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Set up, update, and authenticate the Boltz command-line tool. Check installation and version status, resolve command lookup problems, and recover from login failures or restrictions imposed by the agent’s execution environment.
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Automate Bolt Iot tasks through Rube MCP, a connection between an AI agent and external tools. The skill finds available operations, checks the service connection, and uses current input requirements to run tasks.
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Automate Bolt Iot tasks through Rube MCP, a connection between an AI agent and external tools. The skill finds available operations, checks the service connection, and uses current input requirements to run tasks.
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Inspect existing Boltz jobs, check local download progress, and recover results after an interrupted session. Use run names or job IDs to resume downloads into the existing output directory without starting a new job.
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Estimate small molecule solubility, permeability, and lipophilicity with Boltz using SMILES, a text notation for molecular structures. Process individual molecules or lists, review costs before submission, and receive results mapped to each input.
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Generate new protein, peptide, antibody, or nanobody candidates that bind a chosen target using Boltz. The workflow covers target exploration, design setup, cost estimates, job submission, result downloads, and ranking by predicted binding confidence.
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Use when an admin or partner needs to package an Experience Cloud (Community) site as a reusable Lightning Bolt Solution for distribution — covers the export workflow from Experience Builder, what gets bundled (ExperienceBundle, custom apps, flow categories, theme, layouts, navigation menus) versus what does NOT (data, CMS content, files), choosing Bolt vs managed package vs unlocked package vs cloning a site, sandbox-to-production promotion, multi-org distribution, AppExchange listing as a Bolt, and template versioning via the LightningBolt metadata `versionNumber`. Triggers: 'turn this community into a reusable template', 'package an Experience Cloud site to ship to multiple orgs', 'export Experience Builder template for AppExchange', 'should we use a Bolt or a managed package for this community', 'create an industry-specific community starter', 'how do we version our partner portal template', 'distribute branded Experience site across business units'. NOT for general Experience Cloud site build, content, or member setup (use admin/experience-cloud-site-setup, admin/experience-cloud-cms-content, admin/experience-cloud-member-management). NOT for shipping Apex / LWC / data-model functionality as a product (use devops/managed-package-development, devops/second-generation-managed-packages, devops/unlocked-package-development). NOT for moving a single Experience site between sandbox and prod as a one-off (use devops/experience-cloud-deployment-admin, devops/cicd-for-experience-cloud).
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Screen supplied proteins, peptides, antibodies, or nanobodies against a target with Boltz. Prepares inputs, obtains a cost estimate for approval, downloads results, and ranks candidates by binding confidence, a score for predicted binding reliability.
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Screen an existing compound library against a protein target with Boltz. Rank candidates by binding confidence or lead optimization score, download predicted structures, and review solubility, permeability, and lipophilicity results alongside the ranked hits.
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Generate new small molecules intended to bind a target protein with Boltz. The workflow covers cost estimation, job submission, result downloads, and ranking candidates by binding confidence or a score for improving promising molecules.
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Cofold protein-ligand, protein-protein, and nucleic-acid complexes with Boltz-2, and predict binding affinity with its trained affinity head. Use this skill to build Boltz input YAML, run structure prediction with MSAs, pocket constraints, templates, and modified residues, screen compound libraries by cofolding, and interpret confidence scores (pLDDT, pTM, ipTM, PDE) and affinity output (binder probability and log10 IC50). Also trigger on Boltz, Boltz-1, Boltz-2, cofolding, boltz predict, affinity_pred_value, affinity_probability_binary, ipTM, or open-weights AlphaFold3 alternatives.
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Predict the structure and binding of one defined protein, RNA, DNA, or ligand complex with Boltz. The workflow covers cost confirmation, job submission, result downloads, and summaries of structure and binding metrics.
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Use when working on the Bolt repository and you need to configure or compile code correctly and efficiently. This skill defines the canonical Bolt build workflow, including when and how to run `make`, `cmake --build ...`, and when build reconfiguration is necessary due to added files and test or build target changes.
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Use when building high-performance APIs with django-bolt - routing, JWT auth, msgspec serializers, WebSockets, SSE streaming, file uploads, ORM patterns, runbolt deployment, or migrating from DRF
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Expert Bolt (StackBlitz Bolt, full-stack AI, WebContainer, prototypage rapide)
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Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative with optional binding-affinity prediction.
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Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.
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Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-design-qc. For AlphaFold2 prediction, use alphafold2-multimer. For Chai prediction, use chai1-structure-prediction.
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All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only generation, use rfdiffusion. For sequence-only design, use proteinmpnn. For structure validation, use boltz.
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Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use protein-qc. For AlphaFold2 prediction, use alphafold. For Chai prediction, use chai.
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You are an expert in Bolt.new by StackBlitz, the AI-powered full-stack development environment that runs entirely in the browser. You help developers go from idea to deployed app in minutes using natural language prompts — Bolt generates complete applications with frontend, backend, database, and deployment, all running in a WebContainer without local setup.
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Run protein structure prediction, molecular design, and docking tools on Tamarind Bio without a local GPU. Guides tool selection, input validation, job submission, batch processing, and result retrieval through its REST API or MCP server.
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NOTE: molecule and target inputs and your NGC_API_KEY are transmitted to external NVIDIA-hosted API endpoints on every call. Use local NIM containers for confidential or proprietary data. Run a complete computational drug discovery pipeline using NVIDIA BioNeMo NIMs: generate drug-like molecules with GenMol, dock them to a protein target with DiffDock, then predict binding affinity with Boltz2. Use this skill whenever the user wants to generate and screen small molecule drug candidates, perform hit discovery, optimize leads against a protein target, or do virtual screening combining molecule generation, docking, and affinity prediction. Triggers on: drug discovery pipeline, hit discovery, lead optimization, virtual screening, molecule generation, molecular docking, binding affinity, GenMol, DiffDock, Boltz2, SMILES, SAFE notation, NIM microservice. This is a multi-step pipeline composing three BioNeMo NIMs.
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Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
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