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 inflexa-ai/inflexa --skill multi-omics-integrationgit clone --depth 1 https://github.com/inflexa-ai/inflexaWrote 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/inflexa-ai/inflexa/multi-omics-integration)<a href="https://agentmods.dev/skills/inflexa-ai/inflexa/multi-omics-integration"><img src="https://agentmods.dev/badge/skills/inflexa-ai/inflexa/multi-omics-integration/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/inflexa-ai/inflexa/multi-omics-integration"><img src="https://agentmods.dev/badge/skills/inflexa-ai/inflexa/multi-omics-integration.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 | $0.00026 | $0.01889 |
| Opus 5.5 | $0.00010 | $0.00756 |
| Sonnet 5.5 | $0.00005 | $0.00378 |
| Haiku 4.5 | $0.00003 | $0.00189 |
Grade A, and why
multi-omics-integration 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 1mo 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Omics Integration
This skill guides method selection and execution for integrating data across multiple omics modalities, including exploratory factor analysis, supervised biomarker discovery, network fusion, and causal/mechanistic modeling.
Method Selection Decision Tree
Choose the method based on your analytical question and data characteristics:
1. Exploratory: What factors drive variation across modalities?
- Use MOFA+ via
muon.tl.mofa()(mofapy2 backend). - Unsupervised factor analysis that decomposes shared and modality-specific variation.
- Handles missing data (samples absent in some modalities).
- Outputs: latent factors, factor loadings per modality, variance explained per factor per modality.
- Inspect factors for biological interpretation; correlate with phenotype metadata.
2. Supervised: Predict outcome from multiple omics
- Use DIABLO (
block.splsda) via mixOmics (R via rpy2). - Sparse PLS-DA variant that performs simultaneous feature selection and classification across modalities.
- Requires a categorical outcome variable (e.g., disease vs. control, responder vs. non-responder).
- Tune
keepX(features per component per modality) via cross-validation. - Outputs: discriminant components, selected features per modality, circos correlation plot.
3. Network-based: Find cross-omics interactions
- Prior knowledge (kinase-substrate, TF-target, ligand-receptor, enzyme-metabolite) must come
from an interaction file resolved from the reference data available to you. The OmniPath
web service is unreachable — egress is blocked, so
omnipath.interactions.*.get()and everydc.op.*()loader fail — but the same content is in the reference inventory as a static export, alongside a separate genome-scale scored PPI. Both are opt-in downloads: resolve what you need up front, and if it does not resolve, report that and scope the analysis to the networks that are available. - Build a custom cross-omics network from those edges + data-driven correlations.
- Analyze with NetworkX or igraph: community detection, centrality, shortest paths.
- Appropriate when you want to model regulatory or signaling relationships between modalities.
What ships with it
4 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.
- 1mo ago First seen · 126 lines · 26 tokens per session scan A 37ed49b6a691
multi-omics-integration is a skill published in the GitHub repository inflexa-ai/inflexa (35 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 1,889 once invoked, about $0.0001 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 2026-09-09.
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