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-annotationgit 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-annotation)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/metabolomics-annotation"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/metabolomics-annotation.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 | $0.00080 | $0.01004 |
| Opus 5 | $0.00040 | $0.00502 |
| Sonnet 5 | $0.00016 | $0.00201 |
| Haiku 4.5 | $0.00008 | $0.00100 |
Grade A, and why
metabolomics-annotation 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 8d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
metabolomics-annotation
When to use
The user has a feature table with mz (m/z) values and wants
each feature annotated by m/z match to a metabolite database.
This is demo-only annotation. The reference is an 15-entry
HMDB dictionary (metabolomics_annotation.py:57-74: Glucose,
Lactic acid, Alanine, Glycine, Serine, Proline, Valine, Leucine).
--database {hmdb,kegg,lipidmaps,metlin} is recorded as metadata
but does NOT switch the lookup table.
For real database-scale annotation use SIRIUS / GNPS / MetFrag externally and feed the resulting annotation CSV into a downstream skill.
Inputs & Outputs
Inputs
- File types:
.csv
Outputs
tables/annotations.csvreport.mdresult.json
Flow
- Load CSV (
--input <features.csv>) or generate a demo (--demo). - For each input
mz, search the 15-entry HMDB dictionary (metabolomics_annotation.py:57-74) within--ppmtolerance. - Write
tables/annotations.csv(metabolomics_annotation.py:279) +report.md+result.json.
Gotchas
- Database is HARD-CODED 8 metabolites —
--databaseis metadata only.metabolomics_annotation.py:57-74defines an 15-entry HMDB tuple. The CLI acceptshmdb/kegg/lipidmaps/metlin(:251choices=...) but the value is only logged intoresult.json— the lookup always uses the same 15-entry HMDB list. For real annotation, use SIRIUS / GNPS / MetFrag externally. --ppm 10.0default is m/z-tolerance. Suitable for high-resolution Orbitrap; for low-resolution Q-TOF use--ppm 30.0. The mass-error formula is|mz_obs - mz_ref| < (ppm × mz_ref / 1e6).--inputREQUIRED unless--demo.metabolomics_annotation.py:269raisesValueError("--input required when not using --demo").- Required CSV column is
mz(lowercase). XCMS exportsmzmed, MZmine exportsm/z; rename tomzfirst. - Multiple matches per feature ⇒ multiple rows. A feature with 3 candidate matches yields 3 rows in
tables/annotations.csv; deduplicate downstream byfeature_idif you need 1:1.
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.
- 8d ago First seen · 85 lines · 80 tokens per session scan A ed6d3cb69fbc
metabolomics-annotation is a skill published in the GitHub repository TianGzlab/OmicsClaw (160 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 1,004 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 2026-08-30.
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