Agent skill

Bio Structural Biology Structure Io

by GPTomics in GPTomics/bioSkills

Reads, writes, downloads, and converts macromolecular structures with Biopython Bio.PDB.

MITAuto-check passedResearch & Science

Install Bio Structural Biology Structure Io

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-structural-biology-structure-io -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-structural-biology-structure-io --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/structural-biology/structure-io .claude/skills/bio-structural-biology-structure-io && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-structural-biology-structure-io
GitHub stars
1.2k
Used in
1 other repo
Token cost
~4k tokens
SKILL.md length
1,464 words
Files
5
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Reads, writes, downloads, and converts macromolecular structures with Biopython Bio.PDB.

  • Choosing a format (mmCIF/PDBx vs legacy PDB vs BinaryCIF) for a structure that may exceed PDBs ~62-chain / 99
  • SKILL.md covers Version Compatibility, Governing Principle, Decision: which format and Decision: Bio.PDB vs gemmi, plus 13 more sections
  • Runs Python scripts from its folder; calls pip; reaches files.rcsb.org
  • 999-atom limits

What it does

Bio Structural Biology Structure Io is an agent skill from GPTomics/bioSkills. Reads, writes, downloads, and converts macromolecular structures with Biopython Bio.PDB. Use when choosing a format (mmCIF/PDBx vs legacy PDB vs BinaryCIF) for a structure that may exceed PDB's ~62-chain / 99,999-atom limits; when residue numbers do not match the paper because of auth vs label numbering (MMCIFParser defaults authresidues=True); when metadata (resolution, method, R-free) is missing because Bio.PDB drops it and MMCIF2Dict is needed; when the deposited coordinates are the asymmetric unit and the…

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files (for example `examples/download_structure.py`, `examples/extract_chain.py` and `examples/parse_pdb.py`).

It sits in Research & Science, covering Protein structure and design and Bioinformatics. It works with Biopython. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Choosing a format (mmCIF/PDBx vs legacy PDB vs BinaryCIF) for a structure that may exceed PDBs ~62-chain / 99
  • 999-atom limits
  • Residue numbers do not match the paper because of auth vs label numbering (MMCIFParser defaults authresidues=True)
  • Metadata (resolution

Example prompts

  • “/bio-structural-biology-structure-io”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • files.rcsb.org

    Also links to:

    • rcsb.org
    • wwpdb.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Structural Biology Structure Io loads about 4k tokens when it runs. Until then it costs about 186 tokens; SKILL.md has 1,464 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~186
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,464 words, ~3,982 tokens.

Download SKILL.mdSave it as .claude/skills/bio-structural-biology-structure-io/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
bio-structural-biology-structure-io
description
Reads, writes, downloads, and converts macromolecular structures with Biopython Bio.PDB. Use when choosing a format (mmCIF/PDBx vs legacy PDB vs BinaryCIF) for a structure that may exceed PDB's ~62-chain / 99,999-atom limits; when residue numbers do not match the paper because of auth_* vs label_* numbering (MMCIFParser defaults auth_residues=True); when metadata (resolution, method, R-free) is missing because Bio.PDB drops it and MMCIF2Dict is needed; when the deposited coordinates are the asymmetric unit and the biological assembly must be downloaded separately; when downloading from RCSB (files.rcsb.org, PDBList); and when a legacy MMTF path is dead (RCSB retired MMTF July 2024, use BinaryCIF).
tool_type
python
primary_tool
Bio.PDB
goal_approach_exempt
true

Version Compatibility

Reference examples tested with: biopython 1.85+

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Structure I/O

"Read a structure file" -> Parse a deposited coordinate file into an in-memory SMCRA tree, or fetch it from a wwPDB mirror.

  • Python: Bio.PDB.MMCIFParser().get_structure('id', 'file.cif'), Bio.PDB.PDBParser(), Bio.PDB.PDBList()

Governing Principle

mmCIF (PDBx) is the canonical modern format; the legacy fixed-column PDB format is a frozen, lossy container, and a "parse everything as PDB" reflex silently truncates or fails on anything large. The PDB format was frozen in 2012 and cannot physically exceed 99,999 atoms (5-digit serial), ~62 chains (single alphanumeric chain id), or 9,999 residues per chain (wwPDB file-format documentation; the PDB archive itself is Berman et al. 2000 Nucleic Acids Res 28:235-242). Large assemblies (ribosomes, capsids, spliceosomes, most big cryo-EM structures) therefore exist ONLY as mmCIF, and converting a big mmCIF down to PDB renames multi-character chains and overflows serial numbers, silently corrupting any downstream tool that keys on chain id. mmCIF has been the wwPDB archive standard since 2014 and mandatory for crystallographic depositions since July 2019.

