Results for “ens”

Automate continuous integration, deployment, and post-deployment verification of developed solutions. Use when setting up CI/CD pipelines, deploying to staging/production environments, running automated tests in CI, or verifying deployments. Ensures reproducible builds and reliable deployments.

Forces web searches and reading of official docs before implementing, integrating, or debugging third-party APIs, libraries, and services to ensure accuracy and avoid guesswork.

Runs a multi-phase verification pipeline including build, type-check, lint, tests, security scan, and diff review to ensure code quality before creating a PR.

Define, deploy, and manage cloud infrastructure as code using tools like Terraform, Pulumi, CloudFormation, and CDK, ensuring consistency, repeatability, and version control. Use when the user requests infrastructure as code or provides relevant inputs for this workflow.

Validate backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to ensure backups are reliable for disaster recovery and ransomware response scenarios.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Builds AWS Nitro Enclave-based confidential computing environments with cryptographic attestation, KMS policy integration, and secure vsock communication for processing sensitive data.

AWS infrastructure management — EKS, ECR, VPC, RDS, ElastiCache, S3, Route53, ACM, Secrets Manager, CloudWatch, IAM

Scaffolds or audits Zod-validated environment variables for a Next.js service, including src/lib/env.ts, .env.example, scripts/check-env.ts, and pnpm check:env. Use when adding env vars, auditing process.env usage, or aligning with deployment secrets.

Create and manage SNS topics, subscriptions, and message publishing for pub/sub messaging patterns.

Audit environment-variable hygiene and secrets safety across local development and production with practical scanning, drift awareness, and rotation readiness.

Run ESM protein language models — ESM3 for generative multimodal protein design across sequence, structure, and function, and ESM C for efficient embeddings and representations — locally or via the cloud Forge API. Use when working with protein sequences, structures, or function prediction, designing novel proteins, generating protein embeddings, performing inverse folding, or doing protein-engineering tasks. Part of the AlterLab Academic Skills suite.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.

Build serverless background jobs, event-driven workflows, and durable execution with Inngest, without managing queues or workers.

Enforce security guardrails across GCP resource hierarchy by configuring organization policy constraints to restrict risky configurations and ensure compliance at organization, folder, and project levels.

Orchestrate a content-driven development workflow for AEM Edge Delivery Services, ensuring code is built against real content with author-friendly content models.

Run cloud-native molecular modeling and drug-design workflows via a Python API, covering pKa prediction, docking, conformer and tautomer ensembles, molecular dynamics, and related small-molecule or protein tasks without local HPC infrastructure.

Infer gene regulatory networks (GRNs) from expression matrices using arboreto's scalable GRNBoost2 and GENIE3 tree-ensemble algorithms with Dask-distributed computation. Use when analyzing bulk or single-cell RNA-seq transcriptomics to map transcription-factor-to-target-gene regulatory interactions, build adjacency networks, or run the GRN-inference step of a SCENIC pipeline on large datasets. Part of the AlterLab Academic Skills suite.

Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.