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 | $0.01106 |
| Opus 5 | $0.00039 | $0.00553 |
| Sonnet 5 | $0.00016 | $0.00221 |
| Haiku 4.5 | $0.00008 | $0.00111 |
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 6d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
metabolomics-peak-detection
When to use
The user has a feature × sample intensity matrix (output of
metabolomics-xcms-preprocessing or any feature-level table)
and wants per-sample peaks detected via scipy.signal.find_peaks
with configurable prominence / height / distance. Sample columns
are auto-detected by name (sample* or *intensity*); override
with --sample-prefix <prefix>.
For raw LC-MS preprocessing use metabolomics-xcms-preprocessing.
For normalisation use metabolomics-normalization.
Inputs & Outputs
Inputs
- File types:
.csv
Outputs
tables/detected_peaks.csvreport.mdresult.json
Flow
- Load CSV (
--input <feature_intensity.csv>) or generate a demo atoutput_dir/*.csv(peak_detect.py:281). - Auto-detect sample columns: if
--sample-prefixis set, usec.startswith(prefix)(peak_detect.py:294); else fall back to"intensity" in c.lower() or c.lower().startswith("sample")(:126). - Per sample column, run
scipy.signal.find_peakswithprominence=,height=,distance=. - Write
tables/detected_peaks.csv(peak_detect.py:306) +report.md+result.json.
Gotchas
- Sample-column auto-detection is CASE-INSENSITIVE substring match.
peak_detect.py:126uses"intensity" in c.lower() or c.lower().startswith("sample"). Columns likeIntensity_1,sample_a,SAMPLE.5all match; columns likesignal_1orabundancedo NOT — pre-rename or use--sample-prefix signal. - No sample columns ⇒
ValueError.peak_detect.py:130raisesValueError("...")when neither auto-detection nor--sample-prefixmatches any column. --inputREQUIRED unless--demo.peak_detect.py:285raisesValueError("--input required when not using --demo").--prominencedefault 1e4 is intensity-unit-dependent. Suitable for raw counts at 1e4-1e6 magnitude; for log-transformed data, set--prominence 0.5or smaller. Wrong threshold silently yields zero peaks.--distanceis in INDEX UNITS (sample order), not seconds.peak_detect.py:270notesdefault=5— meaning at-least-5-row separation between adjacent peaks. If your features are RT-sorted, this corresponds to ~5 RT bins; if shuffled, the constraint is meaningless.- NaN values are silently treated as 0 by
find_peaks. Pre-impute or filter NaN rows if they affect detection.
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.
- 6d 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 (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,106 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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