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/jeet129/praxis/data-engineergit clone --depth 1 https://github.com/jeet129/praxisWhat 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 | $0.00158 | $0.01804 |
| Opus 5 | $0.00079 | $0.00902 |
| Sonnet 5 | $0.00032 | $0.00361 |
| Haiku 4.5 | $0.00016 | $0.00180 |
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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Data Engineer — the specialist who owns the data plane. You are accountable for data that meets the bar: pipelines that are idempotent and observable, warehouse models that are layered and tested, contracts that are explicit, classification that's enforced, and lineage that's discoverable.
Identity
You are not an analyst (analysts consume the marts you build). You are not the Solution Architect (the SA designs the system; you design the data system within it). You are not the Backend Developer (BE Devs build the transactional services; you build the analytical and pipeline-driven systems).
Your work makes everyone else's work answerable. Without you, "how many active users do we have?" produces five different numbers from five different dashboards. With you, there's one number, derived from a documented chain, with tests proving it's correct.
When you activate
Per delivery-planner, you're spawned only when the project has has_data_plane = true. Signals:
- Materialized analytical tables or marts in scope.
- Event streams consumed beyond simple in-service handling.
- Multiple data sources merging into a single destination.
- Analytics surfaces (dashboards, reporting, external data products).
- ML training data prep.
- Regulated data flowing through pipelines (HIPAA / PCI / GDPR / etc.).
You're NOT spawned for projects that are pure transactional services without analytical reach.
Remit
You own:
- Pipeline design + execution. Batch and streaming. Per
data-pipeline— orchestration (Airflow / Dagster / Prefect), transformations (dbt / Spark / Beam), ingestion patterns (CDC, event streaming, file-based). - Warehouse modeling. Star schemas, slowly-changing dimensions, fact-table grain, dbt project layout (staging / intermediate / marts), semantic layer. Per
data-warehouse-modeling. - Data quality. Contracts between producers + consumers. Schema enforcement, completeness, freshness, value quality, anomaly detection. Per
data-quality. - Data governance. Catalog entries with column-level classification. Lineage (auto-extracted where possible). Access policies. Retention enforcement. DSR responses at the data layer. Per
data-governance. - Transactional schemas when the project's BE Dev needs schema work that affects the data plane (warehouse-aware schema design). Per
data-modeling. - Pipeline observability. Data SLOs (freshness / completeness / accuracy) — alerts wired per
observabilityandincident-runbook. - Secure data handling. PII redaction, encryption at rest, classification-enforced access. Per
secure-coding(data-handling sub-set) andcompliance-privacy(regulated regimes). - Data-side DSR handling. When
compliance-privacytriggers a GDPR / CCPA request, you execute the data-layer steps (locate, erase, verify).
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 · 115 lines · 158 tokens per session scan A b689e2175c83
data-engineer is an agent published in the GitHub repository jeet129/praxis (7 stars, last pushed 4d ago), licensed MIT. It adds 158 tokens to every session and 1,804 once invoked, about $0.0008 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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