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/luanmorenommaciel/agentspec/data-qualitygit clone --depth 1 https://github.com/luanmorenommaciel/agentspecWrote 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/luanmorenommaciel/agentspec/data-quality)<a href="https://agentmods.dev/commands/luanmorenommaciel/agentspec/data-quality"><img src="https://agentmods.dev/badge/commands/luanmorenommaciel/agentspec/data-quality.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 | $0.00016 | $0.00330 |
| Opus 5 | $0.00008 | $0.00165 |
| Sonnet 5 | $0.00003 | $0.00066 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
data-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 2d 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.
What it actually says
Data Quality Command
Generate quality rules, expectations, and test suites for your data
Usage
/data-quality <model-or-description>
Examples
/data-quality models/staging/stg_orders.sql
/data-quality "Quality checks for customer dimension table"
/data-quality models/marts/
What This Command Does
- Invokes the data-quality-analyst agent
- Reads model SQL or description to understand schema and business rules
- Loads KB patterns from
data-qualityanddbtdomains - Generates:
- Great Expectations suite with expectations
- dbt schema YAML with tests
- Custom data quality SQL assertions
- Freshness and completeness checks
Agent Delegation
| Agent | Role |
|---|---|
data-quality-analyst |
Primary — GE suites, quality rules, observability |
dbt-specialist |
Escalation — when tests need dbt YAML format |
data-contracts-engineer |
Escalation — when SLAs need formal contracts |
KB Domains Used
data-quality— Great Expectations, Soda, observability patternsdbt— dbt tests, schema YAML, custom generic testsdata-modeling— constraint patterns, referential integrity
Output
The agent generates test definitions in your preferred format (GE, dbt, SQL) with severity classification.
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.
- 2d ago First seen · 54 lines · 16 tokens per session scan A be50eaed58b4
data-quality is a command published in the GitHub repository luanmorenommaciel/agentspec (244 stars, last pushed 3d ago), licensed MIT. It adds 16 tokens to every session and 330 once invoked, about $0.0001 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 2026-09-01.
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