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.
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.
npx agentmods add skills/synthetic-sciences/openscience/glycobiologynpx skills add synthetic-sciences/openscience --skill glycobiologygit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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.
[](https://agentmods.dev/skills/synthetic-sciences/openscience/glycobiology)<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>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.
| Model | Per session | Once 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 |
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.
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.
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']}...)")
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.
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.
- 6d ago First seen · 399 lines · 67 tokens per session scan A d9e25a0c844e
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.
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