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 AndyZhuang/Opentest --skill tooluniverse-multiomic-disease-characterizationgit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/tooluniverse-multiomic-disease-characterization)<a href="https://agentmods.dev/skills/andyzhuang/opentest/tooluniverse-multiomic-disease-characterization"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-multiomic-disease-characterization/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/andyzhuang/opentest/tooluniverse-multiomic-disease-characterization"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/tooluniverse-multiomic-disease-characterization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00118 | $0.10785 |
| Opus 5 | $0.00059 | $0.05393 |
| Sonnet 5 | $0.00024 | $0.02157 |
| Haiku 4.5 | $0.00012 | $0.01078 |
Grade A, and why
tooluniverse-multiomic-disease-characterization 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 — 1,139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Omics Disease Characterization Pipeline
Characterize diseases across multiple molecular layers (genomics, transcriptomics, proteomics, pathways) to provide systems-level understanding of disease mechanisms, identify therapeutic opportunities, and discover biomarker candidates.
KEY PRINCIPLES:
- Report-first approach - Create report file FIRST, then populate progressively
- Disease disambiguation FIRST - Resolve all identifiers before omics analysis
- Layer-by-layer analysis - Systematically cover all omics layers
- Cross-layer integration - Identify genes/targets appearing in multiple layers
- Evidence grading - Grade all evidence as T1 (human/clinical) to T4 (computational)
- Tissue context - Emphasize disease-relevant tissues/organs
- Quantitative scoring - Multi-Omics Confidence Score (0-100)
- Druggable focus - Prioritize targets with therapeutic potential
- Biomarker identification - Highlight diagnostic/prognostic markers
- Mechanistic synthesis - Generate testable hypotheses
- Source references - Every statement must cite tool/database
- Completeness checklist - Mandatory section showing analysis coverage
- English-first queries - Always use English terms in tool calls. Respond in user's language
When to Use This Skill
Apply when users:
- Ask about disease mechanisms across omics layers
- Need multi-omics characterization of a disease
- Want to understand disease at the systems biology level
- Ask "What pathways/genes/proteins are involved in [disease]?"
- Need biomarker discovery for a disease
- Want to identify druggable targets from disease profiling
- Ask for integrated genomics + transcriptomics + proteomics analysis
- Need cross-layer concordance analysis
- Ask about disease network biology / hub genes
NOT for (use other skills instead):
- Single gene/target validation -> Use
tooluniverse-drug-target-validation - Drug safety profiling -> Use
tooluniverse-adverse-event-detection - General disease overview -> Use
tooluniverse-disease-research - Variant interpretation -> Use
tooluniverse-variant-interpretation - GWAS-specific analysis -> Use
tooluniverse-gwas-*skills - Pathway-only analysis -> Use
tooluniverse-systems-biology
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 · 1,139 lines · 118 tokens per session scan A 5e563b257871
tooluniverse-multiomic-disease-characterization is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 118 tokens to every session and 10,785 once invoked, about $0.0006 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…