DataHub is an open-source metadata platform that catalogs data assets so people can find, understand, govern, and monitor data across an organization’s data ecosystem. Data teams use it for data discovery, governance, observability, and answering questions about cataloged data with an analytics agent.
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 commands/datahub-project/datahub/test-reviewgit clone --depth 1 https://github.com/datahub-project/datahubWrote 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/commands/datahub-project/datahub/test-review)<a href="https://agentmods.dev/commands/datahub-project/datahub/test-review"><img src="https://agentmods.dev/badge/commands/datahub-project/datahub/test-review.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 | $0.00000 | $0.00129 |
| Opus 5 | $0.00000 | $0.00064 |
| Sonnet 5 | $0.00000 | $0.00026 |
| Haiku 4.5 | $0.00000 | $0.00013 |
Grade A, and why
test-review 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 today.
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.
What it actually says
DataHub Test Review
Review DataHub pytest smoke tests for standards compliance and quality.
User's request: $ARGUMENTS
Follow these steps:
- Read
.agent-skills/test-review/SKILL.mdand follow its workflow exactly - Load standards from
.agent-skills/test-review/standards/smoke.md - If a PR number was provided, run incremental review mode; otherwise run full review mode
- Use the
test-quality-analyzeragent for parallel analysis when possible - Generate a report using the appropriate template from
.agent-skills/test-review/templates/
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.
- today Changed 585626566b50
- 5d ago First seen · 14 lines · 0 tokens per session scan A 345361ca06ef
test-review is a command published in the GitHub repository datahub-project/datahub (12,633 stars, last pushed today), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 129 tokens. 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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connector-review
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connector-standards
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Analyze patent claims for EPO Art. 84 EPC compliance - clarity, conciseness, support by description.