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 formulating-biological-findingsgit 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/formulating-biological-findings)<a href="https://agentmods.dev/skills/mannlabs/proteomics-agent-skills/formulating-biological-findings"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/formulating-biological-findings/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/formulating-biological-findings"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/formulating-biological-findings.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 | $0.00030 | $0.00685 |
| Opus 5.5 | $0.00012 | $0.00274 |
| Sonnet 5.5 | $0.00006 | $0.00137 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
formulating-biological-findings 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Formulating Biological Findings from Proteomics Data
This skill guides the transition from the analysis of differentially expressed proteins (upregulated and downregulated) to the synthesis of findings that highlight protein interactions, pathway cross-talk, or potential roles in disease.
Use When
Use when all preprocessing steps (including data reading, QC, preprocessing, statistical testing) are complete, and only the biological/clinical interpretation remains.
Workflow
Checklist
Copy this checklist to track your progress:
finding Generation Progress:
- [ ] Step 1: Functional Analysis
- [ ] Step 2: Ontology Analysis
- [ ] Step 3: Critical Review
- [ ] Step 4: Biological Context
- [ ] Step 5: Focused Analysis & Prioritization
- [ ] Step 6: Deeper Analysis
- [ ] Step 7: Documentation
Detailed Instructions
Step 1: Functional Analysis
- Identify relationships between differentially expressed proteins (DEPs) using your broad biological knowledge and functional protein annotations (e.g. in uniprot)
- Investigate whether differentially expressed proteins are functionally related, e.g. via biochemical pathways or gene sets, or protein complexes.
Step 2: Ontology Analysis
- Interpret enrichment results (e.g., GSEA/ORA) if available
- Examine which cellular processes are most affected based on protein changes
- Identify shared proteins between pathways (leading edges) as potential regulatory hubs
Step 3: Critical Review
- Review all collected information
- Flag contradictions between different analyses
- Identify repeating patterns across analyses
Step 4: Biological Context
- Consider the experimental design and research question for interpretation
- Explain implications of changes across biological scales (organelles, cells, organs, organism)
- Assess how these changes might affect overall biological function
Step 5: Focused Analysis & Prioritization
- Recommend 3–5 key aspects for further investigation (e.g., specific ontology terms, mechanisms, or homeostatic pathways)
- Explain the scientific rationale for each recommendation
- Select the most promising aspect for deep-dive analysis in the next step
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 · 86 lines · 30 tokens per session scan A 7b4d2d8bf6af
formulating-biological-findings is a skill published in the GitHub repository MannLabs/proteomics-agent-skills (16 stars, last pushed 4d ago), licensed Apache-2.0. It adds 30 tokens to every session and 685 once invoked, about $0.0001 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…