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-diff-expnpx skills add TianGzlab/OmicsClaw --skill proteomics-diff-expgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWhat 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 | $0.00012 | $0.03353 |
| Opus 5 | $0.00006 | $0.01677 |
| Sonnet 5 | $0.00002 | $0.00671 |
| Haiku 4.5 | $0.00001 | $0.00335 |
Grade A, and why
Proteomics Differential Expression (limma + DEqMS) scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proteomics Differential Expression (limma + DEqMS)
Differential protein expression analysis for TMT/LFQ mass spectrometry proteomics data using limma linear models with DEqMS PSM-count-aware variance correction.
When to Use This Skill
Use this skill when you have:
- ✅ Protein quantification data from TMT or LFQ mass spectrometry
- ✅ PSM/peptide counts per protein (for DEqMS variance correction)
- ✅ Biological replicates (≥2 per condition, ≥3 recommended)
- ✅ Need for PSM-aware statistical testing (improved power over standard limma)
Don't use this skill for:
- ❌ RNA-seq data → use bulk-rnaseq-counts-to-de-deseq2
- ❌ Metabolomics data → different normalization/statistics needed
- ❌ Pre-computed fold changes without raw intensities
Quick Start (Example Data)
Test this skill with real TMT proteomics data in ~2 minutes:
source("scripts/load_example_data.R")
data <- load_example_data() # Auto-downloads A431 TMT 10-plex data (~30s)
psm_data <- data$psm_data # 316,726 PSMs × 10 TMT channels
metadata <- data$metadata # 4 conditions: ctrl, miR191, miR372, miR519
# Run complete workflow
source("scripts/basic_workflow.R") # Creates fit_deqms, deqms_results + prints summary
What you get:
- Dataset: A431 human epidermoid carcinoma cells treated with miRNAs (TMT 10-plex)
- Comparison: miR372 vs ctrl (3 vs 3 replicates)
- Expected results: ~9,000 proteins quantified, significant DE proteins at adj.p < 0.05
For your own data: Replace data loading with your protein intensity matrix and metadata (see Inputs section).
Installation
Core packages (required):
options(repos = c(CRAN = "https://cloud.r-project.org"))
if (!require('BiocManager', quietly = TRUE)) install.packages('BiocManager')
BiocManager::install(c('limma', 'DEqMS', 'ExperimentHub'))
Visualization packages (required):
install.packages(c('ggplot2', 'ggprism', 'ggrepel', 'circlize', 'matrixStats'))
BiocManager::install('ComplexHeatmap')
What ships with it
7 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.
- 3d ago First seen · 265 lines · 12 tokens per session scan A 0ea509da05ce
Proteomics Differential Expression (limma + DEqMS) is a skill published in the GitHub repository TianGzlab/OmicsClaw (159 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 12 tokens to every session and 3,353 once invoked, about $0.0001 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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