structure_file_prep

structure_file_prep is a skill for Claude Code, Codex from ai4protein/VenusFactory2. It costs 75 tokens per session (496 once invoked), scanned A, original, no licence file.

A set of tools for preparing protein sequence and structure files, including FASTA files and 3D structures in PDB or mmCIF format. It can also check whether a structure is missing a ligand and connect RCSB Protein Data Bank records to UniProt protein entries.

In plain words
What is it for?
Use it to parse FASTA files, extract chains from PDB files, convert PDB and mmCIF files, check apo structures, convert many PDB files to FASTA, or retrieve a UniProt identifier from RCSB metadata.
Why use it?
It removes routine file-format and chain-extraction work before analysis. It is for preparing existing data, not predicting a protein structure or finding similar sequences.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/ai4protein/venusfactory2/structure_file_prep
Any agent
npx skills add ai4protein/VenusFactory2 --skill structure_file_prep
Clone the repo
git clone --depth 1 https://github.com/ai4protein/VenusFactory2

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for structure_file_prep

README.md
[![agentmods](https://agentmods.dev/badge/skills/ai4protein/venusfactory2/structure_file_prep.svg)](https://agentmods.dev/skills/ai4protein/venusfactory2/structure_file_prep)
Your own site
<a href="https://agentmods.dev/skills/ai4protein/venusfactory2/structure_file_prep"><img src="https://agentmods.dev/badge/skills/ai4protein/venusfactory2/structure_file_prep.svg" alt="Measured on agentmods" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 496 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00075 $0.00496
Opus 5 $0.00037 $0.00248
Sonnet 5 $0.00015 $0.00099
Haiku 4.5 $0.00007 $0.00050

Measured 6d ago against content hash ef1f5fe5ecff, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

structure_file_prep scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

src/agent/skills/structure_file_prep/SKILL.md · 47 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 6d ago First seen · 47 lines · 75 tokens per session scan A ef1f5fe5ecff

Subscribe to this mod's changes

structure_file_prep is a skill published in the GitHub repository ai4protein/VenusFactory2 (248 stars, last pushed 1mo ago), with no licence file. It adds 75 tokens to every session and 496 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

esm

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…

synthetic-sciences/openscience · 86 tokens

omnisci

Run OmniScientist end to end in the OmniScientist CLI using DeepSeek V4 Flash. Turn raw research data (images, signals, audio, video, 3-D, tables, or graphs) and an open direction into perceived evidence, a falsifiable hypothesis, recorded analysis, real citations, a gated candidate paper, PDF, and Overleaf bundle.…

Omni-Scientist/OmniScientist · 145 tokens

Protein Structures — AlphaFold & PDB

Obtain and predict protein 3D structures — fetch AlphaFold predicted models from the AlphaFold DB, experimental structures from the RCSB PDB, or predict a novel sequence with ColabFold — and visualise them in the Mol desktop app.

aristoteleo/PantheonOS · 63 tokens

math-skill

A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling.

Wholiver/Math.Skill · 33 tokens

arxiv-paper-search

通过 arXiv 官方公开 API(http://export.arxiv.org/api/query)检索学术论文,免费、无需 token。支持多维度并行检索和高级检索表达式。.

johnson7788/skill-ppt-agents · 107 tokens

design-taste-frontend

Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.

Leonxlnx/taste-skill · 61 tokens