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-peak-detectiongit 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-peak-detection)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/metabolomics-peak-detection"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/metabolomics-peak-detection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/metabolomics-peak-detection"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/metabolomics-peak-detection.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00078 | $0.00828 |
| Opus 5.5 | $0.00031 | $0.00331 |
| Sonnet 5.5 | $0.00016 | $0.00166 |
| Haiku 4.5 | $0.00008 | $0.00083 |
Grade A, and why
metabolomics-peak-detection 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 17d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
metabolomics-peak-detection
When to use
Detect peaks on tabular sample signals ordered by retention time. This is not raw mzML peak extraction; run XCMS externally for that workflow.
Use from a step
import pandas as pd
from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("metabolomics-peak-detection")
data = read_input('features.csv', reader=pd.read_csv)
result = library.detect_peaks(data)
write_output(result, 'tables/result.csv')
examples/example_step.py runs a seeded synthetic
example through the step runner and writes a table and Figure. Computations
return new DataFrames, leave the input unchanged and expose diagnostics through
run_info(result). Plotting functions write no files.
API
detect_peaks(data, *, sample_cols=None, prominence=10000.0, height=None, distance=5)
Detect per-sample peaks after sorting rows by retention time.
:param data: DataFrame with mz, rt and numeric sample intensities. :param sample_cols: Explicit columns; None uses the CLI intensity/sample name detection. :param prominence: CLI default 10000; adjust to the intensity scale. :param height: CLI default None; optionally require a minimum peak height. :param distance: CLI default 5; minimum separation in sorted row positions, not seconds. :returns: A new peak DataFrame with diagnostics in attrs['run_info']. :raises ValueError: No sample columns match or peak parameters are invalid.
run_info(data, *, keep=True)
Read diagnostics attached to a returned table.
:param data: DataFrame returned by this library. :param keep: Default True; use False in the CLI to remove diagnostics. :returns: An independent dictionary describing the run. :raises ValueError: The table carries no run_info.
peaks_figure(data)
Plot detected peak intensity against retention time.
:param data: Peak table returned by detect_peaks. :returns: A matplotlib Figure. :raises KeyError: Required peak columns are absent.
What ships with it
7 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.
- 17d ago Changed · +10 lines 4b09edda6d51
- 1mo ago First seen · 90 lines · 78 tokens per session scan A 4ac88804166f
metabolomics-peak-detection is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 17d ago), licensed Apache-2.0. It adds 78 tokens to every session and 828 once invoked, about $0.0003 per session on Opus 5.5. 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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