Two further traps compound this. First, Bio.PDB is PERMISSIVE by design: it reads malformed files, and it silently drops anisotropic B-factors (ANISOU), collapses each disordered atom to its highest-occupancy alternate, and never models most metadata (resolution, method, R-free, entity graph, assembly operators). Parse-success is not data integrity. Second, the deposited coordinates for an X-ray entry are usually the ASYMMETRIC UNIT, a crystallographic bookkeeping object that is frequently NOT the biologically functional oligomer -- so any interface, oligomeric-state, or buried-surface question must first obtain the biological assembly (Krissinel & Henrick 2007 J Mol Biol 372:774). "One chain in the file" is never evidence of a monomer.

The escape hatch for all three ceilings (assembly generation, very large structures, full mmCIF fidelity) is gemmi (Wojdyr 2022 JOSS 7:4200); Bio.PDB cannot apply the assembly operators itself. Prefer Bio.PDB for teaching, small structures, and hierarchy walks; reach for gemmi when the questions above appear.

Decision: which format

FormatBest whenFails whenHard limits
mmCIF / PDBx (.cif, .cif.gz)Any modern default; large assemblies; full metadata; auth+label numbering; ANISOU/entitiesA legacy tool only reads fixed-column PDBNone
Legacy PDB (.pdb, .ent)Small structure feeding an old tool that demands PDB columnsStructure exceeds the format's limits (silently truncates/renames)99,999 atoms, ~62 chains, 9,999 resseq/chain, single-char chain id
BinaryCIF (.bcif, .bcif.gz)Compact binary transport at bandwidth/scale; the current binary formatAn ecosystem still expects the retired MMTFNone (lossless mmCIF encoding)
MMTF (.mmtf)Nothing new -- RCSB stopped serving MMTF on 2 July 2024Any live download (the endpoint is decommissioned); treat as read-only-legacyRetired upstream

Decision: Bio.PDB vs gemmi

TaskToolWhy
Hierarchy walk, small X-ray/NMR structure, teachingBio.PDBReadable SMCRA tree, pure Python, ubiquitous
Read metadata Bio.PDB drops (resolution, method, R-free, assembly ops)Bio.PDB MMCIF2DictRaw category access without object-model loss
Generate the biological assembly from deposited coordsgemmiApplies _pdbx_struct_oper_list; Bio.PDB has no operator-application code
Very large structure (>100k atoms), many structures, fast neighbor searchgemmiC++ core scales; Bio.PDB's pure-Python tree is slow/memory-heavy
mmCIF round-trip without data loss (entities, label scheme, ANISOU)gemmiFull PDBx data model; writes hybrid-36 serials when >99,999

Required Imports

python
from Bio.PDB import PDBParser, MMCIFParser, PDBIO, MMCIFIO, PDBList, Select
from Bio.PDB.MMCIF2Dict import MMCIF2Dict
from Bio.PDB.binary_cif import BinaryCIFParser

Parse an mmCIF File (the modern default)

python
from Bio.PDB import MMCIFParser

# auth_residues/auth_chains default to True: numbering matches the paper/UniProt.
parser = MMCIFParser(QUIET=True)
structure = parser.get_structure('4hhb', '4hhb.cif')

# label numbering is contiguous 1..N with no insertion codes -- a DIFFERENT scheme.
label_parser = MMCIFParser(QUIET=True, auth_residues=False, auth_chains=False)
label_structure = label_parser.get_structure('4hhb', '4hhb.cif')

Setting auth_residues=False renumbers to the mmCIF internal label scheme, so residue 100 in one parse is a different residue in the other. This is the single most common "my selection points at the wrong residue" bug. auth is what matches the literature and sequence databases; label is gap-free internal bookkeeping. Pick one scheme and stay in it.

