Differential Gene Expression Analysis (RNA-seq)
v1.0.1Performs differential gene expression analysis on RNA-seq count data using DESeq2 and outputs significant genes, volcano plot, PCA, and heatmap.
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (DE analysis with DESeq2) matches the actions in SKILL.md: installing DESeq2, plotting, and writing result files. Required resources (R packages) are appropriate. Minor inconsistency: skill.json claims an entryFile main.R and runtime R-4.2, but no main.R is present and the runnable R code lives in SKILL.md; this is likely bookkeeping/packaging sloppiness rather than suspicious behavior.
Instruction Scope
The SKILL.md contains full R code that installs BiocManager and Bioconductor/CRAN packages, reads input/count_matrix.csv, performs DESeq2 analysis with a hard-coded group vector (3 Control, 3 Treat), and writes output images/CSV. Instructions do not read unrelated files or environment variables and do not transmit data externally. Note: installing R packages requires network access to CRAN/Bioconductor and will fetch and run package installation scripts — normal but worth noting.
Install Mechanism
No platform install spec; runtime R code calls install.packages() and BiocManager::install() to pull DESeq2, ggplot2, pheatmap from standard repositories (CRAN/Bioconductor). These are standard/public package sources, not arbitrary URLs or archive downloads.
Credentials
The skill requests no environment variables, no credentials, and accesses only the declared input path input/count_matrix.csv and writes to output/. The requested resources are proportional to the stated purpose.
Persistence & Privilege
always is false and the skill does not request persistent system-wide privileges or modify other skills/config. It runs ad-hoc R code at runtime only.
Assessment
This skill appears to do exactly what it says: it runs R/DESeq2 on an input count matrix and writes plots and a CSV. Before running, ensure: (1) you provide a correctly formatted input/count_matrix.csv (the code expects six samples arranged as 3 Control then 3 Treat), (2) you run it in a sandbox or environment where installing R/Bioconductor packages is safe (installing packages downloads and executes package installation code), and (3) you accept that the skill will install packages at runtime (network access and write permissions required). Also note the packaging mismatch (skill.json references main.R which is not present); consider asking the publisher for a proper entry file or confirm that executing the code embedded in SKILL.md is the intended workflow.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.