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-identificationnpx skills add TianGzlab/OmicsClaw --skill proteomics-identificationgit 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-identification)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/proteomics-identification"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/proteomics-identification.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 | $0.00076 | $0.01122 |
| Opus 5 | $0.00038 | $0.00561 |
| Sonnet 5 | $0.00015 | $0.00224 |
| Haiku 4.5 | $0.00008 | $0.00112 |
Grade A, and why
proteomics-identification 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
proteomics-identification
When to use
The user has a peptide-level CSV (from MaxQuant peptides.txt,
FragPipe combined_peptide.tsv, DIA-NN, or any peptide table with
columns including peptide / protein / optionally score /
charge) and wants identification summary statistics: total PSM
count, unique peptide count, distinct protein count, optional
median score, optional charge distribution.
This skill does NOT run a search engine — it summarises a peptide
table that already exists. The --fdr flag is recorded as
metadata only (no FDR re-thresholding is performed).
Inputs & Outputs
Inputs
- File types:
.csv,.tsv,.txt
Outputs
tables/peptides.csvreport.mdresult.json- Produces artifact
proteomics.peptide_tableastables/peptides.csv(csv)
Flow
- Load CSV (
--input <peptides.csv>) or generate a demo peptide table (--demo). - Filter by FDR via
filter_by_fdr(proteomics_identification.py:105-126) — searches columns in orderqvalue→q-value→q_value→PEP→pep→fdr; if NONE found, logs a warning at:117and passes through unchanged. - Compute n_psms, n_unique_peptides, n_proteins, id_rate; optionally median
score(proteomics_identification.py:147) andchargedistribution (:151). - Write
tables/peptides.csv(proteomics_identification.py:235) +report.md+result.json(:241).
Gotchas
- No search engine is invoked. This skill summarises an existing peptide CSV — it does NOT run MaxQuant / MS-GF+ / Comet / Mascot. Run a search engine upstream and feed the peptide-level CSV here.
--fdrACTIVELY filters when an FDR column is present.proteomics_identification.py:229callsfilter_by_fdr(peptides, fdr_threshold=args.fdr). The helper (:105-126) tries columns in orderqvalue→q-value→q_value→PEP→pep→fdr. With NONE present, the run only logs a warning at:117and passes the input through unchanged.--inputREQUIRED unless--demo.proteomics_identification.py:223raisesValueError("--input required when not using --demo").- Optional columns are silently skipped when absent. A CSV without
scoreomitssummary["median_score"]; withoutchargeomitssummary["charge_distribution"]. Inspect the JSON before writing downstream consumers that assume those keys exist. - Column names must match exactly (lowercase):
peptide,protein,score,charge. MaxQuantevidence.txtships withSequence/Proteins/Score/Charge— rename to lowercase first (e.g.df.rename(columns={"Sequence": "peptide", "Proteins": "protein", "Score": "score", "Charge": "charge"})).
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 · 87 lines · 76 tokens per session scan A b273ecd1ba23
proteomics-identification is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 76 tokens to every session and 1,122 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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