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 interpreting-biological-resultsgit 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/interpreting-biological-results)<a href="https://agentmods.dev/skills/mannlabs/proteomics-agent-skills/interpreting-biological-results"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/interpreting-biological-results/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/interpreting-biological-results"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/interpreting-biological-results.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 | $0.00075 | $0.01792 |
| Opus 5.5 | $0.00030 | $0.00717 |
| Sonnet 5.5 | $0.00015 | $0.00358 |
| Haiku 4.5 | $0.00007 | $0.00179 |
Grade A, and why
interpreting-biological-results 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interpreting biological results from omics analyses
This skill applies when an upstream analysis (differential expression, clustering, PCA, etc.) has generated:
- A list of significant features (a "hit list") OR
- A ranked or scored list of all features (e.g., by log2 fold-change or test statistic) OR
- Clusters or modules of co-expressed/co-regulated features OR
- A specific gene or small set of features they want to understand functionally
Features might be protein names or gene names encoding the observed proteins.
The goal of this analysis is to leverage biological knowledge databases in connection with appropriate statistical methods to find higher-order associations between individual proteins.
Context and Definitions
Method Selection
Choose the appropriate methods based on which information is available
| Input | Example | Recommended primary methods |
| A Set of Features | e.g., a list of significantly differentially expressed proteins below an FDR cutoff | Overrepresentation Analysis, STRING DB search | | A ranked list of features | e.g. genes ranked by their logfoldchanges or t-statistic | Gene Set Enrichment Analysis, GSVAMultiple | | gene lists | e.g., differentially expressed proteins per cluster or cohort | Overrepresentation Analysis per cluster | | Single gene of interest | e.g. a protein that is unknown to the analyst | UniProt, STRING (single query), literature search on PubMed |
Handling Protein Groups
Proteomics search engines often report protein groups (e.g., P12345;Q67890) when peptides map to multiple proteins. Before enrichment analysis:
| Strategy | When to use | Implementation |
| Take first | Default for most analyses; assumes first entry is the most confident identification | Split on ; and keep first ID |
| Explode | When it is important to identify all putatively involved gene sets with high sensitivity | Duplicate row for each protein in group |
| Drop ambiguous | When high confidence is critical | Remove rows with multiple proteins |
What ships with it
2 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 · 143 lines · 75 tokens per session scan A 912b3d5ae001
interpreting-biological-results is a skill published in the GitHub repository MannLabs/proteomics-agent-skills (16 stars, last pushed 4d ago), licensed Apache-2.0. It adds 75 tokens to every session and 1,792 once invoked, about $0.0003 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…