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 using-proteomics-skillsgit 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/using-proteomics-skills)<a href="https://agentmods.dev/skills/mannlabs/proteomics-agent-skills/using-proteomics-skills"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/using-proteomics-skills/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/using-proteomics-skills"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/using-proteomics-skills.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 | $0.00069 | $0.02130 |
| Opus 5.5 | $0.00028 | $0.00852 |
| Sonnet 5.5 | $0.00014 | $0.00426 |
| Haiku 4.5 | $0.00007 | $0.00213 |
Grade A, and why
using-proteomics-skills 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 — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using Proteomics Skills
This is the meta-skill that governs how all other proteomics skills are discovered, sequenced, and applied. Each specialized skill encodes the process an expert proteomics analyst would follow for one phase of work. This skill maps an incoming task to the right phase, routes to the corresponding skill, and keeps the overall analysis on track.
When a proteomics task arrives, identify the current phase and apply the corresponding skill. Do not improvise a phase that a dedicated skill already covers.
Skill Discovery
Route the task using the decision logic below:
- Don't know what the data is or which engine produced it yet? →
reading-proteomics-data - Have a matrix but unsure if samples/features are trustworthy? →
performing-proteomics-quality-control - Need to make samples comparable / stabilize variance? →
normalizing-proteomics-data - Missing values blocking a method that needs a complete matrix? →
imputing-proteomics-data - Suspect plate, instrument, or time-point bias? →
correcting-proteomics-batch-effects - Ready to test which proteins change between conditions? →
performing-statistical-analysis - Have a list of regulated proteins and need biological meaning? →
interpreting-biological-results - Need to synthesize results into a narrative or hypothesis? →
formulating-biological-findings - Writing or finalizing any analysis code? →
applying-code-standards(cross-cutting) - Implementing any of the above in Python? →
analyzing-proteomics-data(usealphapepttools, cross-cutting)
If the request spans multiple phases (e.g. "run the full analysis"), follow the Lifecycle Sequence below in order.
Core Operating Behaviors
These behaviors apply at all times, across all skills. They are non-negotiable.
1. Surface Assumptions
Before implementing anything non-trivial, explicitly state your assumptions:
ASSUMPTIONS I'M MAKING:
1. [assumption about inputs — e.g. search engine, table type, intensity type]
2. [assumption about parameters or thresholds — e.g. completeness cutoff, FDR, fold-change]
3. [assumption about experimental design — e.g. groups, replicates, control vs. treatment]
→ Correct me now or I'll proceed with these.
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 · 167 lines · 69 tokens per session scan A ee0f4ea14b27
using-proteomics-skills is a skill published in the GitHub repository MannLabs/proteomics-agent-skills (16 stars, last pushed 4d ago), licensed Apache-2.0. It adds 69 tokens to every session and 2,130 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…