Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting 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 skills add K-Dense-AI/scientific-agent-skills --skill glycoengineeringgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/glycoengineering)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/glycoengineering"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/glycoengineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/glycoengineering"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/glycoengineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00083 | $0.02819 |
| Opus 5.5 | $0.00033 | $0.01128 |
| Sonnet 5.5 | $0.00017 | $0.00564 |
| Haiku 4.5 | $0.00008 | $0.00282 |
Grade A, and why
glycoengineering 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 26d 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- glycoengineering — 100% identical, 437 lines differ
- glycoengineering — 100% identical, 435 lines differ
- glycoengineering — 100% identical, 437 lines differ
- glycoengineering — 100% identical, 437 lines differ
- glycoengineering — 100% identical, 435 lines differ
- glycoengineering — 98% identical, 436 lines differ
- glycoengineering — 98% identical, 436 lines differ
- glycoengineering — 98% identical, 436 lines differ
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Glycoengineering
When to use
Use for canonical N-glycosylation candidate analysis, O-GalNAc candidate triage, antibody glycoform comparison, or structural glycan shielding. Keep four kinds of result separate: sequence motif, prediction, experimentally supported occupancy, and glycan structure/composition. None alone establishes all the others.
This workflow focuses on eukaryotic secretory-pathway N-glycosylation and mucin-type O-GalNAc. O-GlcNAc, O-mannose, O-fucose and other O-linked modifications require appropriate evidence and predictors; S/T enrichment does not identify the type.
Workflow
- Record the protein accession, isoform/version, exact sequence, expression host, signal peptide/transmembrane topology and construct boundaries. Preserve a mapping from submitted sequence coordinates to mature protein, PDB chain/residue identifiers and antibody numbering where relevant.
- Scan canonical N-X-[S/T] candidates with X not Pro. Retain overlaps:
NNSThas candidates starting at 1 and 2. A missing canonical motif does not rule out unusual N-glycosylation; a present motif does not establish occupancy. - Prioritize secreted/luminal/extracellular regions using topology and curated evidence. Sequence-only results in a cytoplasmic region are not evidence of secretory-pathway glycosylation.
- For O-GalNAc, use S/T density only as a descriptive feature, then inspect a suitable predictor and cell/transferase context. Do not exclude SP/TP motifs: adjacent Pro can be favorable, depending on the GalNAc-transferase and context. See the GalNAc-T substrate study.
- Propose explicitly numbered mutations, list every changed residue and rescan the full product for lost/created overlapping motifs. Evaluate structural and expression effects independently of glycosylation.
- Validate occupancy and glycoforms experimentally, with localization evidence, glycan composition/structure confidence, and the biological assay required by the engineering objective. For biosimilar comparisons, match sample handling, analytical coverage and quantification before comparing glycan percentages.
What ships with it
4 files 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.
- 26d ago Changed · -171 lines · +8 tokens per session b529bde5367c
- 27d ago Changed · +2 lines bcd11c49a92d
- 1mo ago Changed · +16 lines 66bd9a2560de
- 1mo ago First seen · 340 lines · 75 tokens per session scan A cfb906827d4d
glycoengineering is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,953 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 2,819 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other skills, from other repositories
bio-prefect-dask-nextflow
Choose and scaffold Prefect+Dask or Nextflow pipelines. Use when designing a local, distributed, or Slurm-backed bioinformatics workflow.
discovery-toolbox
A routed repertoire of 90 scientific thinking operators for biological research agents - visual reasoning, detectability and information budgets, search reframing, causal identification, competing explanations, observation and selection processes, pipeline artifact diagnosis, effort allocation, and confirmation…
rdkit-qsar-pharmacophore
Computes 2048-bit ECFP4 Morgan fingerprints from SMILES, trains LightGBM regressors for pIC50 prediction, and extracts SHAP feature attributions.
discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bulk-rnaseq-counts-to-de-deseq2
Run differential expression analysis on bulk RNA-seq count data with DESeq2 (R). Covers DESeqDataSet construction from a count matrix, tximport (Salmon/Kallisto), featureCounts, or SummarizedExperiment; pre-filtering; design formulas (simple, batch, paired, interaction, multi-factor, LRT); result extraction by…
seurat-skill
Comprehensive Seurat v5 (R) guide for single-cell RNA-seq and multimodal analysis. Covers installation, standard workflows (Normalize/SCTransform), clustering, integration (CCA/RPCA/Harmony), differential expression (FindMarkers/FindAllMarkers), visualization (DimPlot/FeaturePlot/VlnPlot/DoHeatmap), spatial…