Better prompts.
Useful results.

Write a PHP function that registers a custom AI provider for {provider_name} using the php‑ai‑client library, and hook it into the connector settings with a placeholder for {api_key}.

I am building a WordPress plugin to generate images in the Media Library. Write the PHP function to configure the AI Client prompt for a text prompt: '[prompt_text]', with an optional aspect ratio of '[orientation]' (square, landscape, or portrait). Use `FileTypeEnum::inline()` to get the image as base64 data, and set a model preference order of 'gpt-5.4', 'gemini-3.1-pro-preview', then fallback to any available model. Include a support check to ensure image generation is available before executing.

Create an app that shows [metric] for [time period] broken down by [dimension] and allows [action] (e.g., send email, view details) when a record is selected.

Set up Amazon Bedrock AgentCore with Web Search integration for Claude Desktop. First, create an Amazon Cognito user pool named 'agentcore-websearch-pool' with email as a verified attribute. Then create a SAML application in AWS IAM Identity Center pointing to this pool. Register IAM Identity Center as a SAML IdP in the Cognito pool. Create a Cognito app client called 'agentcore-websearch-client' with a generated secret and the SAML IdP as the supported identity provider. Finally, create an AgentCore Gateway named 'websearch-gateway' with protocolType 'MCP', authorizerType 'CUSTOM_JWT', and attach the 'web-search' connector. Configure Claude Desktop to connect to this gateway using JWT-based inbound authentication with the Cognito client ID and secret.

Generate IaC for {{application_name}} using {{internal_module_library}} that complies with {{policy_set_version}} and includes {{required_tags}}.

Implement Amazon S3 Vectors as a custom memory provider for the NVIDIA NeMo Agent Toolkit. Create a S3VectorsMemoryConfig class extending MemoryBaseConfig with vector_bucket, index_name, and aws_region. Implement an S3VectorsMemoryEditor that generates embeddings using Amazon Titan Text Embeddings V2, stores vectors in the specified S3 Vectors index, and supports semantic search with filters on agent_id, memory_type, ticker, team_id, and is_shared. Register the plugin via the builder and integrate it into a NAT agent workflow using the auto_memory_agent wrapper. Ensure the implementation handles metadata truncation (limit content to 1024 chars) and respects per-tenant isolation. Example use case: multi-agent investment research where agents share long-term memory indexed under a team_id.

Create a Python class `MultiObjectiveLinUCB` that takes `n_arms`, `n_features`, `alpha`, and `weights`. Implement `select_arm(context)` returning the arm with highest weighted UCB sum, and `update(arm, context, rewards)` where `rewards` is a list of per‑stage rewards. Include placeholders `{n_arms}`, `{n_features}`, `{alpha}`, `{weights}`.

Describe the image at {image_url} and generate a WordPress post titled {title} with content {content} using a {tone} tone.

Run a compliance sweep for {state} leases as of {date} using the sweep_compliance tool and return the completeness receipt, total counts, and a link to the QuickSight dashboard for detailed findings.

Evaluate the agent session {session_id} using the explainability evaluator {evaluator_arn} and return a score plus feedback on decision rationale, evidence attribution, constraint reasoning, trade‑off explanation, tool‑use explainability, and assumption disclosure.

Downgrade the QuickSight user {{username}} with email {{email}} from Admin to Reader using the AWS CLI step‑down method, assuming AWS account ID {{account_id}} and region {{region}}.

migrate_resources --source-account {{SOURCE_ACCOUNT_ID}} --target-account {{TARGET_ACCOUNT_ID}} --resource-type {{AGENT|CONNECTOR|KNOWLEDGE_BASE|FLOW|SPACE}} --selector {{ID|NAME|ALL}} --region {{AWS_REGION}} --source-env {{SOURCE_ENV}} --target-env {{TARGET_ENV}} --quick-sight-role {{QUICK_SIGHT_SERVICE_ROLE}}