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 PKU-YuanGroup/OpenAI4S --skill bio-clinical-databases-tumor-mutational-burdengit clone --depth 1 https://github.com/PKU-YuanGroup/OpenAI4SWrote 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/pku-yuangroup/openai4s/bio-clinical-databases-tumor-mutational-burden)<a href="https://agentmods.dev/skills/pku-yuangroup/openai4s/bio-clinical-databases-tumor-mutational-burden"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-databases-tumor-mutational-burden/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/pku-yuangroup/openai4s/bio-clinical-databases-tumor-mutational-burden"><img src="https://agentmods.dev/badge/skills/pku-yuangroup/openai4s/bio-clinical-databases-tumor-mutational-burden.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 | $0.00162 | $0.07853 |
| Opus 5 | $0.00081 | $0.03927 |
| Sonnet 5 | $0.00032 | $0.01571 |
| Haiku 4.5 | $0.00016 | $0.00785 |
Grade A, and why
bio-clinical-databases-tumor-mutational-burden 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.
This is a copy
100% identical to bio-clinical-databases-tumor-mutational-burden — 12 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: cyvcf2 0.30+, VEP 111+ (or snpEff 5.2+), pandas 2.2+, numpy 1.26+, LOHHLA 1.0+ (McGranahan 2017), DASH 1.0+ (Pyke 2022). v4.1 (May 2024) gnomAD is current for germline subtraction. Friends of Cancer Research TMB harmonization framework (Vega 2021 Ann Oncol) and ESMO 2024 (Mosele Ann Oncol) define the operational thresholds.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - CLI:
<tool> --version
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. TMB calculation requires VCF with VEP / snpEff / Funcotator consequence annotations; the panel size used as denominator MUST match the assay's actual scored region, NOT the panel's total content.
Tumor Mutational Burden; Calculation, Harmonization, ICI Eligibility
'Calculate TMB from this somatic VCF and apply ICI eligibility cutoff' -> Count nonsynonymous coding variants passing VAF/depth/germline filters; divide by assay scored region in Mb; apply assay-calibrated TMB-H cutoff; integrate with MSI / HLA-LOH / neoantigen quality.
- Python:
cyvcf2.VCF()+ VEP/snpEff consequence parsing + panel-size normalization - CLI:
bcftools viewfiltering + custom counting - HLA-LOH: LOHHLA (McGranahan 2017 Cell) or DASH (Pyke 2022 Nat Commun)
- Neoantigen quality: pVAC-tools, NetMHCpan-4.1, Luksza 2017 fitness model
Regulatory and Trial Landscape
| Event | Year | Threshold | Notes |
|---|---|---|---|
| KEYNOTE-158 + FDA pembrolizumab pan-tumor approval | 2020 | TMB-H >= 10 mut/Mb | FoundationOne CDx companion diagnostic; 10 cohorts |
| Friends of Cancer Research TMB harmonization Phase I (Merino 2020) | 2020 | -- | 11 panels vs WES truth; 3-fold panel-specific differences |
| Friends of Cancer Research Phase II (Vega 2021) | 2021 | Calibration equations | 19 platforms; per-assay calibration to WES-aligned TMB-Mb |
| ESMO 2024 (Mosele Ann Oncol) | 2024 | TMB-H >= 10/Mb retained (tumour-agnostic, ESCAT IB) | Tumour-type limits per McGrail 2021 |
| KEYNOTE-189 (NSCLC + pembrolizumab + chemo) | 2018 | -- | TMB-H did NOT enrich for benefit with chemo backbone |
| POSEIDON / KEYNOTE-021 / KEYNOTE-407 | 2019-2022 | -- | TMB inconsistent with chemo backbones |
| B-F1RST + BFAST Cohort C (bTMB) | 2022 | bTMB >= 16/Mb | BFAST Cohort C FAILED primary endpoint |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 444 lines · 162 tokens per session scan A 5c5b3f5b8dc0
bio-clinical-databases-tumor-mutational-burden is a skill published in the GitHub repository PKU-YuanGroup/OpenAI4S (407 stars, last pushed yesterday), licensed MIT. It adds 162 tokens to every session and 7,853 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bio-clinical-databases-tumor-mutational-burden, differing in 12 lines, and is treated as a copy.
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