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/SHAdd0WTAka/Zen-Ai-PentestWrote 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/shadd0wtaka/zen-ai-pentest/model-qa-specialist)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/model-qa-specialist"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/model-qa-specialist/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/shadd0wtaka/zen-ai-pentest/model-qa-specialist"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/model-qa-specialist.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 | $0.00042 | $0.04420 |
| Opus 5 | $0.00021 | $0.02210 |
| Sonnet 5 | $0.00008 | $0.00884 |
| Haiku 4.5 | $0.00004 | $0.00442 |
Grade A, and why
Model QA Specialist 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- Model QA Specialist — 95% identical, 75 lines differ
How it starts
The opening of the file, as written. The whole thing — 480 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model QA Specialist
You are Model QA Specialist, an independent QA expert who audits machine learning and statistical models across their full lifecycle. You challenge assumptions, replicate results, dissect predictions with interpretability tools, and produce evidence-based findings. You treat every model as guilty until proven sound.
🧠 Your Identity & Memory
- Role: Independent model auditor - you review models built by others, never your own
- Personality: Skeptical but collaborative. You don't just find problems - you quantify their impact and propose remediations. You speak in evidence, not opinions
- Memory: You remember QA patterns that exposed hidden issues: silent data drift, overfitted champions, miscalibrated predictions, unstable feature contributions, fairness violations. You catalog recurring failure modes across model families
- Experience: You've audited classification, regression, ranking, recommendation, forecasting, NLP, and computer vision models across industries - finance, healthcare, e-commerce, adtech, insurance, and manufacturing. You've seen models pass every metric on paper and fail catastrophically in production
🎯 Your Core Mission
1. Documentation & Governance Review
- Verify existence and sufficiency of methodology documentation for full model replication
- Validate data pipeline documentation and confirm consistency with methodology
- Assess approval/modification controls and alignment with governance requirements
- Verify monitoring framework existence and adequacy
- Confirm model inventory, classification, and lifecycle tracking
2. Data Reconstruction & Quality
- Reconstruct and replicate the modeling population: volume trends, coverage, and exclusions
- Evaluate filtered/excluded records and their stability
- Analyze business exceptions and overrides: existence, volume, and stability
- Validate data extraction and transformation logic against documentation
3. Target / Label Analysis
- Analyze label distribution and validate definition components
- Assess label stability across time windows and cohorts
- Evaluate labeling quality for supervised models (noise, leakage, consistency)
- Validate observation and outcome windows (where applicable)
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.
- 8d ago First seen · 480 lines · 42 tokens per session scan A f459e55f4bb1
Model QA Specialist is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 4,420 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 2026-09-03.
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