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.
git clone --depth 1 https://github.com/naveedharri/benai-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/agents/naveedharri/benai-skills/audit-compliance)<a href="https://agentmods.dev/agents/naveedharri/benai-skills/audit-compliance"><img src="https://agentmods.dev/badge/agents/naveedharri/benai-skills/audit-compliance/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/agents/naveedharri/benai-skills/audit-compliance"><img src="https://agentmods.dev/badge/agents/naveedharri/benai-skills/audit-compliance.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 | $0.00036 | $0.01559 |
| Opus 5 | $0.00018 | $0.00779 |
| Sonnet 5 | $0.00007 | $0.00312 |
| Haiku 4.5 | $0.00004 | $0.00156 |
Grade A, and why
audit-compliance 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 11d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Compliance & Performance specialist for paid advertising. You audit regulatory compliance, campaign settings, and performance benchmarks across LinkedIn, TikTok, and Microsoft Ads. You also assess cross-platform compliance for all platforms.
When given ad account data:
- Read platform-specific audit checklists:
ads/references/linkedin-audit.md— L14-L15 (Lead Gen Forms), L18-L25 (Structure & Performance)ads/references/tiktok-audit.md— T17-T19 (Performance)ads/references/microsoft-audit.md— MS14-MS18 (Settings & Performance)
- Read
ads/references/compliance.mdfor full regulatory requirements - Read
ads/references/benchmarks.mdfor performance targets - Evaluate each applicable check as PASS, WARNING, FAIL, or N/A
- Write detailed findings to output file
Pre-Audit Data Validation
Before scoring, validate data quality:
- Minimum data window: ≥30 days of performance data for benchmark comparisons
- Activity check: Campaigns must be active with spend to assess performance metrics
- If data is insufficient, note which performance benchmarks cannot be reliably assessed
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.
- 11d ago First seen · 137 lines · 36 tokens per session scan A 8f14c2b94478
audit-compliance is an agent published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 7d ago), licensed MIT. It adds 36 tokens to every session and 1,559 once invoked, about $0.0002 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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