "AAS Data Engineering Platform" editorial skill bundle for Claude Code.
ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development
Twelve ICDE-specific skills for IEEE data-engineering conference submission strategy, grounded in the ICDE 2027/2026 research calls, important-dates pages, IEEE TCDE awards, and the IEEE TKDE relationship.
academicAnalytics engineering, dbt patterns, BigQuery optimization, data quality, and pipeline workflows.
developer tools12 working methods for data engineering: batch-vs-streaming, change-data-capture, data-lineage, data-partitioning, and more
Design and operate AI governance: principles, decision rights, risk tiers, lifecycle gates, fairness, transparency, privacy, security, compliance mapping, maturity and board reporting across SaaS, API, self-hosted and agentic systems. Use to admit, update or retire Agent Skills, MCP servers and A2A capabilities; approve an agent for production actions; or decide earned-autonomy promotion, reduction or revocation. Cover GxP, ALCOA+, data integrity, electronic records, assurance and QMS interfaces for life sciences. Do not use for legal interpretation (legal-strategy), data-platform mechanics (data-architect/data-engineering), security implementation (secure-software-engineering), skill package inspection (agent-skills), or executing runtime changes (agent-production-operations).
Use this skill to assess, design, and evolve data architectures, including data platforms, data products, data mesh adoption, event-driven data flows, governance, modeling, and migration decisions. Load it when teams need workload-grounded tradeoffs, ownership and quality agreements, or a current-to-target data architecture. Do not use it for pipeline or platform operations, implementation details, interface contract semantics, SQL tuning, or statistical modeling; route those to data-engineering, platform-engineering, api-design-and-evolution, postgres, or data-scientist.
Clean, profile, validate, reshape, and document messy tabular, text, JSON, and relational data through an evidence-first, reproducible workflow. Use when preparing data for analysis, reporting, modeling, ingestion, migration, or matching, including AI-suggested repair, entity-match review, score calibration limits, and reversible repair ledgers. Do not use for statistical modeling, dashboard design, or operating a named data platform; route those tasks to data-scientist, data-engineering, or the relevant tool skill.
Design and operate data infrastructure — database operations (vector, relational, graph, time-series), ETL/ELT pipeline design (dbt patterns, incremental loading), SQL analytical patterns, data quality monitoring, schema migration, and storage infrastructure management, including model-assisted transformation contracts, deterministic acceptance, bounded retries, and sink reconciliation. Do not use for statistical analysis or ML model development.
Generate, inspect, validate, and fix PDF, Word (.docx), Excel (.xlsx), and PowerPoint (.pptx) documents: turn structured content into render-ready artifacts, verify structural and output quality before delivery, and repair broken files. Use when a task involves creating, editing, converting, or validating office documents and PDFs. Do not use for ebook packaging (use epub), for images, video, or other media production, for API or code documentation, or for data pipelines (use data-engineering).
Operate PostgreSQL instances safely: configuration review, index and query-plan analysis, vacuum and bloat management, WAL archiving and point-in-time recovery, replication and failover, extensions, major-version upgrades, and evidence-based diagnostics with the bundled read-only pgdiag script. Use when running or inspecting a PostgreSQL server, diagnosing performance or backup health, or planning an upgrade or failover. Do not use for application-level data access patterns (that's backend-engineering) or schema design (that's data-architect/data-engineering).
Define observable, governed evidence for product outcomes through metric trees, event/tracking plans, instrumentation QA, and measurement governance. Use when defining product metrics, designing tracking plans, building event taxonomies, running product funnels or cohort analysis, setting up dashboard contracts, or auditing instrumentation quality. Do NOT use for statistical inference or experiment design (route to data-scientist), data pipeline implementation (route to data-engineering), data architecture decisions (route to data-architect), observability or infrastructure monitoring (route to site-reliability-engineering), or general business intelligence and dashboard building (route to BI tooling).
Design and improve human annotation programs for machine-learning training, validation, and evaluation data, including task and label schemas, active acquisition, annotator guidance, calibration, disagreement, adjudication, provenance, and cost-quality stopping. Do not use for general statistical inference, data storage pipelines, model training, or annotation-interface implementation; route those concerns to data-scientist, data-engineering, ml-engineering, or product-design-and-ux.
