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 rules/datahub-project/datahub/datahub-devgit clone --depth 1 https://github.com/datahub-project/datahubWhat 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 | $0.00940 | $0.00940 |
| Opus 5 | $0.00470 | $0.00470 |
| Sonnet 5 | $0.00188 | $0.00188 |
| Haiku 4.5 | $0.00094 | $0.00094 |
Grade A, and why
datahub-dev 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 yesterday.
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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataHub Agent Workflow
datahub-dev CLI Tool
A stdlib-only Python CLI for agent-driven development. No venv needed — runs with system python3.
Always use the shell wrapper as the entry point:
scripts/dev/datahub-dev.sh <command>
Run scripts/dev/datahub-dev.sh --help to see all available subcommands (start, setup, frontend,
status, wait, rebuild, test, flag list/get, env, sync-flags, reset, nuke).
End-to-End Workflow
- Setup (once):
scripts/dev/datahub-dev.sh setup— installs Python dev environment (providesdatahubCLI). For frontend work, also runscripts/dev/datahub-dev.sh setup frontend. - Start:
scripts/dev/datahub-dev.sh start - Code: Make changes to Java/Python/frontend code
- Rebuild:
scripts/dev/datahub-dev.sh rebuild --wait - Test:
scripts/dev/datahub-dev.sh test <test-path> - Iterate: Repeat steps 2–4
Frontend hot-reload: Run scripts/dev/datahub-dev.sh frontend to start the React dev server with hot-reload (instead of rebuilding the frontend container).
Module-to-Container Mapping
| Source directory | Container |
|---|---|
metadata-service/ |
datahub-gms |
datahub-graphql-core/ |
datahub-gms |
metadata-io/ |
datahub-gms |
datahub-frontend/ |
datahub-frontend-react |
metadata-jobs/mce-consumer-job/ |
datahub-mce-consumer |
metadata-jobs/mae-consumer-job/ |
datahub-mae-consumer |
metadata-models/ |
All (triggers full rebuild + code generation) |
Environment Variables
Set any env var for DataHub containers via env set + env restart:
scripts/dev/datahub-dev.sh env set KEY=VALUE
scripts/dev/datahub-dev.sh env restart # required — changes take effect on restart
scripts/dev/datahub-dev.sh env list # show current vars and pending_restart status
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.
- yesterday First seen · 97 lines · 940 tokens per session scan A 6dba60e0c224
datahub-dev is a cursor rule published in the GitHub repository datahub-project/datahub (12,618 stars, last pushed yesterday), licensed Apache-2.0. It adds 940 tokens to every session, about $0.0047 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.
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