Parse a Legacy PDB File

python
from Bio.PDB import PDBParser

# QUIET=True suppresses PDBConstructionWarning (discontinuous chains, missing occupancy).
parser = PDBParser(QUIET=True)
structure = parser.get_structure('1crn', '1crn.pdb')

Parse a BinaryCIF File (compact binary, replaces MMTF)

python
from Bio.PDB.binary_cif import BinaryCIFParser

# .get_structure(id, source); gz is handled transparently.
parser = BinaryCIFParser()
structure = parser.get_structure('1gbt', '1gbt.bcif.gz')

MMTF is intentionally absent here. RCSB retired it on 2 July 2024 and MMTFParser.get_structure_from_url targets a decommissioned service; BinaryCIF is the replacement.

Read Metadata Bio.PDB Drops (MMCIF2Dict)

python
from Bio.PDB.MMCIF2Dict import MMCIF2Dict

# MMCIF2Dict returns category -> list[str]; index [0] and cast yourself.
meta = MMCIF2Dict('4hhb.cif')
resolution = meta.get('_refine.ls_d_res_high', ['NA'])[0]
method = meta.get('_exptl.method', ['NA'])[0]
r_free = meta.get('_refine.ls_R_factor_R_free', ['NA'])[0]
r_work = meta.get('_refine.ls_R_factor_R_work', ['NA'])[0]

The parser's thin structure.header omits resolution/R-free for many files; the dict reaches anything in the mmCIF, including assembly operators the object model never builds.

Download from RCSB (PDBList)

python
from Bio.PDB import PDBList

pdbl = PDBList()

# pdir=None writes into a two-char divided subdirectory tree (e.g. hh/4hhb.cif),
# NOT the current directory; pass pdir='.' to control the location.
path = pdbl.retrieve_pdb_file('4HHB', pdir='.', file_format='mmCif')

# file_format 'pdb' fetches legacy PDB only when the entry fits the format.
legacy_path = pdbl.retrieve_pdb_file('4HHB', pdir='.', file_format='pdb')

file_format='mmCif' (that exact casing) is the current default recommendation. retrieve_pdb_file has no assembly_num parameter in current Biopython -- download the assembly directly (below).

Download the Biological Assembly (Bio.PDB cannot build it)

python
import gzip, shutil, urllib.request

# The ASU is often not the functional oligomer; RCSB pre-applies the operators
# in the -assemblyN file, so downloading it is safer than regenerating.
pdb_id = '1abc'
url = f'https://files.rcsb.org/download/{pdb_id.upper()}-assembly1.cif.gz'
urllib.request.urlretrieve(url, f'{pdb_id}-assembly1.cif.gz')
with gzip.open(f'{pdb_id}-assembly1.cif.gz', 'rb') as fin, open(f'{pdb_id}-assembly1.cif', 'wb') as fout:
    shutil.copyfileobj(fin, fout)

Bio.PDB has no operator-application code, so if only the deposited ASU is on disk it cannot construct the assembly -- use the RCSB assembly file or gemmi's transform_to_assembly.

Write a Structure

python
from Bio.PDB import PDBParser, MMCIFIO

parser = PDBParser(QUIET=True)
structure = parser.get_structure('1crn', '1crn.pdb')

# Writing mmCIF preserves multi-char chains and >99,999 serials; PDB cannot.
io = MMCIFIO()
io.set_structure(structure)
io.save('1crn_out.cif')

Write a Subset with the Select Class

python
from Bio.PDB import PDBParser, PDBIO, Select

class ProteinChainSelect(Select):
    def __init__(self, chain_id):
        self.chain_id = chain_id

    def accept_chain(self, chain):
        return chain.id == self.chain_id

    def accept_residue(self, residue):
        # id[0] is the hetflag: ' ' standard, 'W' water, 'H_XXX' hetero.
        return residue.id[0] == ' '

parser = PDBParser(QUIET=True)
structure = parser.get_structure('1crn', '1crn.pdb')

io = PDBIO()
io.set_structure(structure)
io.save('chain_A_protein.pdb', ProteinChainSelect('A'))

Override any of accept_model, accept_chain, accept_residue, accept_atom to return truthy to keep. Writing a large mmCIF back out as PDB through PDBIO is where multi-character chains and serial overflow silently corrupt the output.

