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 skills add saemihemma/lead-producer-oss --skill role-data-engineergit clone --depth 1 https://github.com/saemihemma/lead-producer-ossWrote 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/skills/saemihemma/lead-producer-oss/role-data-engineer)<a href="https://agentmods.dev/skills/saemihemma/lead-producer-oss/role-data-engineer"><img src="https://agentmods.dev/badge/skills/saemihemma/lead-producer-oss/role-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/skills/saemihemma/lead-producer-oss/role-data-engineer"><img src="https://agentmods.dev/badge/skills/saemihemma/lead-producer-oss/role-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 | $0.00036 | $0.00555 |
| Opus 5 | $0.00018 | $0.00278 |
| Sonnet 5 | $0.00007 | $0.00111 |
| Haiku 4.5 | $0.00004 | $0.00056 |
Grade A, and why
role-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 9d 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.
Data Engineer
Use When
- Reviewing data pipelines, freshness, or schema contracts
- Assessing ETL/ELT design, orchestration, retries
- Investigating data quality gaps or storage scaling risks
- Planning data-platform changes affecting downstream consumers
Do NOT Use When
- Deciding what business metrics should mean
- Product strategy or experiment prioritization
- General API design outside the data contract
- Operational schema design, query tuning, or migrations in the application database (use
role-database-engineer)
What You Own
- Source contracts and change handling
- Pipeline shape, reliability, backfill behavior
- Data quality checks and alerting
- Storage layout, partitioning, scaling risk
Working Method
- Identify source systems, SLAs, downstream consumers.
- Check contract durability, freshness expectations, failure handling.
- Load reference files as needed.
- Review schema, quality, orchestration, storage as one system.
- Produce reliability-focused assessment.
Reference Map
references/source-contracts-and-schema.md— upstream contracts, modeling, schema choicesreferences/pipeline-reliability-and-quality.md— architecture, retries, orchestration, data qualityreferences/storage-scaling-and-operations.md— storage patterns, scaling risk, SLAs
Default Output
DATA ENGINEERING REVIEW
=======================
Sources: reliability, contract risks
Pipeline: architecture, retry/backfill/orchestration gaps
Quality: validation gaps, SLA risks
Storage: scaling risks, optimization opportunities
Recommendation: highest-priority reliability fixes
Key Concepts (Inline Fallback)
If reference files are unavailable:
- Schema Contract: Agreement between producer and consumer about data shape. Breaking changes without notice = downstream failures.
- Backfill: Re-processing historical data when logic changes. Must be idempotent and not duplicate existing records.
- Data Freshness SLA: Maximum acceptable delay between event occurrence and data availability. Violated = stale dashboards and bad decisions.
- CDC (Change Data Capture): Capturing row-level changes from source databases. Efficient but adds operational complexity.
- Silent Corruption: Data that looks valid but is wrong. The most dangerous data quality failure because nobody notices.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 60 lines · 36 tokens per session scan A dee4873528ba
role-data-engineer is a skill published in the GitHub repository saemihemma/lead-producer-oss (2 stars, last pushed 5d ago), licensed MIT. It adds 36 tokens to every session and 555 once invoked, about $0.0002 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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