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 AndyZhuang/Opentest --skill tooluniverse-immunotherapy-response-predictiongit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/tooluniverse-immunotherapy-response-prediction)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-immunotherapy-response-prediction"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-immunotherapy-response-prediction/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/andyzhuang/opentest/tooluniverse-immunotherapy-response-prediction"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-immunotherapy-response-prediction.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 | $0.00161 | $0.09677 |
| Opus 5 | $0.00081 | $0.04838 |
| Sonnet 5 | $0.00032 | $0.01935 |
| Haiku 4.5 | $0.00016 | $0.00968 |
Grade A, and why
tooluniverse-immunotherapy-response-prediction 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 9d 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 — 866 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Immunotherapy Response Prediction
Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Transforms a patient tumor profile (cancer type + mutations + biomarkers) into a quantitative ICI Response Score with drug-specific recommendations, resistance risk assessment, and monitoring plan.
KEY PRINCIPLES:
- Report-first approach - Create report file FIRST, then populate progressively
- Evidence-graded - Every finding has an evidence tier (T1-T4)
- Quantitative output - ICI Response Score (0-100) with transparent component breakdown
- Cancer-specific - All thresholds and predictions are cancer-type adjusted
- Multi-biomarker - Integrate TMB + MSI + PD-L1 + neoantigen + mutations
- Resistance-aware - Always check for known resistance mutations (STK11, PTEN, JAK1/2, B2M)
- Drug-specific - Recommend specific ICI agents with evidence
- Source-referenced - Every statement cites the tool/database source
- English-first queries - Always use English terms in tool calls
When to Use
Apply when user asks:
- "Will this patient respond to immunotherapy?"
- "Should I give pembrolizumab to this melanoma patient?"
- "Patient has NSCLC with TMB 25, PD-L1 80% - predict ICI response"
- "MSI-high colorectal cancer - which checkpoint inhibitor?"
- "Patient has BRAF V600E melanoma, TMB 15 - immunotherapy or targeted?"
- "Low TMB NSCLC with STK11 mutation - should I try immunotherapy?"
- "Compare pembrolizumab vs nivolumab for this patient profile"
- "What biomarkers predict checkpoint inhibitor response?"
Input Parsing
Required: Cancer type + at least one of: mutation list OR TMB value Optional: PD-L1 expression, MSI status, immune infiltration data, HLA type, prior treatments, intended ICI
Accepted Input Formats
| Format | Example | How to Parse |
|---|---|---|
| Cancer + mutations | "Melanoma, BRAF V600E, TP53 R273H" | cancer=melanoma, mutations=[BRAF V600E, TP53 R273H] |
| Cancer + TMB | "NSCLC, TMB 25 mut/Mb" | cancer=NSCLC, tmb=25 |
| Cancer + full profile | "Melanoma, BRAF V600E, TMB 15, PD-L1 50%, MSS" | cancer=melanoma, mutations=[BRAF V600E], tmb=15, pdl1=50, msi=MSS |
| Cancer + MSI status | "Colorectal cancer, MSI-high" | cancer=CRC, msi=MSI-H |
| Resistance query | "NSCLC, TMB 2, STK11 loss, PD-L1 <1%" | cancer=NSCLC, tmb=2, mutations=[STK11 loss], pdl1=0 |
| ICI selection | "Which ICI for NSCLC PD-L1 90%?" | cancer=NSCLC, pdl1=90, query_type=drug_selection |
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.
- 9d ago First seen · 866 lines · 161 tokens per session scan A ad0512b70152
tooluniverse-immunotherapy-response-prediction is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 161 tokens to every session and 9,677 once invoked, about $0.0008 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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