If you're engineering therapeutic proteins or antibodies, this handles the glycosylation analysis you'd otherwise code from scratch every time. It scans sequences for N-glycosylation sequons (the N-X-S/T motif where X isn't proline), predicts O-glycosylation hotspots based on local serine/threonine density, and includes helpers for adding or removing glycosites through conservative mutations. The heuristic O-glyc predictor is explicitly a baseline, not a replacement for NetOGlyc, which is the right move since nothing beats experimentally trained models for that. Comes with integrations for external tools like NetOGlyc and references to GlycoShield for vaccine design work. Honest assessment: this is solid for antibody Fc optimization and initial therapeutic protein characterization, though you'll want wet lab confirmation for anything headed to the clinic.
npx -y skills add k-dense-ai/scientific-agent-skills --skill glycoengineering --agent claude-codeInstalls into .claude/skills of the current project.
Select a file.
sickn33/antigravity-awesome-skills
moizibnyousaf/ai-agent-skills
github/awesome-copilot