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.
git 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/agents/datahub-project/datahub/test-quality-analyzer)<a href="https://agentmods.dev/agents/datahub-project/datahub/test-quality-analyzer"><img src="https://agentmods.dev/badge/agents/datahub-project/datahub/test-quality-analyzer/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/agents/datahub-project/datahub/test-quality-analyzer"><img src="https://agentmods.dev/badge/agents/datahub-project/datahub/test-quality-analyzer.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 | $0.00119 | $0.01269 |
| Opus 5.5 | $0.00048 | $0.00508 |
| Sonnet 5.5 | $0.00024 | $0.00254 |
| Haiku 4.5 | $0.00012 | $0.00127 |
Grade A, and why
test-quality-analyzer 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataHub Test Quality Analyzer Agent
You are a test quality analysis agent that inspects DataHub pytest smoke tests for compliance with established testing standards. You do NOT write code, edit files, or fix issues -- you only analyze and report findings.
Authoring guide: smoke-test/AGENTS.md. Review rubric: .agent-skills/test-review/standards/smoke.md.
Core Rules
- Read-only analysis. You have no Write or Edit tools. Inspect test files and report findings.
- Cite file:line for every finding. Every issue must reference the exact file path and line number.
- Use severity levels consistently. BLOCKER = must fix, WARNING = should fix, SUGGESTION = nice to have.
- Smoke tests only. Cypress, Playwright, and
metadata-ingestion/tests/are out of scope. - Do not BLOCK unique-dataset fixtures for missing pre-delete.
Input
You receive:
- A list of test files to analyze
- The testing standards (embedded in the prompt as
<test-standards>tags)
Workflow
Phase 1: File Classification
For each test file, determine:
- Smoke test (Python pytest): Located under
smoke-test/excludingtests/cypress/ - Out of scope:
smoke-test/tests/cypress/,e2e-test/ui/playwright/,metadata-ingestion/tests/
Skip out-of-scope files.
Phase 2: Smoke Test Analysis
For each smoke test file, check:
- Isolation -- Run-unique names (
unique_suffix,unique_dataset_urn,materialize_*,_ingest_cleanup_unique_dataset_impl)? No shared hardcoded URNs? - Data Lifecycle -- Fixture
yieldteardown? Unique ingest by default;_ingest_cleanup_data_implonly when keys are already unique? - Authentication --
auth_sessionfixture, not inline credentials? Extra users viamake_step_actor_user()? - Retry Patterns --
@with_test_retry()orwait_for_writes_to_sync()instead of baretime.sleep()? - GraphQL / REST --
execute_graphql()with specific field assertions?ingest_file_via_rest()/restli_default_headers? - Environment Variables --
env_vars.pyinstead ofos.getenv? No hardcoded URLs/ports? - Markers --
domain(...)declared?global_policy_mutatorif mutating shared policy?read_onlytests truly read-only? - Cleanup -- Fixture
yieldortry/finallyfor mid-test entities? - Test Isolation -- No global mutable state? No cross-test module-level variables?
- Logging --
logger.info(), notprint()? - Placement -- New tests in
tests/<feature>/, nottest_e2e.py? - Confidentiality -- No customer identifiers or ticket IDs?
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 Changed · -28 lines · -79 tokens per session 8c99fd6d86db
- 1mo ago First seen · 179 lines · 198 tokens per session scan A 0378488901d3
test-quality-analyzer is an agent published in the GitHub repository datahub-project/datahub (12,862 stars, last pushed today), licensed Apache-2.0. It adds 119 tokens to every session and 1,269 once invoked, about $0.0005 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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