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 agentmods add skills/tiangzlab/omicsclaw/proteomics-data-importnpx skills add TianGzlab/OmicsClaw --skill proteomics-data-importgit clone --depth 1 https://github.com/TianGzlab/OmicsClawWrote 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/tiangzlab/omicsclaw/proteomics-data-import)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/proteomics-data-import"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/proteomics-data-import.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 | $0.00081 | $0.01197 |
| Opus 5 | $0.00041 | $0.00598 |
| Sonnet 5 | $0.00016 | $0.00239 |
| Haiku 4.5 | $0.00008 | $0.00120 |
Grade A, and why
proteomics-data-import 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 yesterday.
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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
proteomics-data-import
When to use
The user has a search-engine output (MaxQuant proteinGroups.txt,
FragPipe combined_protein.tsv, DIA-NN main report, or a generic
CSV / TSV protein table) and wants it normalised into OmicsClaw's
standard schema (lowercase protein_id plus LFQ_<sample> /
Int_<sample> intensity columns derived from MaxQuant's
LFQ intensity ... / Intensity ... headers).
Pick the format with --format {maxquant,fragpipe,diann,generic}
(default maxquant).
For raw MS spectra (mzML / RAW), run a search engine first (MaxQuant / FragPipe / DIA-NN) and feed THIS skill the resulting table.
Inputs & Outputs
Inputs
- File types:
.txt,.tsv,.csv
Outputs
tables/proteins.csvreport.mdresult.json
Flow
- Load input (
--input <file>) or generate a demo MaxQuant-shaped file (--demo). - Dispatch to the format-specific importer (
proteomics_data_import.py:164-174_dispatch_import); supported keys aremaxquant,fragpipe,diann,generic. - Rename columns:
LFQ intensity <sample>→LFQ_<sample>andIntensity <sample>→Int_<sample>(proteomics_data_import.py:85);Majority protein IDs→protein_id;Gene names→gene_name; etc. - Write
tables/proteins.csv(proteomics_data_import.py:284) +report.md+result.json(:299).
Gotchas
--formatvalue must match_dispatch_importkeys exactly.proteomics_data_import.py:166-171registersmaxquant,fragpipe,diann,generic. An unknown value raisesValueError("Unsupported format: ... Supported: ['maxquant', 'fragpipe', 'diann', 'generic']")at:173. There is nospectronautimporter despite the legacy SKILL.md mention — use--format genericfor Spectronaut and rename columns yourself.--inputREQUIRED unless--demo.proteomics_data_import.py:275raisesValueError("--input required when not using --demo"). Non-existent paths raiseFileNotFoundErrorfrompd.read_csv.- Output schema is LOWERCASE. Column renaming targets
protein_id,intensity_<sample>,gene_nameetc. Downstream skills (proteomics-quantification,proteomics-de) assume this casing. Verify after import withhead tables/proteins.csv. - No deduplication of contaminants / decoys. Contaminant (
CON_*) and decoy (REV_*) rows are passed through unchanged. Filter them upstream with the search engine's--keep-contaminants falseflag, or add a downstreamdf = df[~df["protein_id"].str.startswith(("CON_", "REV_"))]step.
What ships with it
5 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.
- yesterday First seen · 99 lines · 81 tokens per session scan A c22d5a635e4b
proteomics-data-import is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 81 tokens to every session and 1,197 once invoked, about $0.0004 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 2026-09-03.
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