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 TianGzlab/OmicsClaw --skill metabolomics-quantificationgit 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/metabolomics-quantification)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/metabolomics-quantification"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/metabolomics-quantification.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 3 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00068 | $0.01177 |
| Opus 5 | $0.00034 | $0.00589 |
| Sonnet 5 | $0.00014 | $0.00235 |
| Haiku 4.5 | $0.00007 | $0.00118 |
Grade A, and why
metabolomics-quantification 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 4d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
metabolomics-quantification
When to use
The user has a feature × sample metabolomics intensity table and
wants missing-value imputation followed by normalisation, in a
single pass. Imputation: min (1/2 of column min), median
(per-column median), knn (sklearn KNNImputer). Normalisation:
tic (Total Ion Current per sample), median (per-sample
median), log (log2(x+1)).
Sample columns are auto-detected by name prefix sample /
intensity. For just normalisation use metabolomics-normalization;
for raw LC-MS use metabolomics-xcms-preprocessing.
Inputs & Outputs
Inputs
- File types:
.csv
Outputs
tables/quantified_features.csvreport.mdresult.json
Flow
- Load CSV (
--input <features.csv>) or generate a demo (--demo). - Auto-detect sample columns via
c.startswith("sample") or c.startswith("intensity")(met_quantify.py:72-84); raiseValueError("Could not auto-detect sample columns in the input file.")at:174if none found. - Impute missing values per
--impute(min/median/knn); reject unknown method at:187withValueError("Unknown impute method: ..."). - Normalise per
--normalize(tic/median/log); reject unknown method at:193. - Write
tables/quantified_features.csv(met_quantify.py:294) +report.md+result.json.
Gotchas
- Sample-column auto-detection is case-SENSITIVE prefix match.
met_quantify.py:74-84usesc.startswith("sample") or c.startswith("intensity").Sample_1(capital S) does NOT match — pre-rename to lowercase or usemetabolomics-peak-detection's--sample-prefix(no equivalent flag here). - No sample columns ⇒
ValueError.met_quantify.py:174raisesValueError("Could not auto-detect sample columns in the input file.")after both detection passes fail. --inputREQUIRED unless--demo.met_quantify.py:286raisesValueError("--input required when not using --demo").knnimputation requires sklearn. Available by default in OmicsClaw env. Imputes usingKNNImputer(n_neighbors=5).lognormalisation islog2(x+1). Zero → 0 (preserves zeros); negative values raise (silently propagate NaN). Pre-clip negatives upstream.- Imputation runs BEFORE normalisation. This means
minimputation uses unnormalised column min — re-running with a different--normalizedoes NOT change imputed-cell values. To get norm-aware imputation, runmetabolomics-normalizationstandalone first, then use--impute medianhere on already-normalised data.
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.
- 4d ago First seen · 98 lines · 68 tokens per session scan A a766097c8b0e
metabolomics-quantification is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 68 tokens to every session and 1,177 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 2026-09-03.
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