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 performing-statistical-analysisgit 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/performing-statistical-analysis)<a href="https://agentmods.dev/skills/mannlabs/proteomics-agent-skills/performing-statistical-analysis"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/performing-statistical-analysis/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/mannlabs/proteomics-agent-skills/performing-statistical-analysis"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/performing-statistical-analysis.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 | $0.00051 | $0.00853 |
| Opus 5.5 | $0.00020 | $0.00341 |
| Sonnet 5.5 | $0.00010 | $0.00171 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
performing-statistical-analysis 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performing Proteomics Statistical Analysis
The goal of Differential Expression Analysis (DEA) is to identify proteins that change significantly between conditions.
Context
Experimental conditions
- Pairwise (2 Groups): Explicitly define "Control" vs. "Treatment"
- Multi-Group (>2 Groups):
- Reference-based: Compare every condition against a universal control (e.g.,
Drug_A vs Ctrl,Drug_B vs Ctrl). - All-vs-All: Compare every permutation (e.g.,
Drug_A vs Drug_B). - Global: Use ANOVA to detect if any change exists across groups.
- Reference-based: Compare every condition against a universal control (e.g.,
Statistical models
- t-test: The default for pairwise comparisons. With small sample sizes (typically n < 5 per group), use moderated t-tests (e.g., limma's eBayes), which borrow variance information across proteins to stabilize estimates.
- ANOVA: Required when comparing more than two groups simultaneously.
- Linear models: Recommended for complex designs. Use standard linear models for factorial (case-control) designs or batch correction; use linear mixed-effects models specifically when you have repeated measures (e.g., paired samples, time-courses) to account for within-subject correlation.
Multiple Testing Correction
Correcting the resulting p-values for multiple finding testing. The standard correction method to control false positives identifications is Benjamini-Hochberg FDR.
2. Workflow Statistical Testing
Copy this checklist and track progress:
Testcase Progress:
- [ ] Step 1: Define the experimental conditions for comparision
- [ ] Step 2: Run statistical model
- [ ] Step 3: Apply Multiple Testing Correction
- [ ] Step 4: Filter for regulated proteins
- [ ] Step 5: Create a Volcano plot
- [ ] Step 6: Create a Heatmap
- [ ] Step 7: Export results
Step 1: Define the experimental conditions for comparision
Check the study context (Experiment.md) and the prompt.
- For 2 Groups: Define group1 (Control) and group2 (Treatment).
- For >2 Groups: Select Strategy A (ANOVA for global differences) or Strategy B (Specific Pairwise Contrasts).
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 · 73 lines · 51 tokens per session scan A b75ada0af095
performing-statistical-analysis is a skill published in the GitHub repository MannLabs/proteomics-agent-skills (16 stars, last pushed 4d ago), licensed Apache-2.0. It adds 51 tokens to every session and 853 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-15.
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 for Fisher over-representation analysis of protein identifiers against caller-supplied pathways. Skip rank-based GSEA (use bulkrna-enrichment) or protein differential testing (use proteomics-de).
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-ms-qc
Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV. Skip when raw mzML / RAW spectra are the input (run a search engine first); peptide-level QC is needed (use proteomics-identification).
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…