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 agentmods add skills/tiangzlab/omicsclaw/proteomics-denpx skills add TianGzlab/OmicsClaw --skill proteomics-degit 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/proteomics-de)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/proteomics-de"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/proteomics-de.svg" alt="Measured on agentmods" 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 | $0.00079 | $0.01375 |
| Opus 5 | $0.00039 | $0.00687 |
| Sonnet 5 | $0.00016 | $0.00275 |
| Haiku 4.5 | $0.00008 | $0.00137 |
Grade A, and why
proteomics-de 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 yesterday.
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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
proteomics-de
When to use
The user has a wide protein × sample CSV (rows = proteins as index, columns = samples) and wants two-group differential abundance. Three backends:
ttest(default) — Student's two-sample t-test (equal variance).welch— Welch's t-test (unequal variance).mann_whitney— non-parametric Mann-Whitney U.
All return per-protein log2fc (group2 vs group1), pvalue, and
BH-adjusted padj. --alpha controls significance threshold for
the tables/significant.csv shortlist; --log2fc-threshold
optionally adds an absolute log2FC filter.
For multi-condition DE, run pairwise contrasts manually. For label-based TMT linear-mixed models, use MSstats / limma in R.
Inputs & Outputs
Inputs
- File types:
.csv - Accepts artifact
proteomics.abundance_matrix(csv)
Outputs
tables/differential_abundance.csvtables/significant.csvreport.mdresult.json- Produces artifact
proteomics.differential_resultsastables/differential_abundance.csv(csv)
Flow
- Load CSV with
pd.read_csv(args.input_path, index_col=0)(proteomics_de.py:289); split columns at midpoint — first half = group1, second half = group2 (:290-292). NO CLI flag for prefix/suffix. - Dispatch on
--method(proteomics_de.py:295); per-protein test →log2fc(mean(log2(g2)) − mean(log2(g1))) + rawpvalue. - Apply BH FDR adjustment (
proteomics_de.py:137/:178) →padjcolumn. - Filter
padj < args.alpha(and|log2fc| ≥ args.log2fc_thresholdif > 0) →tables/significant.csv. - Write
tables/differential_abundance.csv(proteomics_de.py:299) +tables/significant.csv(:306) +report.md+result.json(:322).
Gotchas
- Group assignment is by COLUMN POSITION — first half / second half.
proteomics_de.py:290-292splitsdata.columns[:mid]vsdata.columns[mid:]. There is NO CLI flag for control / treatment prefixes; if your CSV columns are interleaved, pre-sort them. Demo usescontrol_1..Nthentreatment_1..N(:204-205). - Index column 0 is treated as the protein ID.
pd.read_csv(args.input_path, index_col=0)(proteomics_de.py:289) is unconditional — make sure your protein-ID column is the FIRST column in the CSV. - Unknown
--methodraisesValueError.proteomics_de.py:192rejects values outside("ttest", "welch", "mann_whitney")— argparsechoices=enforces this at parse time too. --inputREQUIRED unless--demo.proteomics_de.py:288raisesValueError("--input required").- log2FC direction: group2 minus group1. Positive
log2fcmeans group2 > group1. If your "control" is in the second half of columns, you'll get inverted signs — the script does NOT auto-detect direction. - NaN handling differs per backend.
ttest/welch(proteomics_de.py:116-118) drop rows where either group's mean is non-finite (np.isfinitefilter).mann_whitney(:150-151) additionally drops0values (g1 > 0,g2 > 0) — small placeholder intensities silently disappear from Mann-Whitney runs but stay in t-test runs. Pre-impute zeros if you need consistent behaviour.
What ships with it
5 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.
- yesterday First seen · 103 lines · 79 tokens per session scan A 7cc16bdc5cc4
proteomics-de is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 79 tokens to every session and 1,375 once invoked, about $0.0004 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.
Other skills, from other repositories
bio-agent-skills-hub
Discover and invoke 1,676 deduplicated biomedical AI agent skills from the Awesome Bio Agent Skills repository (20 source repos, 15 categories). Use this skill as a router whenever a user needs a bioinformatics/biomedical task (genomics, transcriptomics, single-cell, proteomics, protein design, clinical, epigenomics…
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
cellxgene-census-query
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, and integrate with scanpy/PyTorch for population-scale single-cell analysis. Use this skill when: (1) Querying single-cell expression data by cell type, tissue, or disease, (2) Exploring available single-cell datasets…
single-cell-scrna-seq-analysis-scanpy
Complete single-cell RNA-seq analysis workflow built on Scanpy and AnnData. Use this skill when: (1) Loading diverse single-cell data formats (10X, h5ad, CSV), (2) Performing quality control and filtering, (3) Normalization, dimensionality reduction, and clustering, (4) Marker gene identification and cell type…
single-cell-multi-omics-analysis-scvi
Probabilistic deep learning framework for single-cell multi-omics data analysis. Use this skill when: (1) Analyzing single-cell RNA-seq data with batch correction, (2) Integrating multi-modal data (CITE-seq, ATAC-seq, multi-omics), (3) Performing cell type annotation with scANVI, (4) Spatial transcriptomics…
data-stats-analysis
Perform statistical tests, hypothesis testing, correlation analysis, and multiple testing corrections using scipy and statsmodels. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).