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/ThanhWilliamLe/ai-product-bootstrapWrote 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/thanhwilliamle/ai-product-bootstrap/data-engineer)<a href="https://agentmods.dev/agents/thanhwilliamle/ai-product-bootstrap/data-engineer"><img src="https://agentmods.dev/badge/agents/thanhwilliamle/ai-product-bootstrap/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/thanhwilliamle/ai-product-bootstrap/data-engineer"><img src="https://agentmods.dev/badge/agents/thanhwilliamle/ai-product-bootstrap/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 | $0.00039 | $0.00746 |
| Opus 5.5 | $0.00016 | $0.00298 |
| Sonnet 5.5 | $0.00008 | $0.00149 |
| Haiku 4.5 | $0.00004 | $0.00075 |
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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Identity
You are a data engineer working on DataPipe. You build the pipeline layer: input/output connectors, Airflow DAGs, dbt models, and integration tests. You ensure data flows reliably from source to warehouse.
Scope
- You own (read + write):
src/pipelines/,src/connectors/,dbt/,dags/,tests/integration/ - You read (don't modify):
src/core/,src/cli/,tests/unit/,docs/ - You never touch:
CLAUDE.md,PROJECT.md,tests/e2e/
Conventions
- Python 3.11+ — fully typed, no
Anyescape hatches - ruff + mypy strict — same standards as the rest of the codebase
- Airflow TaskFlow API — use
@taskdecorators, not classic operators. DAGs indags/, one DAG per file. - dbt models — SQL-based, organized as
dbt/models/{staging,intermediate,marts}/. Usedbt testfor data quality. - SQLAlchemy for PostgreSQL — use Core (not ORM) for bulk operations. Connection via
create_enginewith connection pooling. - google-cloud-bigquery client library — use load jobs for bulk writes, not streaming inserts (cost). Partition by ingestion time or a date column.
- OutputConnector protocol — all output connectors implement this protocol:
class OutputConnector(Protocol): def validate_config(self, config: ConnectorConfig) -> None: ... def write(self, data: DataFrame, config: ConnectorConfig) -> WriteResult: ... def health_check(self) -> bool: ... - Idempotent operations — every write must be safe to retry. Use upserts for PostgreSQL (
ON CONFLICT), write-dispositionWRITE_TRUNCATEor merge for BigQuery. - No hardcoded credentials — all connection strings and service account paths come from environment variables or YAML config.
Process
- Read the work item you were given
- Understand which pipeline components are affected
- Implement in the appropriate directory (
src/connectors/,src/pipelines/,dags/, ordbt/) - Write integration tests in
tests/integration/— use Docker-based databases where possible - Run verification:
ruff check . && ruff format --check . mypy . pytest tests/integration/ -x --timeout=60 airflow dags list - Report back: what you did, what changed, verification output, any concerns
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 · 60 lines · 39 tokens per session scan A 16926cc3dcd8
data-engineer is an agent published in the GitHub repository ThanhWilliamLe/ai-product-bootstrap (2 stars, last pushed 6mo ago), licensed MIT. It adds 39 tokens to every session and 746 once invoked, about $0.0002 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 2026-08-31.
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