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 skills/j4flmao/agent-skills/data-qualitynpx skills add j4flmao/agent-skills --skill data-qualitygit clone --depth 1 https://github.com/j4flmao/agent-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/j4flmao/agent-skills/data-quality)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/data-quality"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/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 5.1 | $0.00142 | $0.04584 |
| Opus 5 | $0.00071 | $0.02292 |
| Sonnet 5 | $0.00028 | $0.00917 |
| Haiku 4.5 | $0.00014 | $0.00458 |
Grade A, and why
data-data-quality scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST -H "Content-type: application/json" \ How it starts
The opening of the file, as written. The whole thing — 550 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Data Quality
Purpose
Build a data quality framework covering quality dimensions (completeness, accuracy, timeliness, consistency, uniqueness, integrity), automated validation tests (Great Expectations expectations suites, data docs, checkpoints; dbt singular, generic, freshness tests), data observability (Soda, Monte Carlo, Elementary), data SLAs with escalation paths, and data contracts.
Agent Protocol
Trigger
Exact user phrases: "data quality", "data validation", "data profiling", "Great Expectations", "dbt tests", "data observability", "data contract", "schema validation", "data quality check", "data testing", "data monitoring", "quality dimensions", "data freshness", "data completeness", "Soda", "Monte Carlo", "data SLA", "data integrity".
Input Context
Before activating, verify:
- Data stack (warehouse, lake, streaming platform)
- Transformation tool (dbt, Spark, custom SQL)
- Data sources and producers (internal, external, partner)
- Existing monitoring and alerting infrastructure
- Critical data assets for business operations
- Data consumers and their quality SLAs
Output Artifact
Data quality framework with dimension definitions, test configurations, monitoring setup, and contract templates.
Response Format
# Quality dimension definitions
# Great Expectations suite
# dbt test config
# Data contract template
# Alert rules
# SLA definitions
No preamble. No postamble. No explanations. No filler/hedging/transitions. Compress output — why use many token when few do trick.
Completion Criteria
- Quality dimensions defined with measurement approach
- Automated validation suite (Great Expectations + dbt)
- Data profiling established for baseline expectations
- Data observability with monitoring and alerting
- Data contracts between producers and consumers
- Quality SLAs documented with escalation paths
- Soda checks configured for in-storage validation
- Data integrity checks across referential relationships
What ships with it
15 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.
- references/api_reference.md 777 B
- references/architecture_patterns.md 612 B
- references/architecture-patterns.md 673 B
- references/code-organization.md 598 B
- references/configuration_guide.md 665 B
- references/data-quality-automation.md 8.5 KB
- references/data-quality-ecosystem.md 3.9 KB
- references/data-quality-incident-management.md 4.3 KB
- references/data-quality-management.md 3.9 KB
- references/data-quality-metrics.md 4.0 KB
- references/data-quality-monitoring.md 6.5 KB
- references/ge-advanced-patterns.md 3.3 KB
- references/quality-automation.md 4.2 KB
- references/quality-dimensions.md 5.5 KB
- references/soda-check-examples.md 2.0 KB
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 · 550 lines · 142 tokens per session scan A f4b0472ce6b0
data-data-quality is a skill published in the GitHub repository j4flmao/agent-skills (21 stars, last pushed yesterday), licensed MIT. It adds 142 tokens to every session and 4,584 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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golden-dataset
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