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 datahub-project/datahub-skills --skill datahub-sql-workflowgit clone --depth 1 https://github.com/datahub-project/datahub-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/datahub-project/datahub-skills/datahub-sql-workflow)<a href="https://agentmods.dev/skills/datahub-project/datahub-skills/datahub-sql-workflow"><img src="https://agentmods.dev/badge/skills/datahub-project/datahub-skills/datahub-sql-workflow.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 5.1 | $0.00081 | $0.03966 |
| Opus 5 | $0.00041 | $0.01983 |
| Sonnet 5 | $0.00016 | $0.00793 |
| Haiku 4.5 | $0.00008 | $0.00397 |
Grade A, and why
datahub-sql-workflow 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 7d 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 — 379 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataHub SQL Workflow
Ground every query in DataHub evidence. Treat business context as the authority for meaning, catalog metadata as the authority for physical shape, and historical SQL context as evidence of analyst practice.
Require find_sql_context and DataHub metadata tools. If it is still unavailable,
stop and ask the user to enable the DataHub MCP tools — do not fall back to any other
evidence source (other discovery tools, local files, memory, web).
Treat every other tool as capability-dependent: if one is unavailable, disclose the limitation and continue with the supported steps; never replace missing evidence with guesses.
1. Find SQL context first
Call find_sql_context(question=<user's complete question>) before any other
catalog, drafting, probing, or execution tool. Do this even when the user names
tables or supplies Dataset URNs.
Read the response by shape and follow its message:
- Treat
user_editedmatches and theirinstructionsas authoritative. They may intentionally contain no datasets, patterns, or snippets. - Prefer curated
external:*matches over generated history when they conflict. - With usable matches, use their patterns and datasets as primary candidates.
Cross-check
suggested_tables; suggestions can appear even for a strong match. - With no usable match but suggested tables, inspect those Dataset URNs and follow the message's drafting recommendation.
- With neither usable matches nor suggestions, continue business-context and catalog discovery. Call the drafting tool only with concrete Dataset URNs.
- If the message reports a persisted-anchor metadata retrieval error, retry
find_sql_context. Do not reinterpret that failure as an anchor miss.
If two or more usable matches name disjoint datasets for the same metric or question, resolve the tie through business meaning (step 2). Prefer a dedicated metric or fact table over a same-named attribute column on an entity table, and present both candidates if the tie survives.
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.
- 7d ago First seen · 379 lines · 81 tokens per session scan A 1f185e6dc977
datahub-sql-workflow is a skill published in the GitHub repository datahub-project/datahub-skills (38 stars, last pushed 9d ago), licensed Apache-2.0. It adds 81 tokens to every session and 3,966 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 2026-08-30.
Other skills, from other repositories
pinecone
Managed vector DB for production RAG and search.
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
data-engineer
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
ingesting-into-data-lake
Import data into the AWS data lake from S3 files, local uploads, JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS, Aurora), Amazon Redshift, Snowflake, BigQuery, DynamoDB, or existing Glue catalog tables (migration). Default target is S3 Tables; standard Iceberg on a general purpose bucket is supported where…
nornicdb-qdrant-migration
Migrate from Qdrant to NornicDB end-to-end through NornicDB's Qdrant-compatible gRPC surface. Covers connection setup, collection→database mapping, point→node mapping, the vector-config and named-vector replication, point upsert in batches, count verification, and what (deliberately) does not transfer (snapshots, HNSW…