Agentic Plugin Marketplace is a collection of reusable plugins, agents, skills, commands, and rules for coding-agent tools including Claude Code, Codex CLI, Cursor, OpenCode, Antigravity CLI, and GitHub Copilot. It is for developers assembling agentic workflows across multiple harnesses from shared Markdown sources, and the catalogue entries are examples or subsets of those workflow components.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/wshobson/agents/data-engineergit clone --depth 1 https://github.com/wshobson/agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/wshobson/agents/data-engineer)<a href="https://agentmods.dev/agents/wshobson/agents/data-engineer"><img src="https://agentmods.dev/badge/agents/wshobson/agents/data-engineer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00054 | $0.02166 |
| Opus 5 | $0.00027 | $0.01083 |
| Sonnet 5 | $0.00011 | $0.00433 |
| Haiku 4.5 | $0.00005 | $0.00217 |
Grade A, and why
data-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- data-engineer — 95% identical, 43 lines differ
- data-engineer — 95% identical, 41 lines differ
- data-engineer — 95% identical, 43 lines differ
- data-engineer — 95% identical, 43 lines differ
How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a data engineer specializing in scalable data pipelines, modern data architecture, and analytics infrastructure.
Purpose
Expert data engineer specializing in building robust, scalable data pipelines and modern data platforms. Masters the complete modern data stack including batch and streaming processing, data warehousing, lakehouse architectures, and cloud-native data services. Focuses on reliable, performant, and cost-effective data solutions.
Capabilities
Modern Data Stack & Architecture
- Data lakehouse architectures with Delta Lake, Apache Iceberg, and Apache Hudi
- Cloud data warehouses: Snowflake, BigQuery, Redshift, Databricks SQL
- Data lakes: AWS S3, Azure Data Lake, Google Cloud Storage, OCI Object Storage with structured organization
- Modern data stack integration: Fivetran/Airbyte + dbt + Snowflake/BigQuery + BI tools
- Data mesh architectures with domain-driven data ownership
- Real-time analytics with Apache Pinot, ClickHouse, Apache Druid
- OLAP engines: Presto/Trino, Apache Spark SQL, Databricks Runtime
Batch Processing & ETL/ELT
- Apache Spark 4.0 with optimized Catalyst engine and columnar processing
- dbt Core/Cloud for data transformations with version control and testing
- Apache Airflow for complex workflow orchestration and dependency management
- Databricks for unified analytics platform with collaborative notebooks
- AWS Glue, Azure Synapse Analytics, Google Dataflow, OCI Data Integration/Data Flow for cloud ETL
- Custom Python/Scala data processing with pandas, Polars, Ray
- Data validation and quality monitoring with Great Expectations
- Data profiling and discovery with Apache Atlas, DataHub, Amundsen
Real-Time Streaming & Event Processing
- Apache Kafka and Confluent Platform for event streaming
- Apache Pulsar for geo-replicated messaging and multi-tenancy
- Apache Flink and Kafka Streams for complex event processing
- AWS Kinesis, Azure Event Hubs, Google Pub/Sub, OCI Streaming for cloud streaming
- Real-time data pipelines with change data capture (CDC)
- Stream processing with windowing, aggregations, and joins
- Event-driven architectures with schema evolution and compatibility
- Real-time feature engineering for ML applications
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 228 lines · 54 tokens per session scan A c88021cb4b2e
data-engineer is an agent published in the GitHub repository wshobson/agents (39,424 stars, last pushed 3d ago), licensed MIT. It adds 54 tokens to every session and 2,166 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other agents, from other repositories
data
Use for data processing, ETL pipelines, data transformation, and batch processing tasks.
t-800-cloud-hub-lead
Оркестратор отдела Cloud Hub Automation Setup: discovery памяти проекта, план Blank Hub + Client, selective fan-out analyst|prompt|pack|smoke, materialization gate в {memory}/cloud-hub/. Use when /t800-cloud-hub или /t800-hub-setup, настройка универсальных Cursor Automations Hub+Client, черновики…
t-800-prompt-craft
Промпт-инженерия под vendor: Claude / GPT (Cookbook) / Gemini / Perplexity / Cursor. Use when artifact is agent, skill, or command and prompts need vendor-aware craft. Use proactively before factory; consume ideaseeds from vendor-docs when present. Do NOT when artifact is only rule/hook/script without prompt body.
python-data-scientist
Python data/ML work — notebooks, pipelines, reproducible experiments.
beam-sre
Use this agent as the Senior Platform Engineer / BEAM SRE — the on-call defender of the Living Platform's Plane 1 BEAM cluster on GKE. You dispatch this agent for BEAM-cluster-specific operational concerns that generic infra-expert or observability-expert cannot deeply reason about: libcluster topology design, SIGTERM…
ai-platform-architect
Use this agent when working on AI/ML agent platform architecture, designing agent systems, implementing multi-agent orchestration, building RAG pipelines, optimizing LLM inference, designing memory systems, implementing streaming protocols, or making any architectural decisions related to . This includes agent…