“structure”的搜索结果

Set up NVIDIA infrastructure for synthetic data generation on MicroK8s or Azure AKS. Coordinate Kubernetes, inference services, and OSMO workflow execution, with prerequisite checks, deployment validation, capacity planning, and failure investigation before workloads are retried.

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• Set up a synthetic data generation environment on MicroK8s
• Deploy an Azure AKS environment with OSMO and inference services
• Check GPU quotas and capacity before scaling

NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.

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Set up Havoc infrastructure for authorized attack simulations, including its command server, traffic listeners, Nginx relay servers, and Windows agents. The skill also covers communication profiles and operational precautions for running the infrastructure.

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• Preparing Havoc infrastructure for an authorized exercise
• Reviewing server and traffic relay separation
• Configuring communication profiles for an exercise

Build a Python system that follows DNS history, domain registration records, and certificate information from known suspicious domains or IP addresses. Map related attack infrastructure, identify shared connections, and monitor for newly discovered domains matching known patterns.

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• Finding related domains from a known suspicious IP
• Mapping attacker command-and-control infrastructure
• Monitoring domains that match known naming patterns

Look up existing predictions and protein information in the AlphaFold Protein Structure Database. Retrieve concise structure metadata, UniProt summaries, sequence summaries, and annotations, with an option to save the full response for later inspection.

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• Look up an existing protein structure prediction
• Check a UniProt summary
• Review sequence summaries and annotations

Use when the user wants to set up, scale, validate, or harden NVIDIA physical AI infrastructure for synthetic data generation workflows across local MicroK8s or Azure AKS, including Kubernetes clusters, inference endpoint deployment, OSMO deployment, workload submission readiness, and infrastructure failure recovery. Trigger keywords: physical ai infrastructure, resilient scaling, SDG infrastructure, microk8s, azure aks, NVCF deployment, NIM Operator, OSMO deploy, workflow scaling. Don't trigger for: OSMO log summarization or workload-only operations unless infrastructure setup, scaling, validation, or recovery is requested.

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Design the structure of a website or product including sitemap, navigation, URL structure, content types, taxonomy, and labeling. Use this skill whenever the user asks to plan a sitemap, design navigation, structure URLs, define content types, build taxonomies, design site search, or organize content at the system level. Triggers on sitemap, site structure, navigation, IA, information architecture, URL structure, content types, taxonomy, categorization, breadcrumbs, hub pages, faceted navigation, site search, labeling. Also triggers when content is being created without a structural plan, or when an existing site's structure is being audited or restructured.

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Query AlphaFold protein structure predictions. Use when user asks about protein structure, 3D structure, protein folding, or structure prediction. Triggers on "alphafold", "protein structure", "3D structure", "folding", "pLDDT", "structure prediction".

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Provision and manage cloud infrastructure using Terraform with HCL modules, remote state backends, workspaces, and plan/apply workflow. Implement infrastructure as code patterns with variable management, output values, and state locking for team collaboration. Use when provisioning new cloud infrastructure, migrating from ClickOps or CloudFormation to declarative IaC, managing multi-environment infrastructure, versioning infrastructure changes alongside application code, or enforcing standards through reusable modules.

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Plan a website's pages, hierarchy, navigation, URLs, and internal links. Produce page trees, visual sitemaps, URL tables, and navigation specifications to guide a new site or reorganize an existing one around visitor needs and business goals.

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• Plan the page structure for a new website
• Reorganize navigation on an existing site
• Design URLs and internal links for a blog section

Add or repair structured data, labels that help search engines understand page content. The skill selects suitable schema types, prepares JSON-LD code, and provides validation checks while distinguishing supported Google search features from retired ones.

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• Add structured data to product or article pages
• Find and fix errors in existing markup
• Implement breadcrumb structured data

Build reusable Terraform modules for AWS, Azure, GCP, and OCI. Organize cloud infrastructure as code with documented inputs, validation, usage examples, provider version constraints, and Terratest tests for consistent provisioning.

