How do I install the K Dense AI skill?

Run npx skillmds@latest add plurigrid/k-dense-ai in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the K Dense AI skill do?

- `alphafold-database` - Protein structure prediction It is listed under Coding & Dev Tools on SkillMD.

Is K Dense AI safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with K Dense AI?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is K Dense AI free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published K Dense AI?

plurigrid (@plurigrid) published this skill. Their other Agent Skills are listed on their SkillMD profile.

K Dense AI by plurigrid — Agent Skill

Name: K Dense AI
Author: plurigrid

File contents

K-Dense-AI Scientific Computing Bundle

Provenance: K-Dense-AI / Codex scientific skills GF(3) Trit: +1 (PLUS) - Generative/Synthetic Mutual Awareness: trailofbits-security bundle (MINUS -1)

Skills (37)

Bioinformatics Core

alphafold-database - Protein structure prediction
biopython - Biological computation toolkit
esm - Evolutionary Scale Modeling
scanpy - Single-cell analysis
anndata - Annotated data matrices
cellxgene-census - Cell atlas queries

Databases

chembl-database - Bioactive molecules
pubchem-database - Chemical compounds
uniprot-database - Protein sequences
pdb-database - Protein structures
kegg-database - Pathway maps
reactome-database - Biological pathways
brenda-database - Enzyme data
drugbank-database - Drug information
gene-database - Gene annotations
biorxiv-database - Preprints
clinicaltrials-database - Clinical studies

Chemistry & Drug Discovery

rdkit - Cheminformatics
deepchem - Deep learning for chemistry
catalyst-chemical - Reaction modeling

Molecular Simulation

enzyme-autodiff - Automatic differentiation for enzymes

Lab & Workflow

benchling-integration - Lab notebook
bioservices - Web service APIs
adaptyv - Adaptive experiments
biomni - Multi-omics

Physics & Quantum

astropy - Astronomy
cirq - Quantum circuits

ML for Science

scikit-bio - Biological data science
scikit-learn - Machine learning
scikit-survival - Survival analysis
arboreto - Gene regulatory networks
aeon - Time series for science

Categorical

phylogenetic-operad-acset - Tree structures as operads

Mutual Awareness Protocol

{:bundle "k-dense-ai"
 :trit :plus
 :aware-of ["trailofbits-security"]
 :interface
 {:analyze (fn [molecule] "Use rdkit/deepchem")
  :validate (fn [result] "Delegate to trailofbits for security review")
  :synthesize (fn [target] "Generate candidates")}
 :handoff-to "trailofbits-security"
 :handoff-trigger [:untrusted-input :external-api :file-parsing]}

Usage

# Load bundle
skill k-dense-ai

# Cross-bundle workflow
skill k-dense-ai -> trailofbits-security  # Validate drug candidate data pipeline

---name: k-dense-aidescription: - `alphafold-database` - Protein structure prediction---# K-Dense-AI Scientific Computing Bundle> Provenance: K-Dense-AI / Codex scientific skills> GF(3) Trit: **+1 (PLUS)** - Generative/Synthetic> Mutual Awareness: `trailofbits-security` bundle (MINUS -1)## Skills (37)### Bioinformatics Core- `alphafold-database` - Protein structure prediction- `biopython` - Biological computation toolkit- `esm` - Evolutionary Scale Modeling- `scanpy` - Single-cell analysis- `anndata` - Annotated data matrices- `cellxgene-census` - Cell atlas queries### Databases- `chembl-database` - Bioactive molecules- `pubchem-database` - Chemical compounds- `uniprot-database` - Protein sequences- `pdb-database` - Protein structures- `kegg-database` - Pathway maps- `reactome-database` - Biological pathways- `brenda-database` - Enzyme data- `drugbank-database` - Drug information- `gene-database` - Gene annotations- `biorxiv-database` - Preprints- `clinicaltrials-database` - Clinical studies### Chemistry & Drug Discovery- `rdkit` - Cheminformatics- `deepchem` - Deep learning for chemistry- `catalyst-chemical` - Reaction modeling### Molecular Simulation- `enzyme-autodiff` - Automatic differentiation for enzymes### Lab & Workflow- `benchling-integration` - Lab notebook- `bioservices` - Web service APIs- `adaptyv` - Adaptive experiments- `biomni` - Multi-omics### Physics & Quantum- `astropy` - Astronomy- `cirq` - Quantum circuits### ML for Science- `scikit-bio` - Biological data science- `scikit-learn` - Machine learning- `scikit-survival` - Survival analysis- `arboreto` - Gene regulatory networks- `aeon` - Time series for science### Categorical- `phylogenetic-operad-acset` - Tree structures as operads## Mutual Awareness Protocol```clojure{:bundle "k-dense-ai" :trit :plus :aware-of ["trailofbits-security"] :interface {:analyze (fn [molecule] "Use rdkit/deepchem") :validate (fn [result] "Delegate to trailofbits for security review") :synthesize (fn [target] "Generate candidates")} :handoff-to "trailofbits-security" :handoff-trigger [:untrusted-input :external-api :file-parsing]}```## Usage```bash# Load bundleskill k-dense-ai# Cross-bundle workflowskill k-dense-ai -> trailofbits-security # Validate drug candidate data pipeline```

Frequently asked questions

K Dense AI