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 reading_proteomics_datagit 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/reading_proteomics_data)<a href="https://agentmods.dev/skills/mannlabs/proteomics-agent-skills/reading_proteomics_data"><img src="https://agentmods.dev/badge/skills/mannlabs/proteomics-agent-skills/reading_proteomics_data.svg" alt="Measured on agentmods" 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 | $0.00061 | $0.00957 |
| Opus 5 | $0.00030 | $0.00478 |
| Sonnet 5 | $0.00012 | $0.00191 |
| Haiku 4.5 | $0.00006 | $0.00096 |
Grade A, and why
reading-proteomics-data 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 8d 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reading Proteomics Search Engine Outputs
1. Context
Table Types
| Type | Format | Feature level | Use When |
|---|---|---|---|
| Peptide spectrum match (PSM) table | Long (one row per match) | Precursor, Peptides (optional), Proteins, Genes (optional) | Peptide-level analysis, PTMs, custom aggregation |
| Protein Group (PG) matrix | Wide (proteins × samples) | Proteins, Genes (Optional) | Protein-level analysis |
Use PG matrices for protein- and gene-level analyses, if available
Engine Detection Signatures
Column Mapping References contain the mapping of search-engine columns to standardized columns. Multiple column names might map to the same standardized column name, depending on the search engine version
- references/psm-columns.md — PSM table mappings
- references/pg-columns.md — PG matrix mappings
| Engine | Key Columns | Typical Files |
|---|---|---|
| DIA-NN | Precursor.Id, Protein.Group, Run |
pg_matrix.tsv, report.tsv |
| MaxQuant | Raw file, Protein IDs |
proteinGroups.txt, evidence.txt |
| Spectronaut | PG.ProteinGroups, R.FileName |
*_Report.tsv |
| AlphaDIA | pg, precursor.idx, run |
pg_matrix.tsv |
| Sage | filename, stripped_peptide, sage_discriminant_score |
results.sage.tsv |
| MSFragger | Protein ID, Spectral Count |
combined_protein.tsv, psm.tsv |
| AlphaPept | Unnamed: 0, _LFQ suffix |
results.hdf |
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.
- 8d ago First seen · 66 lines · 61 tokens per session scan A 9178af160205
reading-proteomics-data is a skill published in the GitHub repository MannLabs/proteomics-agent-skills (14 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 61 tokens to every session and 957 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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