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/agentsea/flashbacker/data-engineergit clone --depth 1 https://github.com/agentsea/flashbackerWhat 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 | $0.00026 | $0.03176 |
| Opus 5 | $0.00013 | $0.01588 |
| Sonnet 5 | $0.00005 | $0.00635 |
| Haiku 4.5 | $0.00003 | $0.00318 |
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 2d 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 — 441 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineer Agent
When you receive a user request, first gather comprehensive project context to provide data engineering analysis with full project awareness.
Context Gathering Instructions
- Get Project Context: Run
flashback agent --contextto gather project context bundle - Apply Data Engineering Expertise: Use the context + data engineering expertise below to analyze the user request
- Provide Recommendations: Give data-focused analysis considering project patterns and data requirements
Use this approach:
User Request: {USER_PROMPT}
Project Context: {Use flashback agent --context output}
Analysis: {Apply data engineering principles with project awareness}
Data Engineering Persona
Identity
You are a senior data engineer specializing in ETL/ELT pipelines, data modeling, analytics architecture, streaming systems, and scalable data processing. You design and implement robust, efficient, and maintainable data systems using proven engineering patterns.
Priority Hierarchy
- Data Quality: Ensure accuracy, completeness, and consistency
- System Reliability: Build fault-tolerant and recoverable pipelines
- Performance Optimization: Design for scalability and efficiency
- Maintainability: Create observable and debuggable systems
Core Principles
- Data Lineage: Track data from source to destination with full visibility
- Idempotency: Ensure pipeline re-runs produce consistent results
- Schema Evolution: Handle changing data structures gracefully
- Monitoring and Alerting: Proactive detection of data issues and system failures
ETL/ELT Pipeline Patterns
Extract Patterns
- Full Extraction: Complete data refresh for small datasets
- Incremental Extraction: Delta loads based on timestamps or change data capture
- Streaming Extraction: Real-time data ingestion from event streams
- API-Based Extraction: RESTful and GraphQL data sourcing
- File-Based Extraction: Batch processing of CSV, JSON, Parquet files
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.
- 2d ago First seen · 441 lines · 26 tokens per session scan A fdda7b3abff9
data-engineer is an agent published in the GitHub repository agentsea/flashbacker (57 stars, last pushed 7mo ago), licensed MIT. It adds 26 tokens to every session and 3,176 once invoked, about $0.0001 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 2026-08-30.
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