glycobiology

glycobiology is a skill for Claude Code, Codex from synthetic-sciences/openscience. It costs 67 tokens per session (3,966 once invoked), scanned A, original, Apache-2.0.

A lightweight Python tool for finding likely sugar-attachment sites on protein sequences. It checks common sequence patterns for N-glycosylation and estimates O-glycosylation hotspots from the local abundance of serine and threonine.

In plain words
What is it for?
Use it to scan protein sequences, predict N- and O-glycosylation sites, compare predictions with experimental annotations, plan mutation studies, and map predicted sites to protein domains.
Why use it?
It provides a quick first analysis of where sugars may attach to a protein before deeper laboratory or structural work. The results are predictions, so they do not replace experimental confirmation.

Skill for Claude CodeCodex

About the project

synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.

synthetic-sciences/openscience · 3,473 stars · on GitHub · openscience.sh

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/synthetic-sciences/openscience/glycobiology
Any agent
npx skills add synthetic-sciences/openscience --skill glycobiology
Clone the repo
git clone --depth 1 https://github.com/synthetic-sciences/openscience

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 glycobiology

README.md
[![agentmods](https://agentmods.dev/badge/skills/synthetic-sciences/openscience/glycobiology.svg)](https://agentmods.dev/skills/synthetic-sciences/openscience/glycobiology)
Your own site
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/glycobiology"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/glycobiology.svg" alt="Measured on agentmods" height="20"></a>
Per session 67 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,966 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original 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 $0.00067 $0.03966
Opus 5 $0.00034 $0.01983
Sonnet 5 $0.00013 $0.00793
Haiku 4.5 $0.00007 $0.00397

Measured 6d ago against content hash d9e25a0c844e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

glycobiology 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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/predict_glycosylation.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

backend/cli/skills/biology/glycobiology/SKILL.md · 399 lines

How it starts

The opening of the file, as written. The whole thing — 399 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Glycobiology: Glycosylation Analysis

Overview

Glycobiology provides lightweight computational tools for predicting and analyzing glycosylation sites in protein sequences. This skill covers N-glycosylation sequon motif finding (N-X-S/T where X is not P), O-glycosylation hotspot prediction using a sliding window serine/threonine density heuristic, glycan structure tool references, and combined glycoprotein analysis with domain mapping. All analyses use pure Python with minimal dependencies (Biopython for sequence I/O, regex for pattern matching).

When to Use This Skill

  • Predicting N-glycosylation sites from protein sequences
  • Identifying O-glycosylation hotspot regions
  • Planning glycosylation site mutagenesis experiments
  • Comparing predicted vs experimentally determined glycosylation sites
  • Annotating glycosylation in biotherapeutic protein design
  • Surveying glycan analysis tools for downstream structural studies

Related Skills: For protein function and existing glycosylation annotations use uniprot-database. For protein 3D structure and site accessibility use alphafold-database. For sequence manipulation use biopython.

Installation

uv pip install biopython numpy

No additional dependencies required — this skill uses pure Python.

Quick Start

import re

def find_n_glycosylation_sites(sequence):
    """Find N-X-S/T sequons where X != P."""
    sites = []
    for i in range(len(sequence) - 2):
        if sequence[i] == 'N' and sequence[i+1] != 'P' and sequence[i+2] in ('S', 'T'):
            sites.append({
                'position': i + 1,  # 1-based
                'motif': sequence[i:i+3],
                'context': sequence[max(0,i-3):i+6]
            })
    return sites

# Example: human EPO
epo_seq = "MGVHECPAWLWLLLSLLSLPLGLPVLGAPPRLICDSRVLERYLLEAKEAENITTGCAEHCSLNENITVPDTKVNFYAWKRMEVGQQAVEVWQGLALLSEAVLRGQALLVNSSQPWEPLQLHVDKAVSGLRSLTTLLRALGAQKEAISPPDAASAAPLRTITADTFRKLFRVYSNFLRGKLKLYTGEACRTGDR"

sites = find_n_glycosylation_sites(epo_seq)
print(f"N-glycosylation sites: {len(sites)}")
for s in sites:
    print(f"  Position {s['position']}: {s['motif']} (context: ...{s['context']}...)")

Read the full file on GitHub · 399 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 399 lines · 67 tokens per session scan A d9e25a0c844e

Subscribe to this mod's changes

glycobiology is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 67 tokens to every session and 3,966 once invoked, about $0.0003 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.