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• Create reusable cloud infrastructure modules
• Standardize Terraform conventions across a team
• Connect network and database modules

Build threat actor profiles from public reports, social media, code repositories, and other intelligence sources. Correlates indicators, maps related infrastructure and attack techniques, and produces structured dossiers with confidence assessments about who may be responsible.

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• Consolidating public information about an attacker group
• Comparing aliases, targets, and motivations across sources
• Mapping related domains, IP addresses, and infrastructure

Audit Terraform infrastructure definitions and planned changes for excessive permissions, public exposure, and missing encryption. Use security scanners and custom rules to produce actionable findings and add automated checks before cloud infrastructure is deployed.

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• Reviewing Terraform modules and plans before deployment
• Adding security checks to Terraform CI/CD pipelines
• Enforcing organization-specific infrastructure rules

Audit, create, and fix structured data, the labels that help search systems understand web pages. Get JSON-LD code for articles, products, and other page types, plus validation findings and guidance on where to place it.

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• Adding structured data to articles or product pages
• Investigating structured data errors in Search Console
• Auditing existing markup and prioritizing fixes

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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• Predicting the structure of a defined molecular complex
• Docking one ligand to a protein
• Assessing protein–protein binding

Query RCSB PDB for experimental protein structures. Use when user asks about crystal structures, X-ray, cryo-EM, NMR structures, or PDB IDs. Triggers on "pdb", "crystal structure", "cryo-em", "x-ray structure", "protein crystal", "experimental structure".

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Structure similarity search with Foldseek. Use this skill when: (1) Finding similar structures in PDB/AFDB databases, (2) Structural homology search, (3) Database queries by 3D structure, (4) Finding remote homologs not detected by sequence, (5) Clustering structures by similarity. For sequence similarity, use uniprot BLAST. For structure prediction, use chai or boltz.

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Build and navigate DSP (Data Structure Protocol) — graph-based long-term structural memory of codebases for LLM agents. Stores entities (modules, functions), their dependencies (imports), public API (shared/exports), and reasons for every connection. Use when: (1) project has a .dsp/ directory, (2) user asks to set up DSP or bootstrap project structure, (3) creating/modifying/deleting code files in a DSP-tracked project, (4) navigating project structure, understanding dependencies, or finding modules, (5) user mentions DSP, dsp-cli, .dsp, or structure mapping.

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Manages cloud infrastructure through natural language conversations with Pulumi Neo, an AI agent for platform engineers. Enables infrastructure analysis, resource provisioning, stack deployment, and configuration management via conversational AI. Use when creating Neo tasks, requesting infrastructure analysis, automating cloud deployments, managing infrastructure as code (IaC), provisioning AWS/Azure/GCP resources, managing infrastructure through natural language prompts, reviewing PRs with Neo, handling Neo approval workflows, or checking Neo task status and events. Also use when the user mentions "Pulumi Neo", "Neo task", "Neo agent", or wants AI-assisted infrastructure management.

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Deep comparison of A2A protocol, MCP, OpenAI function calling, and LangChain message types. Teaches when to use rigid schemas vs free-form with validation, typed vs untyped, streaming vs batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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Check article pages for structured data, machine-readable details that help search engines understand content. Review required properties, add Article metadata in JSON-LD format, and verify the rendered page and HTTP response for specific implementation gaps.

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• Checking structured data on blog articles
• Adding Article metadata in JSON-LD format
• Finding missing headlines, images, dates, or authors

Read, analyze, modify, and visualize molecular structures with RDKit in Python. Calculate molecular properties, search for similar structures or specific structural patterns, apply reaction rules, and generate 2D or 3D coordinates with detailed control over processing.

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• Calculating properties for a collection of molecules
• Searching for molecules with similar structures
• Filtering molecules by specific structural patterns