Analyze DNA/RNA/protein sequences. Use when the user provides a sequence and asks for analysis, translation, GC content, ORFs, motifs, restriction sites, or primer design. Triggers on "sequence", "translate", "GC content", "ORF", "primer", "restriction", "complement", "reverse complement".
npx skills add https://github.com/BioTender-max/awesome-bio-agent-skills --skill sequence-analysis
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 alignment
from 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?"
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementing any feature or bugfix, before writing implementation code
Use when you have a spec or requirements for a multi-step task, before touching code
Use when creating new skills, editing existing skills, or verifying skills work before deployment
Use when writing or improving README files. Not all READMEs are the same — provides templates and guidance matched to your audience and project type.
| Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.
Take biotender-max/sequence-analysis from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.