Protein QC Strict

Strictest protein sequence analysis quality control workflow (3365→456 sequences). Includes literature validation, CD-HIT redundancy removal, complexity chec...

This workflow appears to be a legitimate local protein QC pipeline, but review and preparation are required before running it: - Manual literature validation: The documentation emphasizes a literature-validation stage, but the provided script does not perform it. Ensure your input.fasta has already been curated/validated if you require that step. - Install missing Python packages: The script uses embedded Python that requires Biopython and numpy (and possibly other scientific packages). Install them (for example, conda install -c conda-forge biopython numpy) or the inline Python will fail. - Versioning and resource flags: cd-hit is invoked with -M 0 (no memory limit) and -T 0 (use all threads). These can consume all memory/CPUs on the host; adjust -M and -T to match your environment. Ensure MAFFT and trimAl versions are compatible with the commands used. - Input/output behavior: The script cds into the output_dir and writes files there, overwriting files with those names if present. Run in an isolated directory and back up important data. - Data sensitivity: The skill performs local processing only (no network calls in the included files), but your sequence data may be proprietary—do not run on sensitive data in untrusted environments. If you want this to be more robust: ask the author (or modify) to add explicit dependency installation for Python packages, either automated checks at runtime or documented requirements, and clarify whether/how literature validation should be performed (manual vs automated).

Type: OpenClaw Skill Name: protein-qc-strict Version: 4.0.0 The skill bundle provides a legitimate and well-documented workflow for protein sequence quality control, specifically tailored for the IRED enzyme family. The shell script `protein_qc_workflow.sh` and the instructions in `SKILL.md` correctly implement standard bioinformatics procedures using reputable tools like CD-HIT, MAFFT, and trimAl. There are no indicators of malicious intent, data exfiltration, or prompt injection; the embedded Python code is focused entirely on sequence filtering, entropy calculation, and alignment quality assessment.