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 hollandkevint/data-product-operator --skill data-quality-assessmentgit clone --depth 1 https://github.com/hollandkevint/data-product-operatorWrote 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/hollandkevint/data-product-operator/data-quality-assessment)<a href="https://agentmods.dev/skills/hollandkevint/data-product-operator/data-quality-assessment"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/data-quality-assessment/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/hollandkevint/data-product-operator/data-quality-assessment"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/data-quality-assessment.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 | $0.00067 | $0.00735 |
| Opus 5 | $0.00034 | $0.00367 |
| Sonnet 5 | $0.00013 | $0.00147 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
data-quality-assessment 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 10d 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Five Quality Dimensions
Score each dimension 1-5 when evaluating any data source or pipeline:
1. Completeness - What percentage of expected records and fields are present?
- Null rate per column
- Missing record detection (expected vs actual row counts)
- Required field coverage
- Score 5: <1% nulls in required fields. Score 1: >20% missing data.
2. Accuracy - Does the data reflect reality?
- Cross-field validation (age matches birth date, totals match line items)
- Reference data matching (codes exist in terminology tables)
- Statistical outlier detection
- Score 5: <0.1% error rate verified against gold standard. Score 1: Known systematic errors unresolved.
3. Timeliness - Is the data fresh enough for its intended use?
- Data freshness (time since last update vs SLA)
- Pipeline latency (ingestion to availability)
- Score 5: Real-time or within SLA. Score 1: Data is days/weeks stale.
4. Consistency - Does the same fact look the same everywhere?
- Format standardization (dates, codes, identifiers)
- Cross-system agreement (same patient, same record across sources)
- Naming convention compliance
- Score 5: Single source of truth, no conflicting definitions. Score 1: "Revenue" means 3 different things.
5. Validity - Does the data conform to business rules?
- Range checks (negative ages, future dates)
- Referential integrity (foreign keys resolve)
- Business rule compliance (diagnosis codes valid for encounter type)
- Score 5: All business rules enforced at ingestion. Score 1: Invalid data flows through unchecked.
Circuit Breaker Protocol
CRITICAL: If data accuracy drops below threshold during development, pause deployment until the data lead investigates. Quality incidents in production destroy trust that takes months to rebuild.
Implement circuit breakers at every pipeline stage:
- Source validation (schema, nulls, ranges)
- Transformation validation (business rules applied correctly)
- Output validation (against reference data or prior period)
- If any stage fails, block promotion to the next stage
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.
- 10d ago First seen · 70 lines · 67 tokens per session scan A 131b96e2b854
data-quality-assessment is a skill published in the GitHub repository hollandkevint/data-product-operator (3 stars, last pushed yesterday), licensed MIT. It adds 67 tokens to every session and 735 once invoked, about $0.0003 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 2026-08-31.
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