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 krillinai/growth-skills --skill growth-data-quality-auditgit clone --depth 1 https://github.com/krillinai/growth-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/krillinai/growth-skills/growth-data-quality-audit)<a href="https://agentmods.dev/skills/krillinai/growth-skills/growth-data-quality-audit"><img src="https://agentmods.dev/badge/skills/krillinai/growth-skills/growth-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/krillinai/growth-skills/growth-data-quality-audit"><img src="https://agentmods.dev/badge/skills/krillinai/growth-skills/growth-data-quality-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00089 | $0.02919 |
| Opus 5 | $0.00044 | $0.01460 |
| Sonnet 5 | $0.00018 | $0.00584 |
| Haiku 4.5 | $0.00009 | $0.00292 |
Grade A, and why
growth-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 12d 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Data Quality Audit
Turn a vague trust concern into a bounded, reproducible quality decision. Define the expected population and semantics, test supplied evidence, preserve missing and incompatible states, reconcile sources without plugs, trace versions and revisions, and connect defects to actual decisions and controls. Data volume, uptime, a favorable sample, or one composite score cannot certify quality.
Read data-quality-contract.md before accepting the decision, data product, entities, population, sources, fields, systems, versions, evidence, or access state. Read coverage-identity-and-validity.md before testing coverage, completeness, identity, uniqueness, event semantics, validity, timeliness, maturity, models, or exclusions. Read lineage-reconciliation-and-change.md before assigning source authority, reconciling systems, tracing lineage, comparing versions, auditing backfills, or preserving historical revisions. Read controls-governance-market-and-actions.md before rating findings, designing controls, handling incidents, transferring across markets, using personal data, or proposing external actions. Read output-contract.md before delivery. Use playbook-sources.md for the pinned Growth Playbook basis.
Select One Mode
| Mode | Use |
|---|---|
audit |
Inspect supplied datasets, aggregates, dictionaries, samples, logs, queries, exports, or reconciliations for decision-relevant quality defects and unknowns |
control-design |
Specify preventive, detective, reconciliation, monitoring, incident, correction, and review controls for one bounded data product or decision |
refresh |
Version a prior audit after sources, definitions, schemas, pipelines, backfills, incidents, controls, evidence, or downstream uses change |
What ships with it
6 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.
- 12d ago First seen · 107 lines · 89 tokens per session scan A 73f7f3a6cb0c
growth-data-quality-audit is a skill published in the GitHub repository krillinai/growth-skills (43 stars, last pushed 16d ago), licensed MIT. It adds 89 tokens to every session and 2,919 once invoked, about $0.0004 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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