Pseudotime Trajectory Viz
v1.0.0Analyze data with `pseudotime-trajectory-viz` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
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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, SKILL.md, README, requirements.txt and scripts/main.py all align: they implement single-cell pseudotime inference and visualization using scanpy/anndata and related libraries. Required packages are appropriate for the stated functionality and there are no unrelated binaries, credentials, or config paths requested.
Instruction Scope
Runtime instructions are narrowly scoped: validate inputs, run a non-destructive smoke check (python -m py_compile scripts/main.py), and execute scripts/main.py with the user-supplied AnnData file. Instructions do not ask the agent to read unrelated system files, environment secrets, or to transmit data to external endpoints.
Install Mechanism
No install spec is provided (instruction-only with included script). Dependencies are listed in requirements.txt for pip; there are no downloads from arbitrary URLs or archive extraction steps in the manifest. This is a low-risk install posture, though installing Python scientific packages can pull many transitive dependencies (expected for this domain).
Credentials
The skill declares no environment variables, no credentials, and no config paths. The code operates on user-specified input files and local outputs only. There are no requested secrets or unrelated environment access.
Persistence & Privilege
always:false (default) and the skill does not request persistent system-wide changes. The skill can be invoked autonomously by an agent (disable-model-invocation:false) which is the platform default; this is not combined with any other broad privileges or secret access here.
Assessment
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.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.