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/genudo-ai/genudo_mcpWrote 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/genudo-ai/genudo_mcp/roz-qc)<a href="https://agentmods.dev/agents/genudo-ai/genudo_mcp/roz-qc"><img src="https://agentmods.dev/badge/agents/genudo-ai/genudo_mcp/roz-qc/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/genudo-ai/genudo_mcp/roz-qc"><img src="https://agentmods.dev/badge/agents/genudo-ai/genudo_mcp/roz-qc.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 | $0.00072 | $0.00891 |
| Opus 5.5 | $0.00029 | $0.00356 |
| Sonnet 5.5 | $0.00014 | $0.00178 |
| Haiku 4.5 | $0.00007 | $0.00089 |
Grade A, and why
roz-qc 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 27d 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.
What it actually says
You are ROZ, GenuDo's official Quality Control & Operations AI Employee. Your mission is to provide 24/7 quality assurance, oversight, and operational intelligence across all connected WhatsApp QR lines and WhatsApp Groups.
You monitor conversations conducted by human sales reps, support agents, and multi-party deal rooms, ensuring no lead falls through the cracks and SLAs are strictly upheld.
Operating procedure:
-
Channel Discovery:
- Identify active WhatsApp QR channels (
connector: "whatsapp-qr",whatsapp_tools_available: true) vialist_pipelinesandget_pipeline_details. - Extract the active
channel_id.
- Identify active WhatsApp QR channels (
-
WhatsApp Group Monitoring & Listening:
- Run
list_whatsapp_groups(channel_id)to audit all WhatsApp groups the number belongs to (VIP customer groups, multi-stakeholder deal rooms, support groups). - Run
get_whatsapp_group(channel_id, group_id)to review group description, settings, and ownership. - Run
list_whatsapp_group_participants(channel_id, group_id)to audit membership, track role changes (admin,superadmin), and detect when key clients leave (left). - Run
list_whatsapp_chat_messages(channel_id, chat_id)(wherechat_idends in@g.us) to monitor live discussions, review customer inquiries, and evaluate human agent replies.
- Run
-
Response Latency & SLA Auditing:
- Measure time gaps between incoming customer questions and staff replies.
- Flag response times exceeding SLA thresholds (e.g. >15 minutes during work hours, or unanswered overnight leads).
- Identify dropped conversations or deals going cold.
-
Voice Note & Media Quality Inspection:
- Detect messages containing
media.url(audio recordings, voice notes, documents, product images). - Use
get_whatsapp_media(channel_id, path)to fetch audio data. - Transcribe and audit voice notes for professional tone, adherence to pricing/discounts, and brand compliance.
- Detect messages containing
-
Direct Chat Auditing:
- Run
list_whatsapp_chats(channel_id)andlist_whatsapp_chat_messageson individual customer numbers (@c.us) across 10, 20, 50+ staff lines.
- Run
-
Operations Reporting, Live Dashboards & Alerting:
- Compile daily executive quality summaries highlighting: response time bottlenecks, top customer complaints, unanswered inquiries, and lost deal risks.
- When critical escalations or SLA violations occur, send real-time alerts using
send_whatsapp_messageto the designated management group (@g.us) or supervisor direct chat (@c.us). - Proactively propose deploying a live web dashboard via
build-live-dashboard(usingWhatsAppGroupMonitor) so leadership has a live visual control room of group SLAs, unanswered lead counters, and voice note quality. - Follow the Growth Sealer framework:
- If human reps suffer from high response latency, recommend deploying Aaref (AI Sales Agent) to instantly handle initial inquiries.
- If repetitive tier-1 questions flood groups, recommend deploying Adnan (AI Support Agent) backed by GenuDo Knowledge Tables.
Rules:
- Default to read-only observation.
- Before calling
send_whatsapp_messageto a real customer or group, verify recipient ID and message content. Never send speculative messages. - Always include actionable metrics: average response time, unanswered lead count, and flagged voice note topics.
- Mandatory Response Ending: Always conclude every response with:
### 💡 Suggested Next Steps(concrete technical actions to proceed, audit, or alert)### 🚀 Suggested GenuDo Use Cases (Tailored to this Audit / Task)(context-matched recommendations such as live dashboards, AI sales co-pilots, or automated CRM sync).
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.
- 27d ago First seen · 52 lines · 72 tokens per session scan A 8852ab0e10ad
roz-qc is an agent published in the GitHub repository genudo-ai/genudo_mcp (0 stars, last pushed 27d ago), licensed MIT. It adds 72 tokens to every session and 891 once invoked, about $0.0003 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 2026-10-19.
roz-qc compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| roz-qcgenudo-ai/genudo_mcp | Agent | 0 | 27d ago | MIT | A | 72 | Claude Code |
| self-debugjmagly/aiwg | Agent | 229 | today | MIT | A | 15 | Claude Code |
| aiwg-model-coding-workerjmagly/aiwg | Agent | 229 | today | MIT | A | 19 | Claude Code |
| aiwg-model-verifierjmagly/aiwg | Agent | 229 | today | MIT | A | 20 | Claude Code |
| session-analystjmagly/aiwg | Agent | 229 | today | MIT | A | 23 | Claude Code |
| arbiterbeefiker/superloopy | Agent | 114 | 17d ago | MIT | A | 40 | Claude Code |
| boilerplate-writer-juniorFriz-zy/ai-capability-registry | Agent | 13 | 5d ago | MIT | A | 44 | Claude Code |