sequence-analysis — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited sequence-analysis (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.
Comprehensive sequence analysis using BioPython and command-line tools.
from Bio.Seq import Seq
from Bio.SeqUtils import gc_fraction, molecular_weight
seq = Seq("ATGCGATCGATCGATCG...")
print(f"Length: {len(seq)} bp")
print(f"GC Content: {gc_fraction(seq)*100:.1f}%")
print(f"Complement: {seq.complement()}")
print(f"Reverse Complement: {seq.reverse_complement()}")
print(f"Protein: {seq.translate()}")from Bio.Seq import Seq
def find_orfs(sequence, min_length=100):
orfs = []
seq = Seq(str(sequence))
for strand, nuc in [("+", seq), ("-", seq.reverse_complement())]:
for frame in range(3):
trans = nuc[frame:].translate()
aa_seq = str(trans)
start = 0
while start < len(aa_seq):
m_pos = aa_seq.find("M", start)
if m_pos == -1:
break
stop_pos = aa_seq.find("*", m_pos)
if stop_pos == -1:
stop_pos = len(aa_seq)
orf_len = (stop_pos - m_pos) * 3
if orf_len >= min_length:
nt_start = frame + m_pos * 3
orfs.append({
"strand": strand,
"frame": frame + 1,
"start": nt_start,
"length_aa": stop_pos - m_pos,
"length_nt": orf_len,
"protein": aa_seq[m_pos:stop_pos]
})
start = stop_pos + 1
return sorted(orfs, key=lambda x: x["length_nt"], reverse=True)from Bio.Restriction import RestrictionBatch, Analysis
from Bio.Seq import Seq
seq = Seq("ATGCGATCGATCG...")
rb = RestrictionBatch(["EcoRI", "BamHI", "HindIII", "NotI", "XhoI"])
ana = Analysis(rb, seq)
results = ana.full()
for enzyme, sites in results.items():
if sites:
print(f"{enzyme}: cuts at positions {sites}")from Bio.Seq import Seq
from Bio.SeqUtils import MeltingTemp as mt
def design_primers(seq_str, product_size_range=(200, 800)):
seq = Seq(seq_str)
# Forward primer (first 20bp)
fwd = seq[:20]
fwd_tm = mt.Tm_NN(fwd)
# Reverse primer (last 20bp, reverse complement)
rev = seq[-20:].reverse_complement()
rev_tm = mt.Tm_NN(rev)
print(f"Forward: 5'-{fwd}-3' (Tm={fwd_tm:.1f}°C, GC={gc_fraction(fwd)*100:.0f}%)")
print(f"Reverse: 5'-{rev}-3' (Tm={rev_tm:.1f}°C, GC={gc_fraction(rev)*100:.0f}%)")
print(f"Product size: {len(seq)} bp")If user provides multiple sequences:
# Write sequences to FASTA file
cat > /tmp/sequences.fa << 'EOF'
>seq1
ATGCGATCG...
>seq2
ATGCAATCG...
EOF
# If clustalw/muscle available, use them
# Otherwise use BioPython's pairwise alignmentfrom Bio import pairwise2
from Bio.pairwise2 import format_alignment
alignments = pairwise2.align.globalxx(seq1, seq2)
print(format_alignment(*alignments[0]))*Sequence Analysis Results*
• Length: 1,234 bp
• GC Content: 52.3%
• ORFs found: 3 (longest: 456 aa)
*Protein Translation (frame +1):*Restriction Sites: • EcoRI: positions 123, 456 • BamHI: position 789 • HindIII: no sites found
### 7. Follow-up suggestions
- "Want me to BLAST this sequence?"
- "Should I design primers for a specific region?"
- "Want a detailed ORF map?"
- "Should I check for conserved domains?"~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.