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 TianGzlab/OmicsClaw --skill lasso-biomarker-panelgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/lasso-biomarker-panel)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/lasso-biomarker-panel"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/lasso-biomarker-panel/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/tiangzlab/omicsclaw/lasso-biomarker-panel"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/lasso-biomarker-panel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00009 | $0.04487 |
| Opus 5 | $0.00005 | $0.02243 |
| Sonnet 5 | $0.00002 | $0.00897 |
| Haiku 4.5 | $0.00001 | $0.00449 |
Grade A, and why
LASSO Biomarker Panel Discovery & Validation 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 — 328 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LASSO Biomarker Panel Discovery & Validation
Select minimal, interpretable biomarker panels from high-dimensional omics data using penalized logistic regression (LASSO/elastic net) with nested cross-validation and stability selection.
When to Use This Skill
Use this skill when you need to:
- Select a minimal biomarker panel (5-15 features) from thousands of candidates
- Build a predictive model for a binary clinical outcome (responder/non-responder, disease/control)
- Validate across cohorts — discovery + independent validation design
- Generate regulatory-grade outputs — ROC/AUC, calibration, decision curves
- Design clinical exploratory endpoints from omics biomarker signatures
Don't use this skill for:
- Unsupervised clustering (use
bulk-omics-clustering) - Differential expression only (use
bulk-rnaseq-counts-to-de-deseq2) - Multi-omics integration/factor discovery (use
multiomics-patient-stratification) - Continuous outcomes — this skill is for binary classification
Installation
options(repos = c(CRAN = "https://cloud.r-project.org"))
if (!require('BiocManager', quietly = TRUE)) install.packages('BiocManager')
# Core (required)
install.packages(c('glmnet', 'pROC', 'ggplot2', 'ggprism', 'ggrepel'))
# Heatmap (required for feature heatmap)
BiocManager::install(c('ComplexHeatmap'))
install.packages('circlize')
# Example data — Sepsis MARS consortium (recommended demo) + breast cancer/UNIFI
BiocManager::install(c('GEOquery', 'Biobase'))
# Example data — IMvigor210 bladder cancer IO (alternative demo)
install.packages('remotes')
remotes::install_github('SiYangming/DESeq', upgrade = 'never')
remotes::install_github('SiYangming/IMvigor210CoreBiologies', upgrade = 'never')
BiocManager::install('DESeq2')
# Optional: DE pre-filtering
BiocManager::install('limma')
# Optional: Biological interpretation (pathway enrichment, cell-type context)
BiocManager::install(c('clusterProfiler', 'org.Hs.eg.db', 'fgsea'))
install.packages('msigdbr')
What ships with it
12 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.
- references/decision-guide.md 2.7 KB
- references/lasso-reference.md 4.0 KB
- references/validation-guide.md 3.8 KB
- scripts/biological_interpretation.R 85 KB
- scripts/biomarker_plots.R 13 KB
- scripts/export_results.R 41 KB
- scripts/lasso_workflow.R 12 KB
- scripts/load_example_data.R 99 KB
- scripts/plotting_helpers.R 2.8 KB
- scripts/prepare_features.R 8.3 KB
- scripts/query_cellxgene.py 3.8 KB runs code
- scripts/validate_external.R 6.7 KB
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 · 328 lines · 9 tokens per session scan A 8bb4ca12ec3e
LASSO Biomarker Panel Discovery & Validation is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 9 tokens to every session and 4,487 once invoked, about $0.0000 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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