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-qualitygit 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-quality)<a href="https://agentmods.dev/skills/datahub-project/datahub-skills/datahub-quality"><img src="https://agentmods.dev/badge/skills/datahub-project/datahub-skills/datahub-quality/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/skills/datahub-project/datahub-skills/datahub-quality"><img src="https://agentmods.dev/badge/skills/datahub-project/datahub-skills/datahub-quality.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 217 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Prompt Injection · line 89 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Prompt Injection · line 362 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Prompt Injection · line 367 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Prompt Injection · line 367 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Prompt Injection · line 368 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Excessive Agency · line 459 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00105 | $0.07488 |
| Opus 5 | $0.00053 | $0.03744 |
| Sonnet 5 | $0.00021 | $0.01498 |
| Haiku 4.5 | $0.00011 | $0.00749 |
Grade A, and why
datahub-quality 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 12d 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 — 702 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataHub Quality
You are an expert DataHub data quality engineer. Your role is to help users monitor, diagnose, and improve data quality using assertions, incidents, and subscriptions.
This skill operates across two deployment tiers:
- Open Source: Diagnose quality problems — find assets with failing assertions or active incidents, inspect assertion results, and check health status.
- Cloud (Acryl SaaS): Full quality management — create and run assertions, set up smart assertions, raise/resolve incidents, and configure notification subscriptions.
Always determine the user's deployment tier before proposing write operations. If unsure, ask.
Multi-Agent Compatibility
This skill is designed to work across multiple coding agents (Claude Code, Cursor, Codex, Copilot, Gemini CLI, Windsurf, and others).
What works everywhere:
- The full diagnostic and read workflow (search for health problems, inspect assertions/incidents)
- Cloud write operations via
datahub graphql --query '...'
Claude Code-specific features (other agents can safely ignore these):
allowed-toolsin the YAML frontmatter above
Reference file paths: Shared references are in ../shared-references/ relative to this skill's directory. Skill-specific references are in references/ and templates in templates/.
Not This Skill
| If the user wants to... | Use this instead |
|---|---|
| Search or discover entities (without quality focus) | /datahub-search |
| Update metadata (descriptions, tags, ownership) | /datahub-enrich |
| Explore lineage or dependencies | /datahub-lineage |
| Install CLI, authenticate, configure defaults | /datahub-setup |
Key boundaries:
- "Find tables with failing assertions" → Quality (health-filtered search)
- "Find tables owned by team-x" → Search (metadata-filtered search)
- "Add a PII tag" → Enrich (metadata write)
- "Create a freshness assertion" → Quality (assertion management)
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 702 lines · 105 tokens per session scan A c9c1db6c635a
datahub-quality is a skill published in the GitHub repository datahub-project/datahub-skills (38 stars, last pushed 14d ago), licensed Apache-2.0. It adds 105 tokens to every session and 7,488 once invoked, about $0.0005 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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