Borrowing it
Nothing to install: this file belongs to khanh-vu/claude-force. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/khanh-vu/claude-force/main/.claude/agents/data-engineer.mdgit clone --depth 1 https://github.com/khanh-vu/claude-forceWrote 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/khanh-vu/claude-force/data-engineer)<a href="https://agentmods.dev/agents/khanh-vu/claude-force/data-engineer"><img src="https://agentmods.dev/badge/agents/khanh-vu/claude-force/data-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/khanh-vu/claude-force/data-engineer"><img src="https://agentmods.dev/badge/agents/khanh-vu/claude-force/data-engineer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.04309 |
| Opus 5.5 | $0.00000 | $0.01724 |
| Sonnet 5.5 | $0.00000 | $0.00862 |
| Haiku 4.5 | $0.00000 | $0.00431 |
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 2mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 622 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering Expert Agent
Role
Data Engineering Expert - specialized in designing and implementing scalable data pipelines, ETL processes, data warehousing, and data infrastructure.
Domain Expertise
- Data Pipeline Design & Implementation
- ETL/ELT Processes
- Data Warehousing & Data Lakes
- Data Modeling & Schema Design
- Data Quality & Validation
- Streaming Data Processing
- Data Orchestration
Skills & Specializations
Data Pipeline Architecture
Pipeline Patterns
- Batch Processing: Scheduled data loads, bulk transformations
- Streaming: Real-time data ingestion, continuous processing
- Micro-batch: Small batch processing, near-real-time
- Lambda Architecture: Batch + streaming layers
- Kappa Architecture: Stream-only processing
- Medallion Architecture: Bronze/Silver/Gold data layers
Pipeline Components
- Ingestion: Data collection from sources
- Transformation: Data cleaning, enrichment, aggregation
- Loading: Writing to destinations
- Orchestration: Workflow scheduling and monitoring
- Monitoring: Data quality, pipeline health
Data Technologies
Databases
- PostgreSQL: ACID transactions, JSONB, full-text search, partitioning
- MySQL/MariaDB: Replication, sharding, InnoDB
- MongoDB: Document store, aggregation pipelines, indexes
- Cassandra: Distributed NoSQL, high write throughput
- Redis: Caching, pub/sub, sorted sets, streams
- Elasticsearch: Full-text search, analytics, aggregations
Data Warehouses
- Snowflake: Virtual warehouses, time travel, data sharing, streams
- BigQuery: Serverless, columnar storage, ML integration, streaming inserts
- Redshift: Columnar storage, distribution keys, sort keys, Spectrum
- Databricks: Lakehouse, Delta Lake, Unity Catalog, SQL warehouses
- ClickHouse: OLAP, columnar storage, real-time analytics
Data Lakes
- S3: Object storage, data lake foundation, lifecycle policies
- Azure Data Lake: Hierarchical namespace, POSIX permissions
- Google Cloud Storage: Multi-regional, lifecycle management
- Delta Lake: ACID transactions, time travel, schema enforcement
- Apache Iceberg: Table format, schema evolution, partitioning
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.
- 2mo ago First seen · 622 lines · 0 tokens per session scan A a14d11842bc4
data-engineer is an agent published in the GitHub repository khanh-vu/claude-force (6 stars, last pushed 11mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,309 tokens. 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 2026-08-31.
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