Single-cell Pipeline
v1.0.0Generate single-cell RNA-seq analysis code templates for Seurat and Scanpy, supporting QC, clustering, visualization, and downstream analysis. Trigger when u...
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byAIpoch@aipoch-ai
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 (generate Seurat/Scanpy templates) align with included artifacts: a CLI (scripts/main.py), template files (references/scanpy_template.py) and guidance. The declared inputs/outputs and supported methods (batch correction, trajectory, etc.) are consistent with what the code generates.
Instruction Scope
SKILL.md instructs generating templates via scripts/main.py and documents expected input formats and outputs. The instructions do not ask the agent to read unrelated system files, access external endpoints, or exfiltrate secrets. The runtime behavior appears limited to reading bundled reference files and writing output templates/plots to the working directory.
Install Mechanism
No install spec is provided (instruction-only with included code files). Dependencies are listed in requirements files, but there is no remote download or archive extraction performed by the skill itself.
Credentials
The skill requests no environment variables, credentials, or config paths. The listed Python/R packages in requirements are appropriate for scRNA-seq analysis and consistent with the templates' content.
Persistence & Privilege
always is false and the skill does not request elevated or persistent presence. It does not modify other skills or system-wide agent settings; behavior is local file generation.
Assessment
This skill appears coherent for generating single-cell analysis templates, but before using it: (1) review scripts/main.py to confirm output paths and ensure it will not overwrite important local files; (2) run it in an isolated virtualenv/conda environment because dependencies (scanpy, scvi, etc.) are large and may require specific Python/R versions or GPUs for some methods; (3) note that the generated pipelines will read local genomic data files—treat those files as sensitive and run on systems with appropriate data governance; (4) pin dependency versions or inspect the referenced requirements.txt before installing packages; and (5) if you plan to run any generated code (e.g., training scVI), verify computational/resource requirements and that optional third-party tools are trusted. Overall, no credential exposure or network exfiltration is evident.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.