“responses”的搜索结果

Use the MiniMax API from the command line to generate chat responses and turn text into speech. Select models and voices, adjust speech settings, and save audio files for further use.

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• Generate explanations or writing through chat
• Receive text responses as they are generated
• Save written text as spoken audio

Prepare and review research funding proposals for NSF, NIH, DOE, DARPA, and Taiwan NSTC. Maps application requirements to draft sections, identifies gaps, and helps refine research aims, budgets, impact statements, and responses to reviewers.

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• Map funding-call requirements to proposal sections
• Review research aims against evaluation criteria
• Align budgets and timelines with the research plan

Analyze DepMap cancer model data to rank genes required for cell survival, compare groups defined by biological markers, and explore drug responses. Release tracking and statistical checks help organize evidence for research hypotheses.

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• Ranking gene dependencies in cancer models
• Comparing groups defined by biological markers
• Finding genes with similar dependency patterns

Helps build AI agents in .NET with persistent conversation threads. Covers messages, execution status, streaming responses, document search, and custom function calls using Azure.AI.Agents.Persistent, with examples for creating agents and cleaning up resources.

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• Build an AI assistant that retains conversation threads
• Answer questions using uploaded documents
• Connect an agent to custom functions

Design mock APIs, simulated services that reproduce expected responses, authentication, errors, and delays. Define scenarios and repeatable test data to support frontend development, integration testing, and realistic demos before the real backend is ready.

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• Developing a frontend before the backend is ready
• Simulating a partner or third-party API
• Preparing a demo with realistic API responses

Store and retrieve execution output, model responses, reports, and operation receipts in YYLO Ledger. Choose storage and retention settings, attach provenance describing the source, and verify immutable evidence using record identifiers and content digests.

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• Preserving test reports and execution output
• Recording AI responses with their source information
• Storing operational plans and contracts as evidence

Help an AI coding assistant assess claims independently instead of automatically agreeing with the user. Responses lead with a conclusion and supporting evidence, while disagreements are evaluated for new information that could justify changing the assessment.

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• Review claims without automatic agreement
• Ask for conclusions supported by evidence
• Reassess a position when new evidence appears

Turn a system description into REST API endpoints, the URLs and operations applications use to exchange data. Choose an endpoint list or detailed specifications covering authentication, request fields, responses, error codes, and pagination for lists.

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• Listing the API endpoints a new system needs
• Drafting request and response specifications
• Designing authentication and error responses

Assess claims when a request pushes for agreement, invokes authority, or creates artificial urgency. The skill guides responses that separate evidence from persuasion, present strong counter-evidence first, and clearly state what remains uncertain.

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• Evaluating claims under deadline pressure
• Reviewing requests that invoke authority
• Responding to demands to endorse unsupported claims

Add a new exchange integration to CCXT using TypeScript and established exchange templates. Follow guidance for unified methods, data conversion, authentication, sandbox settings, recorded API test data, documentation, and checks across generated language implementations.

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• Add an exchange that CCXT does not yet support
• Convert exchange responses into CCXT's common data format
• Record real API responses for regression tests

Generate API documentation from source code, including OpenAPI/Swagger specifications. Describe endpoints, the addresses used to access API operations, with parameters, responses, errors, authentication guidance, and examples in cURL, JavaScript, and Python.

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• Creating API documentation from source code
• Preparing OpenAPI/Swagger specifications
• Documenting parameters, responses, and errors

Check an MCP server before release through a real client session. Compare available features with source code and documentation, test invalid requests and installation, and produce a verdict backed by reproducible commands and responses.

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• Checking an MCP server before release
• Comparing live features with source code and documentation
• Testing invalid requests and error responses

Connect an AI agent to Power Automate through FlowStudio MCP. This skill covers authentication, discovering available tools, reusable Python or Node.js helpers, and extracting useful data from responses too large to display directly.

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• Set up authentication for a Power Automate connection
• Discover available tools for a task
• Build a reusable Python or Node.js helper

Build ASP.NET Minimal API endpoints with explicit request and response types, input validation, and standard error responses. Organize related endpoints and add OpenAPI documentation, a structured description of API operations, parameters, and schemas.

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• Building Minimal API endpoints with explicit types
• Adding operation and parameter descriptions to OpenAPI
• Organizing related endpoints with MapGroup()

Improve LLM prompts using Arize production traces, evaluation results, and human feedback. Extract current instructions, identify recurring failures, revise the prompt, and compare experiments to measure changes in output quality and catch regressions.

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• Improve a prompt using failed production traces
• Fix responses that use the wrong output format
• Review unsupported answers in retrieval-based responses

Investigate service account misuse by examining unexpected interactive logons, privilege changes, and movement between systems. Correlate endpoint and security logs, distinguish suspicious activity from legitimate operations, and document evidence, risk, and recommended responses.

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• Investigating unexpected interactive logons by service accounts
• Checking service account access outside its assigned scope
• Tracing suspicious movement between systems after hours

Build automated responses to AWS GuardDuty security findings using EventBridge and Lambda. Configure severity filters, isolate EC2 instances, disable compromised access keys, preserve disk snapshots for investigation, and send notifications to security teams.

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• Triggering responses to high-severity GuardDuty findings
• Isolating EC2 instances and preserving investigation data
• Disabling compromised IAM access keys

Set up and operate Amazon GuardDuty to detect threats in AWS. Configure protection features, centralize findings across accounts, investigate suspicious activity, and build automated responses with EventBridge and Lambda, including containment of potentially compromised resources.

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• Setting up threat detection for AWS accounts
• Investigating credential abuse and compromised instances
• Automating responses to high-severity findings

Plan and run authorized security tests that simulate deceptive emails, calls, text messages, and in-person approaches. Measure employee responses, compare results across test methods, and report gaps in security awareness training and access controls.

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• Planning an authorized social engineering assessment
• Measuring responses to simulated phishing
• Evaluating phone and physical access checks

Set up host-based intrusion detection to monitor changes on Windows and Linux endpoints. Configure Wazuh agents, file integrity checks, registry monitoring, alert rules, and automated responses, with guidance on exclusions and centralized security monitoring.

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• Deploying Wazuh agents on Windows and Linux
• Monitoring critical files and registry changes
• Planning change detection for compliance requirements

Choose how an API, the interface software uses to exchange data, should work. Covers REST, GraphQL, and tRPC selection, consistent responses, pagination, versioning, authentication, request limits, documentation, and security review considerations.

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• Choosing between REST, GraphQL, and tRPC
• Standardizing API responses, errors, and pagination
• Planning authentication, request limits, and versioning

Guide language model training with TRL, from learning to follow instructions to matching human preferences. Covers method selection, training data, reward models that score responses, evaluation, and adjustments for memory limits or unstable training.

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• Train a model on instruction and response examples
• Align responses using preferred and rejected answer pairs
• Build a training pipeline using human feedback

Build safety checks for LLM prompts and responses using Meta's LlamaGuard moderation model. Classify potentially harmful content, block flagged messages, serve checks through an API, and integrate them with NeMo Guardrails.

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• Checking prompts before sending them to an LLM
• Filtering responses before displaying them
• Building a moderation API

Build workflows that extract structured data from LLM responses, check field types and values with Pydantic, and retry failed extractions. Includes examples for nested data, classification, and receiving results as they are generated.

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• Extracting contact and company details from text
• Classifying articles into predefined categories
• Checking review analysis against validation rules