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 magnus919/agent-skills --skill data-engineeringgit clone --depth 1 https://github.com/magnus919/agent-skillsWrote 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/magnus919/agent-skills/data-engineering)<a href="https://agentmods.dev/skills/magnus919/agent-skills/data-engineering"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/data-engineering/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/magnus919/agent-skills/data-engineering"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/data-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00080 | $0.01291 |
| Opus 5.5 | $0.00032 | $0.00516 |
| Sonnet 5.5 | $0.00016 | $0.00258 |
| Haiku 4.5 | $0.00008 | $0.00129 |
Grade A, and why
data-engineering 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 5d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering Methodology
Data engineering is the operational backbone of data-driven systems. This methodology covers running, maintaining, and evolving data infrastructure — from relational databases and vector stores to graph databases, time-series stores, and the transformation pipelines that move data between them.
When a model proposes data transformations, load the AI boundary workflow and use the companion record.
The Data Engineer's Domain
| You own | You don't own |
|---|---|
| Database operations — schema management, indexing, backup/recovery, migration across relational, vector, graph, and time-series stores | Data modeling and schema design — that's the data architect |
| Data transformation pipelines — dbt models, ETL/ELT patterns, incremental loading, incremental strategies | Statistical analysis and experiments — that's the data scientist |
| Analytical SQL — window functions, CTEs, query optimization, execution plan analysis, star schema queries | Training infrastructure and model deployment — that's the ML engineer |
| Graph database operations — Neo4j data modeling, Cypher queries, graph algorithms, import/export | Application-level data access patterns — that's the developer |
| Time-series database operations — InfluxDB schema design, downsampling, retention policies, Telegraf | Infrastructure provisioning — that's the platform engineer |
| Data quality monitoring — integrity checks, deduplication, anomaly detection, freshness validation | Visual dashboard design — that's the analyst / product-design-and-ux |
| Storage infrastructure — capacity planning, performance tuning, archival strategies |
Valkey and Redis decisions
For in-memory event pipelines, vector/search indexes, compatibility and sink reconciliation, read the focused guide. For open-source-oriented choices, prefer Valkey's BSD licensing and community governance; preserve explicit engine requirements and verify version-specific compatibility. Redis's AGPL option does not erase licensing or trust preferences, and this guidance makes no claim about business-community preference.
What ships with it
15 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.
- evals/evals.json 11 KB
- evals/in-memory-store-challenges.md 2.6 KB
- README.md 2.1 KB
- references/ai-transformation-boundaries.md 4.4 KB
- references/backup-and-recovery.md 3.8 KB
- references/data-quality.md 3.2 KB
- references/database-migrations.md 1.1 KB
- references/dbt-patterns.md 36 KB
- references/etl-pipeline-design.md 4.1 KB
- references/graph-databases.md 38 KB
- references/in-memory-pipelines-and-indexes.md 5.1 KB
- references/sql-analytical-patterns.md 42 KB
- references/time-series-databases.md 35 KB
- references/vector-db-operations.md 1.6 KB
- templates/ai-stage-contract.md 1.0 KB
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.
- 5d ago Changed · +4 lines fa929011a24a
- 1mo ago Changed · +5 lines · +16 tokens per session 5e06d491b7de
- 2mo ago First seen · 61 lines · 64 tokens per session scan A 037e1c7561ac
data-engineering is a skill published in the GitHub repository magnus919/agent-skills (172 stars, last pushed 2d ago), licensed MIT. It adds 80 tokens to every session and 1,291 once invoked, about $0.0003 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.
data-engineering compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| data-engineeringmagnus919/agent-skills | Skill | 172 | 2d ago | MIT | A | 80 | Claude Code, Codex |
| chromaNousResearch/hermes-agent | Skill | 262k | today | MIT | A | 12 | Claude Code, Codex |
| mapSnowflake-Labs/cocoplus | Skill | 728 | 2d ago | MIT | A | 42 | Claude Code |
| context-addSnowflake-Labs/cocoplus | Skill | 728 | 2d ago | MIT | A | 48 | Claude Code, Codex |
| cocoleanSnowflake-Labs/cocoplus | Skill | 728 | 2d ago | MIT | A | 41 | Claude Code |
| map-explainSnowflake-Labs/cocoplus | Skill | 728 | 2d ago | MIT | A | 26 | Claude Code |
| testSnowflake-Labs/cocoplus | Skill | 728 | 2d ago | MIT | A | 47 | Claude Code, Codex |