Pseudotime Trajectory Viz

Analyze data with `pseudotime-trajectory-viz` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

This package appears coherent for pseudotime analysis, but because source/homepage are unknown, do the following before trusting it with sensitive data: (1) run the recommended smoke check: python -m py_compile scripts/main.py; (2) inspect scripts/main.py locally (it is included) and run it on a small synthetic or non-sensitive AnnData file to verify behavior and outputs; (3) install dependencies in an isolated virtual environment (venv/conda) to avoid contaminating your system Python; (4) confirm there are no unexpected network calls during execution (e.g., run in an offline or sandboxed environment or monitor network activity); (5) pin dependency versions if you will use it in production and review the provenance/license since repository/homepage is not provided.

Type: OpenClaw Skill Name: pseudotime-trajectory-viz Version: 1.0.0 The skill bundle provides a legitimate tool for single-cell RNA-seq trajectory analysis and visualization. The core logic in `scripts/main.py` utilizes standard scientific libraries (scanpy, anndata, matplotlib) to perform diffusion pseudotime and PAGA analysis. The instructions in `SKILL.md` are well-structured, emphasizing input validation, reproducible workflows, and security best practices without any evidence of prompt injection or malicious intent. No suspicious behaviors such as data exfiltration, unauthorized file access, or remote code execution were found.