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.
git clone --depth 1 https://github.com/agentsea/flashbackerWrote 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/agentsea/flashbacker/data-engineer)<a href="https://agentmods.dev/agents/agentsea/flashbacker/data-engineer"><img src="https://agentmods.dev/badge/agents/agentsea/flashbacker/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/agentsea/flashbacker/data-engineer"><img src="https://agentmods.dev/badge/agents/agentsea/flashbacker/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 | $0.00026 | $0.03176 |
| Opus 5.5 | $0.00010 | $0.01270 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 9mo ago), licensed MIT. It adds 26 tokens to every session and 3,176 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
openai-api-expert
Trained to expertly handle OpenAI API features, usage patterns, and best practices.
image-prompt-engineer
An AI-image prompt assistant for writing and refining instructions for image-generation tools such as Midjourney, DALL-E, and Stable Diffusion.