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-structuralgit 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-structural)<a href="https://agentmods.dev/skills/tiangzlab/omicsclaw/proteomics-structural"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/proteomics-structural/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-structural"><img src="https://agentmods.dev/badge/skills/tiangzlab/omicsclaw/proteomics-structural.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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.
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00088 | $0.01048 |
| Opus 5.5 | $0.00035 | $0.00419 |
| Sonnet 5.5 | $0.00018 | $0.00210 |
| Haiku 4.5 | $0.00009 | $0.00105 |
Grade A, and why
proteomics-structural 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 — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
proteomics-structural
When to use
The CLI defaults are DSS and fdr_threshold=0.05. DSS/BS3/DSSO/DSBU use 30 angstrom; EDC uses 20 angstrom. 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-structural')
data = library.demo_data(random_state=42)
result = library.analyse_crosslinks(data)
write_output(result, 'tables/crosslinks.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
analyse_crosslinks(data: pd.DataFrame, *, fdr_threshold: float=0.05, crosslinker: str='DSS') -> pd.DataFrame
Return filtered crosslinks with protein-pair and optional distance classifications.
:param data: Crosslink rows with protein_a and protein_b, optional fdr and distance_angstrom. :param fdr_threshold: CLI default 0.05; filters only when an fdr column is present. :param crosslinker: CLI default DSS; BS3, DSSO and DSBU use 30 A, EDC uses 20 A. :returns: New crosslink table; absent distance evidence remains unchecked in run_info. :raises ValueError: Protein identifiers, crosslinker or confidence threshold are invalid.
run_info(table: pd.DataFrame, *, keep: bool=True) -> dict
Read crosslink analysis diagnostics.
:param table: Output of analyse_crosslinks. :param keep: True preserves attrs; False removes diagnostics. :returns: A separate dictionary stating which optional checks ran. :raises TypeError: The input is not a DataFrame.
distance_figure(table: pd.DataFrame)
Plot measured crosslink distances.
:param table: Crosslinks containing distance_angstrom. :returns: A matplotlib Figure without writing files. :raises KeyError: distance_angstrom is absent.
demo_data(*, random_state: int=42) -> pd.DataFrame
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 · +19 lines 4fadcd86402c
- 1mo ago First seen · 106 lines · 88 tokens per session scan A f5a3d131dfc8
proteomics-structural is a skill published in the GitHub repository TianGzlab/OmicsClaw (161 stars, last pushed 20d ago), licensed Apache-2.0. It adds 88 tokens to every session and 1,048 once invoked, about $0.0004 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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