Capture Warnings for an Unfamiliar File

python
from Bio.PDB import PDBParser
import warnings

# QUIET=True is the reflex, but it hides 'chain is discontinuous' -- the warning
# that flags a numbering gap or a merge the parser should not have made.
parser = PDBParser(QUIET=False)
with warnings.catch_warnings(record=True) as caught:
    warnings.simplefilter('always')
    structure = parser.get_structure('unknown', 'unknown.pdb')
    for w in caught:
        print(w.message)
Show full SKILL.md (667 more words)Show less

Common Errors

SymptomCauseFix
Residue numbers do not match the paper / a UniProt mappingParsed with auth_residues=False, so numbering is the label schemeUse the default auth_residues=True; only switch to label for gap-free internal indexing, never mix schemes
resolution/R-free is None from structure.headerBio.PDB's header dict is thin and omits refinement metadataRead _refine.ls_d_res_high, _refine.ls_R_factor_R_free, _exptl.method via MMCIF2Dict
TypeError: retrieve_pdb_file() got an unexpected keyword 'assembly_num'Current Biopython PDBList has no assembly_num parameterDownload ...-assembly1.cif.gz from files.rcsb.org directly (or use gemmi)
Downloaded file is not in the current directorypdir=None writes a two-char divided subdirectory tree (hh/4hhb.cif)Pass an explicit pdir='.' (or the target dir) to retrieve_pdb_file
MMTF download 404s / connection failsRCSB retired MMTF on 2 July 2024; the endpoint is goneUse BinaryCIF (.bcif) or mmCIF; treat MMTF files as read-only-legacy
Chains renamed and atom serials wrong after PDB outputA large mmCIF exceeded PDB's ~62-chain / 99,999-atom limits on writeStay in mmCIF (MMCIFIO), or use gemmi's hybrid-36 writer
Analyzing a "monomer" that is really half a dimerComputed on the deposited ASU, not the biological assemblyFetch the -assembly1 file (or generate with gemmi) before any interface/oligomer analysis
Download URL 404s for a newly deposited entryThe 4-char id space is being exhausted (~2028) and RCSB is phasing in extended 12-char ids (pdb_00006uv8)Use the full extended id in the files.rcsb.org path; a hard-coded 4-char assumption breaks once extended ids arrive
Anisotropic B-factors (ANISOU) lost after a Bio.PDB round-tripANISOU is parsed but not reliably written backPreserve the original file, or round-trip through gemmi when ANISOU matters
Distances/clashes look wrong at a partially disordered siteA disordered atom silently forwards to its highest-occupancy altlocEnumerate altlocs with atom.disordered_get_list() and set an explicit altloc policy (see structure-navigation)
KeyError fetching a residue by integer, e.g. chain[100]Residue id is the tuple (hetflag, resseq, icode); insertion codes and hetero break the bare-int pathKey on the full 3-tuple, e.g. chain[(' ', 100, ' ')]
BinaryCIFParser import fails from Bio.PDBIt lives in the submodule Bio.PDB.binary_cif, not the top-level packagefrom Bio.PDB.binary_cif import BinaryCIFParser
Silent wrong results on a malformed file that "parsed fine"Bio.PDB is permissive; parse-success is not data integrityParse with QUIET=False and inspect PDBConstructionWarnings for unfamiliar files
  • structure-navigation - Walk the SMCRA tree, handle altlocs/insertion codes, extract observed vs SEQRES sequence
  • structure-modification - Transform coordinates, strip waters/hetero safely, edit B-factors before writing
  • geometric-analysis - Measure distances, angles, SASA, and superimpose once the correct assembly is loaded
  • interface-analysis - Analyze the interfaces that only exist in the biological assembly, not the ASU
  • structure-validation - Read resolution/R-free/clashscore to judge whether the loaded model is trustworthy
  • structure-preparation - Add hydrogens/protonation and fill atoms on the loaded assembly before docking or MD
  • alignment/structural-alignment - Superpose sequence-different structures that Bio.PDB's ordered correspondence cannot handle
  • database-access/uniprot-access - Map structure residues back to a UniProt reference sequence

