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 proteomics-enrichmentgit 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-enrichment)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/proteomics-enrichment"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/proteomics-enrichment/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/proteomics-enrichment"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/proteomics-enrichment.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 | $0.00045 | $0.01012 |
| Opus 5.5 | $0.00018 | $0.00405 |
| Sonnet 5.5 | $0.00009 | $0.00202 |
| Haiku 4.5 | $0.00005 | $0.00101 |
Grade A, and why
proteomics-enrichment 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 20d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
proteomics-enrichment
When to use
ORA uses one-sided Fisher tests and BH correction. Pass an explicit pathway_db and a background_size matching the measured universe. Use existing search-engine tables; this skill does not search raw spectra.
Use from a step
from skills._sdk.notebook import load_skill, write_output
library = load_skill('proteomics-enrichment')
data = library.demo_data(random_state=42)
result = library.enrich(data['protein_id'].tolist(), pathway_db=library.demo_pathways())
write_output(result, 'tables/enrichment_results.csv')
For real data, use read_input and pass any read_table helper as reader=.
The executable examples/example_step.py also checks the result and writes a Figure.
API
enrich(genes: list[str], *, pathway_db: dict | None=None, background_size: int | None=None, method: str='ora') -> pd.DataFrame
Run one-sided Fisher tests with BH correction against an explicit pathway library.
:param genes: Protein or gene identifiers in the same identifier space as pathway_db. :param pathway_db: Required pathway-to-member mapping; no default biological database is assumed. :param background_size: CLI default None uses the input/library union plus at least one background-only member. :param method: CLI default ora is the only implemented method. :returns: Ranked pathway overlaps, odds ratios and adjusted p values. :raises ValueError: The library is absent, the method is invalid, or the background is too small.
run_info(table: pd.DataFrame, *, keep: bool=True) -> dict
Read enrichment diagnostics.
:param table: Output of enrich. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate dictionary with library provenance and summary. :raises TypeError: The input is not a DataFrame.
enrichment_figure(table: pd.DataFrame, *, top_n: int=10)
Plot leading pathways by enrichment ratio.
What ships with it
8 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.
- 20d ago Changed · +29 lines · -25 tokens per session 69498d441bcc
- 1mo ago First seen · 94 lines · 70 tokens per session scan A deb4e65a36b1
proteomics-enrichment is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 20d ago), licensed Apache-2.0. It adds 45 tokens to every session and 1,012 once invoked, about $0.0002 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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