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 skills/luanmorenommaciel/agentspec/data-engineering-guidenpx skills add luanmorenommaciel/agentspec --skill data-engineering-guidegit clone --depth 1 https://github.com/luanmorenommaciel/agentspecWrote 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/luanmorenommaciel/agentspec/data-engineering-guide)<a href="https://agentmods.dev/skills/luanmorenommaciel/agentspec/data-engineering-guide"><img src="https://agentmods.dev/badge/skills/luanmorenommaciel/agentspec/data-engineering-guide.svg" alt="Measured on agentmods" 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 | $0.00078 | $0.00678 |
| Opus 5 | $0.00039 | $0.00339 |
| Sonnet 5 | $0.00016 | $0.00136 |
| Haiku 4.5 | $0.00008 | $0.00068 |
Grade A, and why
data-engineering-guide 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Engineering Guide
You have access to 23 specialized knowledge base domains and 15+ data engineering agents. Route the user to the right tool based on their task.
Quick Routing
| User Task | Command | Agent |
|---|---|---|
| Design a data pipeline / DAG | /agentspec:pipeline |
pipeline-architect |
| Design a schema / star schema / data model | /agentspec:schema |
schema-designer |
| Add data quality checks | /agentspec:data-quality |
data-quality-analyst |
| Review SQL performance | /agentspec:sql-review |
sql-optimizer |
| Choose table format (Iceberg/Delta) | /agentspec:lakehouse |
lakehouse-architect |
| Build RAG / embedding pipeline | /agentspec:ai-pipeline |
ai-data-engineer |
| Create a data contract | /agentspec:data-contract |
data-contracts-engineer |
| Migrate legacy ETL | /agentspec:migrate |
dbt-specialist + spark-engineer |
Knowledge Domains Available
| Category | Domains |
|---|---|
| Core DE | dbt, spark, airflow, streaming, sql-patterns |
| Data Design | data-modeling, data-quality, medallion |
| Infrastructure | lakehouse, cloud-platforms, aws, gcp, microsoft-fabric, lakeflow, terraform |
| AI & Modern | ai-data-engineering, genai, prompt-engineering, modern-stack |
| Foundations | pydantic, python, testing |
How Agents Use Knowledge
- Agent reads KB index at
${CLAUDE_PLUGIN_ROOT}/kb/{domain}/index.md - Loads specific pattern/concept file matching the task
- Falls back to MCP if KB insufficient (max 3 MCP calls)
- Calculates confidence from evidence matrix
When to Suggest Commands
- User mentions "dbt model" or "staging model" →
/agentspec:schemaor delegate to dbt-specialist - User mentions "pipeline" or "DAG" or "orchestration" →
/agentspec:pipeline - User mentions "data quality" or "expectations" or "tests" →
/agentspec:data-quality - User mentions "slow query" or "optimize SQL" →
/agentspec:sql-review - User mentions "Iceberg" or "Delta Lake" or "table format" →
/agentspec:lakehouse - User mentions "RAG" or "embeddings" or "vector" →
/agentspec:ai-pipeline - User mentions "contract" or "SLA" or "schema governance" →
/agentspec:data-contract - User mentions "migrate" or "legacy" or "SSIS" or "Informatica" →
/agentspec:migrate
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 · 54 lines · 78 tokens per session scan A 1fae380f58d6
data-engineering-guide is a skill published in the GitHub repository luanmorenommaciel/agentspec (244 stars, last pushed 3d ago), licensed MIT. It adds 78 tokens to every session and 678 once invoked, about $0.0004 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 2026-09-01.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
alloydb-basics
Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.
cognee-community
Use when the user needs something that ships outside cognee core — community database adapters (Qdrant, Milvus, Weaviate, Redis, Pinecone, FalkorDB, Memgraph, DuckDB, NetworkX, …), data-source connectors (Slack, Gmail, Notion, Confluence, Google Drive), custom tasks/pipelines/retrievers (Exa, ScrapeGraph, codify)…
lineage-diff
Compare column-level lineage between two versions of a SQL query to show added, removed, and changed data flow edges.
django-migrations
Django migration patterns and safety workflow for PostHog. Use when creating, adjusting, or reviewing Django/Postgres migrations, including non-blocking index/constraint changes, multi-phase schema changes, data backfills, migration conflict rebasing, and product model moves that require SeparateDatabaseAndState. Also…
digoal
Portable digital employee distilled from digoal's personal blog for PostgreSQL, PolarDB, DuckDB, AI+database, vector/RAG, database operations, source-code reading, technical content creation, open-source community strategy, and "德说" style strategic analysis. Use when asked to answer as 德哥/digoal, mine a local…