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 nimrodfisher/data-analytics-skills --skill data-quality-auditgit clone --depth 1 https://github.com/nimrodfisher/data-analytics-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/nimrodfisher/data-analytics-skills/data-quality-audit)<a href="https://agentmods.dev/skills/nimrodfisher/data-analytics-skills/data-quality-audit"><img src="https://agentmods.dev/badge/skills/nimrodfisher/data-analytics-skills/data-quality-audit/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/nimrodfisher/data-analytics-skills/data-quality-audit"><img src="https://agentmods.dev/badge/skills/nimrodfisher/data-analytics-skills/data-quality-audit.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 | $0.00050 | $0.00562 |
| Opus 5.5 | $0.00020 | $0.00225 |
| Sonnet 5.5 | $0.00010 | $0.00112 |
| Haiku 4.5 | $0.00005 | $0.00056 |
Grade A, and why
data-quality-audit 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 1mo 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 — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use
- A data pipeline has just loaded new data and needs validation before downstream reports consume it
- A stakeholder has flagged data quality concerns (wrong totals, unexpected nulls, stale data)
- You need to produce a formal data quality scorecard for a data asset as part of a data governance process
- You are onboarding a new data source and need to understand its quality profile before building on it
Process
- Null and completeness audit — run
scripts/null_counter.pyfor a column-by-column null profile. Flag columns above acceptable thresholds for the business context. - Duplicate detection — run
scripts/duplicate_finder.pyto identify full-row and key-level duplicates. Determine if duplicates are intentional (versioning) or errors (pipeline fan-out). - Referential integrity check — run
scripts/referential_integrity.pyto validate that foreign key values in child tables exist in parent tables. Report orphan rate per relationship. - Value range validation — run
scripts/value_range_validator.pywith business rules defined inreferences/business_rule_patterns.md. Flag values outside acceptable ranges. - Freshness check — run
scripts/freshness_check.pyto verify the dataset is up to date — compare the latest record timestamp against the expected lag for this pipeline. - Score and classify findings — map each finding to a quality dimension using
references/quality_dimensions.md. Assign severity (CRITICAL / HIGH / MEDIUM / LOW). - Produce deliverables — fill
assets/audit_report_template.htmlfor a shareable report; fillassets/quality_rubric.mdfor a concise scorecard.
Inputs the skill needs
- Required: dataset (CSV / Parquet / database table reference)
- Required: schema relationships — which columns are primary keys, which are foreign keys to which tables
- Required: business rules — acceptable value ranges, expected value sets, freshness SLA
- Optional: acceptable error rates — at what threshold does a failure become CRITICAL vs. HIGH
- Optional: pipeline schedule — to assess freshness relative to expected update frequency
What ships with it
9 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.
- assets/audit_report_template.html 5.8 KB
- assets/quality_rubric.md 2.0 KB
- references/business_rule_patterns.md 3.9 KB
- references/quality_dimensions.md 4.2 KB
- scripts/duplicate_finder.py 3.6 KB runs code
- scripts/freshness_check.py 3.3 KB runs code
- scripts/null_counter.py 3.1 KB runs code
- scripts/referential_integrity.py 2.8 KB runs code
- scripts/value_range_validator.py 3.9 KB runs code
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.
- 1mo ago First seen · 32 lines · 50 tokens per session scan A 83c4c73726a0
data-quality-audit is a skill published in the GitHub repository nimrodfisher/data-analytics-skills (493 stars, last pushed 28d ago), licensed MIT. It adds 50 tokens to every session and 562 once invoked, about $0.0002 per session on Opus 5.5. 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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