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 zamushwani/biomedical-ai-skills --skill checkpoint-biomarkersgit clone --depth 1 https://github.com/zamushwani/biomedical-ai-skillsWrote 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/zamushwani/biomedical-ai-skills/checkpoint-biomarkers)<a href="https://agentmods.dev/skills/zamushwani/biomedical-ai-skills/checkpoint-biomarkers"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/checkpoint-biomarkers/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/zamushwani/biomedical-ai-skills/checkpoint-biomarkers"><img src="https://agentmods.dev/badge/skills/zamushwani/biomedical-ai-skills/checkpoint-biomarkers.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 | $0.00000 | $0.03252 |
| Opus 5 | $0.00000 | $0.01626 |
| Sonnet 5 | $0.00000 | $0.00650 |
| Haiku 4.5 | $0.00000 | $0.00325 |
Grade A, and why
checkpoint-biomarkers 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 — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Checkpoint Biomarkers
Predictive biomarkers for immune checkpoint blockade: what PD-L1 IHC scores are and why expression data cannot produce them, tumour mutational burden, microsatellite instability, and the expression signatures (IFN-gamma, TIS, TIDE) that are computable from RNA. Written to keep the assay-derived biomarkers and the expression-derived ones apart, because conflating them is the common failure.
When to Use This Skill
Activate when the user requests:
- PD-L1 status, CPS, TPS, or IC/TC scoring
- TMB-high classification for checkpoint inhibitor eligibility
- MSI-high or dMMR determination
- IFN-gamma, Tumour Inflammation Signature, or TIDE scoring
- Predicting or stratifying response to anti-PD-1, anti-PD-L1, or anti-CTLA-4
- Combining several checkpoint biomarkers into one call
Inputs
| Data Type | Form | Produces |
|---|---|---|
| Stained slide | PD-L1 IHC, a named antibody clone | CPS, TPS, IC/TC — only from here |
| Somatic variants | VCF or MAF plus panel definition | TMB |
| Reads or variants | BAM, or MSI marker loci | MSI status |
| Bulk expression | genes x samples, TPM or normalized counts | IFN-gamma, TIS, TIDE |
| Clinical | response, PFS, OS | evaluation, never input to the score |
The Division That Matters
Two families of checkpoint biomarker, and they are not interchangeable.
ASSAY-DERIVED, from a slide or a sequencer
PD-L1 CPS / TPS IHC. Requires counting stained cells.
TMB mutations per megabase. Panel-dependent.
MSI / dMMR marker instability or MMR protein loss.
EXPRESSION-DERIVED, computable from RNA
IFN-gamma signature mean of a small gene set
TIS / GEP 18-gene inflammation signature
TIDE dysfunction and exclusion modelling
Most confusion in this area comes from treating a member of the first group
as though it belonged to the second.
CPS and TPS Cannot Be Computed From Expression
This is the correction worth stating first, because the request arrives
often and sounds reasonable.
TPS = (PD-L1-stained viable TUMOUR cells / total viable tumour cells) x 100
CPS = (PD-L1-stained tumour cells + lymphocytes + macrophages
/ total viable tumour cells) x 100
Both are counts of individual stained cells on an IHC slide, and CPS
requires telling a stained tumour cell apart from a stained lymphocyte or
macrophage. That is a morphological judgement at cellular resolution.
Bulk RNA gives you one CD274 value per sample. It has no cells in it, so it
cannot enumerate them and cannot assign them to a compartment. CD274 mRNA
does correlate with PD-L1 IHC, and reasonably well, but a correlation is not
a score: you cannot recover CPS from it, and a "CPS estimated from
expression" is a number with no regulatory or clinical meaning.
What you CAN honestly do with expression:
- report CD274 (PD-L1) expression as its own continuous variable
- test its association with outcome
- state that it is a correlate of, not a substitute for, IHC
If the analysis requires CPS or TPS, it requires a stained slide scored by a
pathologist. Say so rather than approximating.
What ships with it
4 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.
- 12d ago First seen · 279 lines · 0 tokens per session scan A ae5e0d95d533
checkpoint-biomarkers is a skill published in the GitHub repository zamushwani/biomedical-ai-skills (1 stars, last pushed 13d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,252 tokens. 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 2026-08-31.
Other skills, from other repositories
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estimate-immune-score-analysis
Use this skill to compute ESTIMATE immune-related microenvironment scores from a bulk expression matrix, generate an ESTIMATE score heatmap, and optionally generate group-wise ESTIMATE score boxplots plus significance tables when a sample group file is supplied. Trigger keywords: ESTIMATE, immune score, stromal score…
external-model-validation
Use when validating an existing prognostic risk signature on an external bulk expression cohort with survival outcomes, producing risk scores, Kaplan-Meier curves, risk distribution plots, heatmap, and time-dependent ROC curves. NOT for: model training, feature selection, nomogram construction, calibration analysis…
gsva-analysis-and-visualization
Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression…
hierarchical-clustering-plot
Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment. Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity. NOT for…
immune-pathway-analysis
Run immune pathway GSVA or ssGSEA analysis from a bulk expression matrix, a sample group file, and a local immune Reactome gene-set table, then export differential pathway results and a heatmap for two-group comparison.