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 MannLabs/proteomics-agent-skills --skill correcting_proteomics_batch_effectsgit clone --depth 1 https://github.com/MannLabs/proteomics-agent-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/mannlabs/proteomics-agent-skills/correcting_proteomics_batch_effects)<a href="https://agentmods.dev/skills/mannlabs/proteomics-agent-skills/correcting_proteomics_batch_effects"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/correcting_proteomics_batch_effects.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 5.1 | $0.00066 | $0.00711 |
| Opus 5 | $0.00033 | $0.00356 |
| Sonnet 5 | $0.00013 | $0.00142 |
| Haiku 4.5 | $0.00007 | $0.00071 |
Grade A, and why
correcting-proteomics-batch-effects 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 8d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Batch Correction for Proteomics Data
Batch effects are systematic technical biases from sample preparation, instruments, or time points that obscure biological signals. Correction should only be applied on the protein level when diagnostics indicate necessity.
Commonly used batch effect correction algorithms include:
| Priority | Algorithm | Description | When to Use |
|---|---|---|---|
| 1 | ComBat | Empirical Bayes shift+scale model | Default choice, handles small batches well |
| 2 | Median centering | Equalizes batch-wise feature medians | Simple cases, interpretable |
| 3 | Harmony | Iterative mixing in PCA space | Large datasets, many batches |
Workflow
Copy this checklist and track progress:
Analysis step progress:
- [ ] **Assess batch effects**
- [ ] **Prepare dataset**
- [ ] **Apply correction** - Use appropriate algorithm at protein level
- [ ] **Validate results** - Confirm improvement with same metrics
If successful: Proceed with statistical analysis
If unsuccessful: Try another batch effect correction
Workflow
1. Assess Batch Effects
Qualitative: Principal Component Analysis (PCA) Visualization
Compute PCA and color samples by technical factors (e.g. processing plate, instrument, timepoint, processing date). Compare separation by technical vs biological factors. Strong batch effects show samples clustering by technical factors more than biological conditions.
Quantitative Metrics
| Metric | Description | Interpretation |
|---|---|---|
| Principal Component Regression (PCR) | Weighted correlation of batch covariate with PCs | 0 - +1, lower is better |
| Average Silhouette Width | Batch clustering in feature/PCA space | -1 - +1, closer to 0 indicates lower batch effects, closer to 1 for biological factors indicates stronger separation by biology |
| Technical Replicate Correlation | Pairwise feature correlation of replicates across batches | -1 - +1, higher is better |
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.
- 8d ago First seen · 75 lines · 66 tokens per session scan A 79eac87d98b6
correcting-proteomics-batch-effects is a skill published in the GitHub repository MannLabs/proteomics-agent-skills (14 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 66 tokens to every session and 711 once invoked, about $0.0003 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.
Other skills, from other repositories
proteomics-data-import
Load when ingesting a MaxQuant proteinGroups.txt, FragPipe combinedprotein.tsv, DIA-NN report, or generic CSV / TSV protein-quantification table — normalises columns to a standard schema, emits tables/proteins.csv. Skip when raw spectra are the input (run the search engine first); the file is already OmicsClaw schema.
proteomics-de
Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise contrasts manually); label-based TMT linear-mixed models.
proteomics-enrichment
Load when running over-representation analysis (ORA) on a list of proteins via Fisher's exact test against a built-in 8-pathway DEMO dictionary, with BH-FDR correction. Skip when needing a real pathway database (this skill is demo-only) (use bulkrna-enrichment); rank-based GSEA.
proteomics-identification
Load when summarising peptide identifications (PSM count, unique peptide count, distinct protein count, score / charge distributions) from a peptide-level CSV produced by MaxQuant / FragPipe / DIA-NN. Skip when raw spectra are the input (run a search engine first); working with protein-quantification tables (use…
proteomics-ptm
Load when summarising PTM sites (phosphorylation, acetylation, ubiquitination, etc.) from a per-site CSV — site-class assignment (Olsen et al. Class I/II/III by localizationprobability), per-PTM-type counts, amino-acid distribution, sites-per-protein. Skip when raw spectra are the input; you only need protein-level…
proteomics-quantification
Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input is already protein-level (use proteomics-ms-qc); label-based TMT / iTRAQ workflows (search upstream first).