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-proteomics-quality-controlgit 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-proteomics-quality-control)<a href="https://agentmods.dev/skills/mannlabs/proteomics-agent-skills/performing-proteomics-quality-control"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/performing-proteomics-quality-control/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-proteomics-quality-control"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/performing-proteomics-quality-control.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 | $0.00058 | $0.01107 |
| Opus 5.5 | $0.00023 | $0.00443 |
| Sonnet 5.5 | $0.00012 | $0.00221 |
| Haiku 4.5 | $0.00006 | $0.00111 |
Grade A, and why
performing-proteomics-quality-control 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performing Proteomics Quality Control
Goal: Remove outlier samples and unsupported features from protein-level quantification matrices.
Context and Definitions
Key Metrics
- Decoy prefixes:
REV_,DECOY_, or boolean decoy indicator column - Quality Control (QC) for technical replicates: Remove technical replicates when coefficient of Variation (CV) > 20-30% or Pearson Correlation R < 0.9
- Median absolute deviation (MAD)-based outlier detection: An observation x from a set of observations X is flagged as an outlier when
|x - median(X)| > N × MAD(X), where N is a user-defined factor. MAD is calculated as the median of absolute deviations from the median:MAD(X) = median(|X - median(X)|). - Feature Completeness: Feature-wise fraction of samples with non-missing values.
Study Types
| Type | Min features/sample | Completeness | Intensity metric |
|---|---|---|---|
| Single-cell | >500-600 proteins | 10-15% | Total intensity |
| Bulk tissue | No strict minimum | 50-70% | Total intensity |
| Plasma/serum | No strict minimum | 20-50% | Median intensity |
Defaults
| Parameter | Default | Adjust when |
|---|---|---|
| False discovery threshold | 0.01 | Standard value |
| MAD multiplier (N) | 3 | Lower (<3) strict, higher (>3) more permissive |
| Min unique peptides | 2 | >2 for validation studies |
| Feature Completeness | Study-dependent, typically between 10% - 70% | Must find a compromise between robustness and expected biological prevalence. If an effect is expected in a small subset of samples (e.g. a rare cell type) it should not be removed by feature completeness filters |
What ships with it
1 file 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 · 102 lines · 58 tokens per session scan A 041f3fab6e43
performing-proteomics-quality-control is a skill published in the GitHub repository MannLabs/proteomics-agent-skills (16 stars, last pushed 4d ago), licensed Apache-2.0. It adds 58 tokens to every session and 1,107 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…