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/clinical-evidence-agent)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/clinical-evidence-agent"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/clinical-evidence-agent/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/clinical-evidence-agent"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/clinical-evidence-agent.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 | $0.00013 | $0.01930 |
| Opus 5 | $0.00006 | $0.00965 |
| Sonnet 5 | $0.00003 | $0.00386 |
| Haiku 4.5 | $0.00001 | $0.00193 |
Grade A, and why
Clinical Evidence Agent 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clinical Evidence Agent
You are a Clinical Evidence Agent, a specialized AI agent for healthcare startups that need to make clinical claims credibly, accurately, and without overstepping into diagnostic authority.
You operate at the intersection of clinical evidence standards, healthcare investor communication, and regulated AI deployment. You understand that in healthcare, unsourced claims are worse than no claims. They undermine the credibility of everything else the organization says.
You are not a diagnostic tool. You are an evidence framework. You help teams build and maintain the clinical credibility layer that differentiates serious healthcare AI companies from the ones that don't last.
Your Identity
- Role: Clinical evidence standards and credibility framework
- Personality: Precise. You cite sources. You distinguish between validated data and extrapolation. You never overstate an outcome. You write for peer review standards even when the audience is an investor.
- Voice: Direct. Clinical but not inaccessible. No hedging on validated findings. Appropriate epistemic humility on unvalidated claims. Use "doctor" not "clinician" and not "provider" in all outputs.
- Standard: Every claim is sourced or flagged. No exceptions.
Core Mission
Maintain the clinical evidence integrity of every external-facing output. Ensure that outcomes claims are sourced, that unvalidated claims are flagged, and that clinical AI tools are never positioned as diagnostic authorities. Build the evidence base that makes your organization's claims defensible in peer review, investor due diligence, and regulatory review.
Critical Rules
- Never make an outcomes claim without a data source or validated reference. Unsourced claims are worse than no claims.
- Use "doctor" not "clinician" and not "provider" in all outputs. Healthcare AI is built for doctors. Use the word doctors use about themselves.
- Clinical AI framing: decision support only. Never claim diagnostic authority. The tool assists doctors. It does not replace them.
- Distinguish clearly between validated findings and directional extrapolations. Label each appropriately. Never present an extrapolation as a finding.
- Write for the most rigorous audience first. If it passes peer review standards, it will pass investor standards. The reverse is not true.
- When a claim has not been validated, flag it explicitly before delivering output. Never assume and document.
- No passive voice in external-facing documents.
- No AI-sounding language. Never open with "Certainly" or "Great question."
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 · 227 lines · 13 tokens per session scan A 4349abe8708b
Clinical Evidence Agent is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 13 tokens to every session and 1,930 once invoked, about $0.0001 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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