Explore AI Agent Skills & Claude Prompts

manage-volumes

by LiorZ

star 3

Manage Vast.ai persistent storage volumes — search, create, delete, clone, and attach volumes to instances. Use for persistent data across instance lifecycles.

autoscale

by LiorZ

star 3

Manage Vast.ai autoscaling endpoints and worker groups for production deployments. Use when setting up auto-scaling GPU inference, managing worker pools, or deploying services.

vastai

by LiorZ

star 3

Vast.ai GPU marketplace reference. Auto-invoked when the user discusses renting GPUs, launching GPU instances, searching for machines, vast.ai pricing, managing cloud GPU workloads, volumes, templates, autoscaling, SSH keys, or any vast.ai topic.

manage-instances

by LiorZ

star 3

Manage Vast.ai GPU instances — show status, start, stop, destroy, SSH, execute commands, view logs, copy files, take snapshots. Use when the user wants to check on, connect to, transfer files, or control their GPU instances.

invrotzyme

by LiorZ

star 2

Build inverse-rotamer active-site assemblies from a Rosetta matcher/enzdes constraint (CST) file using PyRosetta. Use this skill when: (1) Constructing theozyme / active-site stubs (catalytic sidechains placed around a ligand) as starting points for de novo enzyme design, (2) Preparing inputs for **RFdiffusion All-Atom** (RFdiffusionAA) enzyme design pipelines — outputs include `REMARK 666` enzdes records so they drop straight into the published heme-binder-diffusion workflow, (3) Exhaustively enumerating clash-free combinations of catalytic rotamers + small idealized helix/strand backbone stubs around a small-molecule substrate / cofactor, (4) Hosting one catalytic residue inside an externally-provided **motif PDB** (e.g. a CYS loop from a cytochrome P450) while still enumerating the other CST residues as inverse rotamers, (5) Filtering rotamers by Dunbrack cumulative probability (per-CST or global), per-CST secondary structure, and per-CST random subsampling to control co

bindcraft

by LiorZ

star 2

Run BindCraft — a hallucination-based de novo binder design pipeline that couples **AF2 backpropagation** (ColabDesign), **ProteinMPNN** redesign, **AF2 reprediction**, and a full **PyRosetta** interface-score filter pass against a single target PDB. One `bindcraft.py` (or `sbatch bindcraft.slurm`) call runs an endless trajectory loop until the requested number of designs that pass every filter is reached. Use this skill when: (1) De novo designing **mini-protein binders** (~65–150 aa) against a protein target — the canonical BindCraft use case, (2) Designing **peptide binders** (≤ ~25 aa) using the dedicated `peptide_3stage_multimer` advanced preset + `peptide_filters`, (3) Designing for a **β-sheet-rich** target or a target where the binder *should* be β-sheet (`betasheet_4stage_multimer` preset), (4) Selecting a **hotspot patch** (`target_hotspot_residues`: `"56"`, `"A1-10,B1-20"`, `"A"`, or `null`) to focus AF2 on a specific epitope, (5) Producing AF2-validated designs ranked by **interfac

fair-esm

by LiorZ

star 2

Run the FAIR `fair-esm` package — the original Meta Fundamental AI Research reference implementation of the ESM family of protein language and structure models. Use this skill when: (1) Extracting per-residue or per-sequence embeddings from a protein language model (ESM-2 6 variants from 8M → 15B, ESM-1b, ESM-1v, ESM-MSA), (2) Predicting protein 3D structure end-to-end from a single sequence with **ESMFold** (`esm-fold` CLI or `model.infer_pdb()`), (3) Designing sequences for a fixed backbone — fixed-backbone (a.k.a. inverse-folding) sequence design with **ESM-IF1** (`GVPTransformer`, single-chain or multi-chain complex), (4) Scoring conditional log-likelihoods of candidate sequences against a backbone (variant ranking, design scoring, perplexity), (5) Zero-shot variant effect prediction on deep mutational scans with **ESM-1v** ensembles or ESM-MSA (wt-marginals, masked-marginals, pseudo-perplexity), (6) Unsupervised contact prediction from attention maps (`return_contacts=True

esm-biohub

by LiorZ

star 2

Run the **Biohub `esm` repository** (formerly EvolutionaryScale) — the world model of protein biology that ships **ESMC** (state-of-the-art protein language model), **ESMFold2** (AF3-class structure prediction with proteins + DNA + RNA + ligands), **ESM3** (generative model over sequence / structure / function), and **ESMC Sparse Autoencoders** (interpretable feature decomposition of ESMC's internal representations). Use this skill when: (1) Producing **per-residue or pooled embeddings** of one or many sequences with ESMC (300M / 600M / 6B) for downstream classification, regression, retrieval, clustering, or representation learning, (2) **Zero-shot mutation scoring / pseudo-perplexity / entropy** on a sequence by sampling masked logits (the headline ESMC use case for variant-effect prediction), (3) **Fine-tuning** ESMC for a task (PEFT / LoRA classification or regression head) or doing a **layer sweep** to find the best feature layer for a downstream probe, (4) Predicting **all-atom 3D

vast-ai-cli

by LiorZ

star 1

Vast.ai CLI workflows for searching GPU offers, creating and managing instances, handling volumes and data transfer, and troubleshooting authentication or command errors. Use when Codex needs to run or compose vastai/vast.py commands, interpret results, or automate Vast.ai marketplace tasks.