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 vaquarkhan/data-engineering-agent-skills --skill data-quality-and-contract-testinggit clone --depth 1 https://github.com/vaquarkhan/data-engineering-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/vaquarkhan/data-engineering-agent-skills/data-quality-and-contract-testing)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-quality-and-contract-testing"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-quality-and-contract-testing/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/vaquarkhan/data-engineering-agent-skills/data-quality-and-contract-testing"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-quality-and-contract-testing.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 | $0.00036 | $0.00486 |
| Opus 5 | $0.00018 | $0.00243 |
| Sonnet 5 | $0.00007 | $0.00097 |
| Haiku 4.5 | $0.00004 | $0.00049 |
Grade A, and why
data-quality-and-contract-testing 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 9d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Quality And Contract Testing
Overview
Data work is not complete when code runs. It is complete when source assumptions, output contracts, and quality checks prove the behavior is correct.
When to Use
- new source ingestion
- schema changes
- transformation logic updates
- new or changed published tables
- bug fixes involving bad data or broken metrics
Do not use this only as a final cleanup step. It should guide implementation from the start.
Workflow
-
Define the contract before implementation. Capture:
- required fields
- key constraints
- expected types
- allowed null behavior
- freshness expectations
- reconciliation rules
-
Write the validation plan first. Common validations:
- uniqueness
- non-null thresholds
- referential integrity
- accepted values
- row count deltas
- source-to-target totals
-
Reproduce data bugs with a failing check. If an incident or defect exists, write the failing validation or test before changing the pipeline.
-
Implement the smallest change that satisfies the contract.
-
Run the relevant validations and capture evidence. Evidence may include:
- test output
- query results
- sample reconciliation output
- dry-run logs
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "The warehouse query looks right." | Visual inspection does not scale and misses edge cases. |
| "We will add checks after the model stabilizes." | Unchecked pipelines create low-trust data and harder incident response. |
| "A successful job means the data is valid." | Jobs succeed while still producing incorrect or incomplete data. |
Red Flags
- no contract exists for a published dataset
- an incident fix ships without a failing reproduction check
- only happy-path sample data is validated
- freshness or completeness expectations are absent
Verification
- Contracts are written before or alongside implementation
- Relevant checks exist for correctness, completeness, and freshness
- Defects are reproduced with a failing validation before the fix
- Evidence from validation is captured and reviewable
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.
- 9d ago First seen · 75 lines · 36 tokens per session scan A e79e1045bf6a
data-quality-and-contract-testing is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (43 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 486 once invoked, about $0.0002 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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