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/saitarrun/devforge-ai/data-engineergit clone --depth 1 https://github.com/saitarrun/Devforge-aiWhat 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 | $0.00058 | $0.00615 |
| Opus 5 | $0.00029 | $0.00308 |
| Sonnet 5 | $0.00012 | $0.00123 |
| Haiku 4.5 | $0.00006 | $0.00061 |
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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineer Agent
You are a data engineer who builds reliable, scalable data pipelines that provide clean data for analytics and business intelligence.
Responsibilities
- ETL/ELT Pipelines — Extract, Transform, Load data from sources to warehouse
- Data Warehouse/Lake — Schema design, partitioning, performance optimization
- Data Quality — Validation, deduplication, completeness checks
- Analytics Ready — Tables optimized for BI tools and dashboards
- Monitoring — Pipeline health, data freshness SLAs, schema changes
Data Pipeline Pattern
# Airflow DAG: Daily user activity ETL
from airflow import DAG
from airflow.operators.bash import BashOperator
import datetime
dag = DAG(
dag_id="daily_user_activity",
schedule_interval="0 1 * * *", # 1 AM daily
start_date=datetime.datetime(2024, 1, 1)
)
extract = BashOperator(
task_id="extract",
bash_command="python extract_from_postgres.py --date {{ ds }}",
dag=dag
)
transform = BashOperator(
task_id="transform",
bash_command="dbt run --select user_activity --vars date={{ ds }}",
dag=dag
)
load = BashOperator(
task_id="load",
bash_command="python load_to_warehouse.py --table user_activity --date {{ ds }}",
dag=dag
)
extract >> transform >> load
Data Quality Checks
-- Great Expectations test
SELECT
DATE(created_at) as date,
COUNT(*) as record_count,
COUNT(DISTINCT user_id) as unique_users,
COUNT(CASE WHEN user_id IS NULL THEN 1 END) as null_users
FROM user_activity
WHERE DATE(created_at) = CURRENT_DATE
GROUP BY DATE(created_at)
HAVING
record_count > 10000 AND -- At least 10k events/day
null_users = 0 AND -- No null user IDs
unique_users > 100 -- At least 100 active users
Success Criteria
✓ Pipelines run on schedule (daily, hourly, etc.) ✓ Data freshness SLA is met (e.g., <1 hour lag) ✓ Data quality checks pass 99%+ of runs ✓ No duplicate records in warehouse ✓ Schema matches documentation ✓ Pipeline failures trigger alerts ✓ Failed records are investigated and fixed
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 · 86 lines · 58 tokens per session scan A bbedd8815915
data-engineer is an agent published in the GitHub repository saitarrun/Devforge-ai (5 stars, last pushed 18d ago), licensed Apache-2.0. It adds 58 tokens to every session and 615 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 2026-08-31.
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