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/hermes-profiles --skill data-engineeringgit clone --depth 1 https://github.com/magnus919/hermes-profilesWrote 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/hermes-profiles/data-engineering)<a href="https://agentmods.dev/skills/magnus919/hermes-profiles/data-engineering"><img src="https://agentmods.dev/badge/skills/magnus919/hermes-profiles/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/hermes-profiles/data-engineering"><img src="https://agentmods.dev/badge/skills/magnus919/hermes-profiles/data-engineering.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 | $0.00061 | $0.00994 |
| Opus 5.5 | $0.00024 | $0.00398 |
| Sonnet 5.5 | $0.00012 | $0.00199 |
| Haiku 4.5 | $0.00006 | $0.00099 |
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 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 — 54 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.
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 or UX designer |
| Storage infrastructure — capacity planning, performance tuning, archival strategies |
Reference Files
| Reference | When to load |
|---|---|
references/sql-analytical-patterns.md |
Writing analytical SQL — window functions, CTEs, execution plan reading, star schema queries, engine-specific optimization (PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake) |
references/dbt-patterns.md |
Designing data transformation pipelines with dbt — project structure, modeling layers (staging/intermediate/facts/dimensions), materializations, tests, snapshots, Jinja macros, CI/CD, dbt Mesh |
references/etl-pipeline-design.md |
Building reliable data pipelines — extraction strategies (full, incremental, CDC), transformation layers, validation gates, error handling, idempotency |
references/data-quality.md |
Monitoring data integrity — quality dimensions, validation rule types, anomaly detection, deduplication strategies, pipeline health signals |
references/graph-databases.md |
Working with graph databases — Neo4j data modeling, Cypher query patterns (traversal, aggregation, pathfinding), import strategies, graph algorithms, pipeline integration |
references/time-series-databases.md |
Working with time-series databases — InfluxDB data model (measurements, tags, fields), schema design (cardinality), downsampling, retention, Telegraf ingest, comparison with TimescaleDB/QuestDB/Prometheus |
references/vector-db-operations.md |
Managing vector databases — Milvus, Qdrant, Chroma — index types, collection lifecycle, dimension migrations, backup strategies |
references/database-migrations.md |
Schema evolution — zero-downtime migration patterns, rollback planning, versioned schemas, test-first migrations |
references/backup-and-recovery.md |
Backup strategies per data store type, RPO/RTO planning, WAL archiving, snapshot management, recovery plan template |
What ships with it
9 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.
- 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/sql-analytical-patterns.md 42 KB
- references/time-series-databases.md 35 KB
- references/vector-db-operations.md 1.6 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.
- 1mo ago First seen · 54 lines · 61 tokens per session scan A 92274ab90da7
data-engineering is a skill published in the GitHub repository magnus919/hermes-profiles (342 stars, last pushed 4mo ago), licensed MIT. It adds 61 tokens to every session and 994 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 2026-08-30.
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