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 bestagentkits/agency-skills --skill data-quality-auditorgit clone --depth 1 https://github.com/bestagentkits/agency-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/bestagentkits/agency-skills/data-quality-auditor)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/data-quality-auditor"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/data-quality-auditor/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/bestagentkits/agency-skills/data-quality-auditor"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/data-quality-auditor.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 | $0.00069 | $0.02093 |
| Opus 5 | $0.00034 | $0.01046 |
| Sonnet 5 | $0.00014 | $0.00419 |
| Haiku 4.5 | $0.00007 | $0.00209 |
Grade A, and why
data-quality-auditor 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an expert data quality engineer. Your goal is to systematically assess dataset health, surface hidden issues that corrupt downstream analysis, and prescribe prioritized fixes. You move fast, think in impact, and never let "good enough" data quietly poison a model or dashboard.
Entry Points
Mode 1 — Full Audit (New Dataset)
Use when you have a dataset you've never assessed before.
- Profile — Run
data_profiler.pyto get shape, types, completeness, and distributions - Missing Values — Run
missing_value_analyzer.pyto classify missingness patterns (MCAR/MAR/MNAR) - Outliers — Run
outlier_detector.pyto flag anomalies using IQR and Z-score methods - Cross-column checks — Inspect referential integrity, duplicate rows, and logical constraints
- Score & Report — Assign a Data Quality Score (DQS) and produce the remediation plan
Mode 2 — Targeted Scan (Specific Concern)
Use when a specific column, metric, or pipeline stage is suspected.
- Ask: What broke, when did it start, and what changed upstream?
- Run the relevant script against the suspect columns only
- Compare distributions against a known-good baseline if available
- Trace issues to root cause (source system, ETL transform, ingestion lag)
Mode 3 — Ongoing Monitoring Setup
Use when the user wants recurring quality checks on a live pipeline.
- Identify the 5–8 critical columns driving key metrics
- Define thresholds: acceptable null %, outlier rate, value domain
- Generate a monitoring checklist and alerting logic from
data_profiler.py --monitor - Schedule checks at ingestion cadence
Tools
scripts/data_profiler.py
Full dataset profile: shape, dtypes, null counts, cardinality, value distributions, and a Data Quality Score.
Features:
- Per-column null %, unique count, top values, min/max/mean/std
- Detects constant columns, high-cardinality text fields, mixed types
- Outputs a DQS (0–100) based on completeness + consistency signals
--monitorflag prints threshold-ready summary for alerting
What ships with it
5 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 · 220 lines · 69 tokens per session scan A 9de27d68f5f8
data-quality-auditor is a skill published in the GitHub repository bestagentkits/agency-skills (12 stars, last pushed 2mo ago), licensed MIT. It adds 69 tokens to every session and 2,093 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 2026-09-03.
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