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-ptmnpx skills add TianGzlab/OmicsClaw --skill proteomics-ptmgit 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-ptm)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/proteomics-ptm"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/proteomics-ptm.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 | $0.00095 | $0.01348 |
| Opus 5 | $0.00048 | $0.00674 |
| Sonnet 5 | $0.00019 | $0.00270 |
| Haiku 4.5 | $0.00010 | $0.00135 |
Grade A, and why
proteomics-ptm 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 2d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
proteomics-ptm
When to use
The user has a PTM-site CSV (columns include protein and
ptm_type, optionally localization_probability, amino_acid)
and wants per-PTM summary: site-class assignment using
Olsen et al. (2006) thresholds (Class I ≥ --loc-threshold,
Class II ≥ 0.50, Class III < 0.50, Unknown if no probability),
per-PTM-type counts, amino-acid distribution, sites-per-protein.
--loc-threshold controls the Class I cutoff (default 0.75).
For protein-level abundance (no PTM split) use
proteomics-quantification. For DE between conditions use
proteomics-de.
Inputs & Outputs
Inputs
- File types:
.csv
Outputs
tables/ptm_class_I_sites.csvtables/ptm_sites.csvreport.mdresult.json
Flow
- Load CSV (
--input <ptm_sites.csv>) or generate a demo (--demo). - Validate required columns
protein,ptm_type(proteomics_ptm.py:148-152raisesValueError("Missing required column: '{col}'")). - If
localization_probabilitycolumn exists, classify each site (proteomics_ptm.py:154-161):- Class I: prob ≥
--loc-threshold(default 0.75) - Class II: prob ≥ 0.50
- Class III: < 0.50 (default branch)
- If column missing →
Unknown
- Class I: prob ≥
- Aggregate per-PTM-type counts (
:166), amino-acid distribution (:171, optional), sites-per-protein (:177). - Write
tables/ptm_sites.csv(proteomics_ptm.py:292) +tables/ptm_class_I_sites.csv(:297) +report.md+result.json.
Gotchas
- Required CSV columns are LOWERCASE:
protein,ptm_type.proteomics_ptm.py:149-152raisesValueError("Missing required column: '{col}'")on first missing column. MaxQuantPhospho (STY)Sites.txtusesProteins/Modification; rename to lowercaseprotein/ptm_typefirst. - Without
localization_probability, EVERY site isUnknown.proteomics_ptm.py:163falls back todf["site_class"] = "Unknown". Thetables/ptm_class_I_sites.csvoutput will then be empty (no Class I sites). For unprocessed search-engine output that lacks the localization-probability column, run a localization tool (e.g. PhosphoRS / Andromeda) upstream. --inputREQUIRED unless--demo.proteomics_ptm.py:284raisesValueError("--input required when not using --demo").- Class II cutoff is HARD-CODED at 0.50. Only
--loc-threshold(Class I cutoff) is configurable. The 0.50 boundary atproteomics_ptm.py:158cannot be tuned via CLI. amino_aciddistribution is optional and key-absent when empty. Without theamino_acidcolumn, the script omitssummary["amino_acid_distribution"]entirely (theif aa_counts:guard atproteomics_ptm.py:204skips the assignment). Downstream consumers should check key presence ("amino_acid_distribution" in summary), not just length. Note the actual key name isamino_acid_distribution— NOTaa_counts.ptm_typevalues are case-sensitive.Phosphoandphosphoare counted as distinct PTM types. Pre-normalise casing if your search engine emits mixed values.
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.
- 2d ago First seen · 99 lines · 95 tokens per session scan A a7343205b6de
proteomics-ptm is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 95 tokens to every session and 1,348 once invoked, about $0.0005 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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