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 Growth-Today/claude-skills --skill gt-data-auditorgit clone --depth 1 https://github.com/Growth-Today/claude-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/growth-today/claude-skills/gt-data-auditor)<a href="https://agentmods.dev/skills/growth-today/claude-skills/gt-data-auditor"><img src="https://agentmods.dev/badge/skills/growth-today/claude-skills/gt-data-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/growth-today/claude-skills/gt-data-auditor"><img src="https://agentmods.dev/badge/skills/growth-today/claude-skills/gt-data-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 | $0.00236 | $0.05997 |
| Opus 5.5 | $0.00094 | $0.02399 |
| Sonnet 5.5 | $0.00047 | $0.01199 |
| Haiku 4.5 | $0.00024 | $0.00600 |
Grade A, and why
gt-data-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 22d 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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GT Data Audit
A 10-dimension audit framework for B2B contact data quality. Produces a 0 to 10 score across coverage, accuracy, freshness, cost, governance, and 5 other dimensions. Built for RevOps leaders, GTM Engineers, and Sales Operations at B2B SaaS past $10M ARR.
The full audit takes 3 to 5 hours of hands-on work for the person running it. The skill covers all 10 dimensions in order, scores each one against a pass benchmark, and returns a single number plus a prioritized fix list.
Two operating modes
Client-facing mode (default) For lead magnet downloads, prospect deliverables, and external sharing. Anyone can run this audit on their own stack. Voice is operator-real and jargon-free with no GT-internal references. Output formats: Notion page, 1-page PDF, scorecard.
GT-internal mode For the GT team running an audit on a live client portal. Adds internal context: client tier, engagement type, link to the GTM Alpha Playbook, deliverable templates, and a recommended next-step engagement based on the score.
Default to client-facing mode. Switch to GT-internal mode ONLY when the prompt contains an explicit marker:
- "for [client name]"
- "Velocity engagement" / "GTM Alpha engagement" / "GTM Alpha"
- "before GTM Alpha delivery"
- "internal audit" / "team mode"
If it is unclear which mode applies, ask once: "Is this for external sharing as a lead magnet, or internal use by the GT team running an audit on a client?"
When to fire
Fire when the user's message contains any of these intents:
- "audit our data" / "data quality check" / "data audit"
- "is our data the bottleneck" / "data layer for AI agents" / "AI agent data foundation"
- "email accuracy benchmark" / "what is a good bounce rate" / "verified email rate"
- "cost per usable contact" / "cost per credit" / "data provider pricing"
- "mobile connect rate" / "phone data accuracy" / "cold call connect benchmark"
- "data refresh cycle" / "how often refresh data" / "data decay"
- "CRM data audit" / "CRM hygiene" / "data governance for GTM"
- "validate our data provider" / "is Apollo enough" / "ZoomInfo vs Cognism"
- "waterfall enrichment vs single source" / "AI routing for enrichment"
- "RevOps audit" / "data scorecard" / "data foundation"
- "BetterContact audit" / "BetterContact partnership"
What ships with it
8 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.
- 22d ago First seen · 393 lines · 236 tokens per session scan A 0b60c3a81ce5
gt-data-auditor is a skill published in the GitHub repository Growth-Today/claude-skills (6 stars, last pushed 2d ago), licensed MIT. It adds 236 tokens to every session and 5,997 once invoked, about $0.0009 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 2026-09-24.
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