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Biopython Bio

Community
beita6969
biopython-bio

Bioinformatics operations via Biopython. Use when: user asks about DNA/protein sequences, BLAST, or PDB structures. NOT for: clinical genomics or variant calling pipelines.

Overview

Publisherbeita6969
RepositoryScienceClaw
Skill namebiopython-bio
Stars
950
Forks
106
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by beita6969 on GitHub. Read the source before you install it.

Installation

Install the Biopython Bio AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/beita6969/ScienceClaw/tree/main/skills/biopython-bio
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/biopython-bio .claude/skills/biopython-bio
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Biopython Bio in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Biopython Bio on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Biopython Bio is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Biopython Bio

Bioinformatics operations using Biopython.

When to Use

  • Reading/writing sequence files (FASTA, GenBank)
  • Running BLAST searches (local or remote NCBI)
  • Sequence alignment and manipulation
  • Parsing PDB protein structures
  • Phylogenetic tree construction
  • Querying NCBI Entrez databases

When NOT to Use

  • Clinical genomics or variant calling (use GATK, bcftools)
  • RNA-seq differential expression (use DESeq2, edgeR)
  • Genome assembly (use SPAdes, Canu)
  • Molecular dynamics simulations (use GROMACS, OpenMM)

Sequence Reading and Writing

python
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord

for record in SeqIO.parse('sequences.fasta', 'fasta'):
    print(f"{record.id}: {len(record.seq)} bp")

# Write FASTA
records = [SeqRecord(Seq('ATGCGATCGATCG'), id='seq1', description='example')]
SeqIO.write(records, 'output.fasta', 'fasta')

Sequence Manipulation

python
from Bio.Seq import Seq
from Bio.SeqUtils import gc_fraction, molecular_weight

dna = Seq('ATGCGATCGATCGATCG')
rev_comp = dna.reverse_complement()
protein = dna.translate()
gc = gc_fraction(dna)
mw = molecular_weight(dna, seq_type='DNA')

BLAST Searches

python
from Bio.Blast import NCBIWWW, NCBIXML

result_handle = NCBIWWW.qblast('blastn', 'nt', 'ATGCGATCGATCGATCG')
for record in NCBIXML.parse(result_handle):
    for aln in record.alignments:
        for hsp in aln.hsps:
            if hsp.expect < 1e-10:
                print(f"{aln.title[:60]}, E={hsp.expect}")

Pairwise Alignment

python
from Bio import Align

aligner = Align.PairwiseAligner()
aligner.mode = 'global'
aligner.match_score = 2
aligner.mismatch_score = -1
best = aligner.align('ATCGATCGATCG', 'ATCAATCAATCG')[0]
print(best, f"Score: {best.score}")

PDB Structure Parsing

python
from Bio.PDB import PDBParser, PDBList

structure = PDBParser(QUIET=True).get_structure('prot', 'structure.pdb')
for chain in structure[0]:
    for res in chain:
        if res.id[0] == ' ' and 'CA' in res:
            print(f"{res.resname} {res.id[1]}: {res['CA'].coord}")

Entrez Queries and Phylogenetics

python
from Bio import Entrez, Phylo, AlignIO
from Bio.Phylo.TreeConstruction import DistanceCalculator, DistanceTreeConstructor

Entrez.email = 'your.email@example.com'  # Required by NCBI
handle = Entrez.esearch(db='pubmed', term='CRISPR AND 2025[pdat]', retmax=5)
record = Entrez.read(handle)

# Phylogenetics from alignment
aln = AlignIO.read('aligned.fasta', 'fasta')
tree = DistanceTreeConstructor().nj(DistanceCalculator('identity').get_distance(aln))
Phylo.draw_ascii(tree)

Quick One-liner

bash
python3 -c "
from Bio.Seq import Seq
dna = Seq('ATGAAAGCTTGA')
print(f'Protein: {dna.translate()}, RevComp: {dna.reverse_complement()}')
"

Best Practices

  1. Always set Entrez.email before NCBI queries.
  2. Respect NCBI rate limits: max 3 requests/second without API key.
  3. Use QUIET=True in PDB parser to suppress warnings.
  4. Check sequence type before operations like translate().
  5. For large BLAST jobs, prefer local BLAST+ over remote NCBI.

Frequently asked questions

What does the Biopython Bio AI skill do?

Bioinformatics operations via Biopython. Use when: user asks about DNA/protein sequences, BLAST, or PDB structures. NOT for: clinical genomics or variant calling pipelines.

Why use Biopython Bio on TypingMind?

Because you install it once and use it with any model. Biopython Bio is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Biopython Bio in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/biopython-bio. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Biopython Bio?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Biopython Bio?

As many as you like. As long as a model supports skills, you can use Biopython Bio with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Biopython Bio AI skill free?

Yes. It is published on GitHub by beita6969 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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