References

  • Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000). The Protein Data Bank. Nucleic Acids Res 28(1):235-242. DOI 10.1093/nar/28.1.235.
  • Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, de Hoon MJL (2009). Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25(11):1422-1423. DOI 10.1093/bioinformatics/btp163.
  • Hamelryck T, Manderick B (2003). PDB file parser and structure class implemented in Python. Bioinformatics 19(17):2308-2310. DOI 10.1093/bioinformatics/btg332.
  • Wojdyr M (2022). GEMMI: A library for structural biology. Journal of Open Source Software 7(73):4200. DOI 10.21105/joss.04200.
  • Kunzmann P, Hamacher K (2018). Biotite: a unifying open source computational biology framework in Python. BMC Bioinformatics 19:346. DOI 10.1186/s12859-018-2367-z.
  • Kim H, Mirdita M, Steinegger M (2023). Foldcomp: a library and format for compressing and indexing large protein structure sets. Bioinformatics 39(4):btad153. DOI 10.1093/bioinformatics/btad153.
  • Krissinel E, Henrick K (2007). Inference of macromolecular assemblies from crystalline state. J Mol Biol 372(3):774-797. DOI 10.1016/j.jmb.2007.05.022.
  • RCSB PDB (2024). Switch from MMTF to BinaryCIF: RCSB ceased serving MMTF on 2 July 2024. https://www.rcsb.org/news/65a1af31c76ca3abcc925d0c
  • wwPDB. File formats and the PDB (legacy format frozen 2012; mmCIF archive standard 2014; 99,999-atom / 62-chain limits; large entries mmCIF-only). https://www.wwpdb.org/documentation/file-formats-and-the-pdb

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files in structural-biology/structure-io of GPTomics/bioSkills.

  • SKILL.md
  • examples/download_structure.py
  • examples/extract_chain.py
  • examples/parse_pdb.py
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

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Works with

Questions about Bio Structural Biology Structure Io

What does Bio Structural Biology Structure Io do?

Reads, writes, downloads, and converts macromolecular structures with Biopython Bio.PDB. Bio Structural Biology Structure Io is an agent skill from GPTomics/bioSkills.PDB.

When should I use Bio Structural Biology Structure Io?

Bio Structural Biology Structure Io fits situations like: choosing a format (mmCIF/PDBx vs legacy PDB vs BinaryCIF) for a structure that may exceed PDBs ~62-chain / 99; 999-atom limits; residue numbers do not match the paper because of auth vs label numbering (MMCIFParser defaults authresidues=True); metadata (resolution.

How do I install Bio Structural Biology Structure Io in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-structural-biology-structure-io -a claude-code`. Or copy the skill folder (structural-biology/structure-io in GPTomics/bioSkills) into .claude/skills/bio-structural-biology-structure-io in your project. Claude Code loads it when a task matches its description.

How do I install Bio Structural Biology Structure Io in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-structural-biology-structure-io -a codex`. Or copy the skill folder (structural-biology/structure-io in GPTomics/bioSkills) into .agents/skills/bio-structural-biology-structure-io in your project. Codex loads it when a task matches its description.

Can I use Bio Structural Biology Structure Io in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-structural-biology-structure-io -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-structural-biology-structure-io, .gemini/skills/bio-structural-biology-structure-io, .github/skills/bio-structural-biology-structure-io and .opencode/skills/bio-structural-biology-structure-io in your project.

What does Bio Structural Biology Structure Io need to run?

Going by SKILL.md and its folder, Bio Structural Biology Structure Io needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Bio Structural Biology Structure Io access the network?

SKILL.md names 3 domains. In commands or code: files.rcsb.org; the agent is likely to contact it when it follows the instructions. As links in the text: rcsb.org and wwpdb.org. This is read from the text; nothing was executed.

Is Bio Structural Biology Structure Io safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Structural Biology Structure Io use?

Bio Structural Biology Structure Io is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Structural Biology Structure Io use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Structural Biology Structure Io?

Skills that share tags, products or a category with Bio Structural Biology Structure Io: Biopython Bioinformatics (aiming-lab/AutoResearchClaw, 15k stars), Gget (davila7/claude-code-templates, 33k stars), Bio Pdb Geometric Analysis (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars) and Bio Pdb Structure Io (FreedomIntelligence/OpenClaw-Medical-Skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Structural Biology Structure Io?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.