bio-workflows-riboseq-pipeline — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited bio-workflows-riboseq-pipeline (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Reference examples tested with: Bowtie2 2.5.3+, STAR 2.7.11+, cutadapt 4.4+, numpy 1.26+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signatures<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Analyze my ribosome profiling data from FASTQ to translation efficiency" → Orchestrate adapter trimming, rRNA depletion, genome alignment, periodicity QC, ORF detection (RiboCode), stalling analysis, and translation efficiency estimation (riborex).
FASTQ → Preprocessing → rRNA removal → Alignment → P-site → TE → ORF calling# Remove adapters
cutadapt -a CTGTAGGCACCATCAAT \
--minimum-length 25 --maximum-length 35 \
-o trimmed.fastq.gz reads.fastq.gz
# Remove rRNA
bowtie2 -x rRNA_index --un non_rrna.fastq.gz -U trimmed.fastq.gz# Align to transcriptome
STAR --genomeDir star_index \
--readFilesIn non_rrna.fastq.gz \
--readFilesCommand zcat \
--outFilterMismatchNmax 2 \
--alignEndsType EndToEnd \
--outSAMtype BAM SortedByCoordinatefrom plastid import BAMGenomeArray
# Build metagene profile
metagene_generate annotation.gtf ribo.bam metagene_output/
# Calculate P-site offsets
psite annotation.gtf metagene_output/profile.txt psite_offsets.txt# TE = Ribo-seq RPKM / RNA-seq RPKM
from plastid import BAMGenomeArray
import numpy as np
ribo_counts = count_reads(ribo_bam, genes)
rna_counts = count_reads(rna_bam, genes)
te = ribo_counts / rna_counts# RiboCode for ORF calling
RiboCode -a annotation.gtf -c config.txt -o ribocoded_orfs~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.