“deployment”的搜索结果

检查Django应用的登录、权限、输入处理和生产配置，梳理恶意查询、脚本注入及非预期请求的防护，并审查文件上传、API保护、密钥管理和安全日志。

• 审查登录与用户权限
• 上线前检查生产配置
• 审查输入处理与请求防护

梳理持续运行的 AI 智能体的启停、日志监控、权限管理、分阶段更新与回滚流程，支持故障响应，以及成功率、重试次数、恢复时间和成本评估。

• 设计持续运行智能体的运维流程
• 应对失败次数突然增加
• 制定更新和回滚步骤

将静态网站或Web应用的构建产物发布到托管平台，并检查公开URL。流程包含预览审批、git提交与标签版本记录，也支持更新网站或恢复先前发布的版本。

• 发布作品集或文档网站
• 通过URL分享仪表盘或原型
• 记录版本并更新已发布网站

Organize software releases on GitHub, from version suggestions and release notes to testing, builds, and deployment. Coordinate multiple packages or repositories, roll changes out in stages, and configure rollback to restore an earlier release when problems occur.

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• Preparing version updates and release notes
• Coordinating releases across packages and repositories
• Deploying in stages with monitoring and rollback

Create and improve GitHub Actions workflows for automated testing and deployment. Coordinate multiple AI agents to review pull requests, investigate failed runs, scan for security issues, and manage releases across repositories.

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• Create workflows for automated testing and deployment
• Investigate failed runs and suggest fixes
• Coordinate code reviews and security checks

Create and improve GitHub Actions workflows that automate building, testing, and deployment. The skill covers testing across multiple environments, dependency caching, build output management, and security settings such as limited permissions and protected deployment environments.

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• Create a build, test, and deployment workflow
• Run automated tests on pull requests
• Configure tests across multiple environments

Review deployment evidence and recovery conditions when assessing a release. The skill directs the agent to consult the accompanying restore reference and include a timestamp in its decision, providing a narrowly defined release review procedure.

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• Reviewing deployment evidence
• Checking recovery conditions for a release
• Preparing a release decision with a timestamp

Build AI agents with Cline SDK using guidance on choosing APIs, adding custom tools, saving sessions, and handling live events. References also cover model providers, scheduled runs, multi-agent coordination, and production deployment.

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• Build an AI agent with custom tools
• Choose an API for persistent sessions
• Configure model providers and live event handling

Operate an existing Twenty app by syncing changes, managing server connections, building and deploying releases, and inspecting function logs. It also guides troubleshooting and CI/CD, the workflows used to automate testing and deployment.

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• Sync app changes to a Twenty server
• Prepare and deploy an app update
• Investigate failed builds or deployments

Implement .NET workflows for Azure AI Foundry projects, including agent creation, resource connections, dataset uploads, model deployment inspection, search index management, and evaluations. Examples also cover authentication, asynchronous operations, and resource cleanup.

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• Creating and running agents from a .NET application
• Inspecting resource connections and model deployments
• Uploading datasets and managing search indexes

Manage AWS infrastructure as TypeScript code with SST v4 and Pulumi. Guides resource creation, connections between services, infrastructure tests, deployment checks, troubleshooting, and resource migrations while accounting for existing project conventions.

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• Adding AWS resources to an SST v4 application
• Connecting resources and reviewing access permissions
• Adding infrastructure tests and reviewing deployment changes

Build AWS applications that run without managing servers directly. Covers Lambda functions, APIs, DynamoDB storage, message-driven workflows with SQS/SNS, deployment through SAM/CDK, and reducing the delay when a function starts for the first time.

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• Building an API with Lambda and API Gateway
• Developing an application backed by DynamoDB
• Designing event-driven workflows with SQS/SNS

Audit an AI agent's completion claims using existing logs, artifacts, approvals, and deployment records. Reconstruct the run and assess each claim separately, distinguishing proven results, partial support, contradictory evidence, and missing proof without rerunning the work.

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• Checking whether an agent's completion report has supporting evidence
• Reviewing tool calls, retries, and checkpoint recovery
• Matching approvals and deployments to the same artifact

Manage coding work from issue intake through implementation, review, deployment, and acceptance checks. Track each completion requirement against fresh evidence, with defined retry limits and explicit escalation when progress needs human input.

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• Taking a ready issue through implementation and verification
• Coordinating implementation, review, and deployment handoffs
• Checking runtime behavior against acceptance criteria

Investigate AI-agent and cloud infrastructure challenges in a CTF sandbox. Trace untrusted content into tool actions, compare deployment configuration with runtime behavior, and follow software artifacts from source through build and release to their actual consumers.

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• Tracing untrusted content into agent tool actions
• Comparing deployment configuration with live behavior
• Following dependencies and artifacts through CI/CD

Build Helm charts, reusable packages for deploying Kubernetes applications. Organize templates, manage configuration values for different environments, document settings, and validate charts before packaging or installation, with guidance for troubleshooting deployment and dependency problems.

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• Create a Helm chart from scratch
• Package a Kubernetes application for distribution
• Manage deployment settings across environments

Retrieve past operational experience from OpenViking before or during coding, configuration, deployment, or recovery work. It reads relevant records, checks whether their assumptions match the current environment, and applies suitable procedures and checks.

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• Consulting past experience before code changes
• Checking prior procedures for configuration changes
• Finding diagnostic steps after a deployment failure

Design or review distributed systems, where multiple computers work together, using 42 cloud design patterns. Organizes options for reliability, performance, messaging, security, and deployment, emphasizing why to choose each pattern and the trade-offs involved.

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• Review a distributed system architecture
• Choose patterns for failures and recovery
• Compare caching, scaling, and load management approaches

Develop and troubleshoot distributed applications with Aspire. Coordinate services written in different languages through AppHost, configure connections and startup dependencies, inspect logs and metrics, and prepare deployment settings.

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• Creating an Aspire application from a template
• Connecting services written in different languages
• Diagnosing services through logs and traces

Investigate AWS production incidents using reusable CloudWatch log queries and metric calculations. Narrow affected resources, compare alarms with deployment events, and reconstruct a timeline to identify changes that may explain the symptoms.

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• Investigate error spikes, slow responses, and memory exhaustion
• Compare alarm transitions with recent deployments
• Narrow an incident to affected services and resources

Analyze Solidity smart contracts with Slither and Mythril before Ethereum deployment. Combine code inspection with execution path analysis, review potential vulnerabilities, remove duplicate findings, and produce an audit report with severity ratings and suggested fixes.

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• Checking Solidity contracts before deployment
• Investigating reentrancy and access control weaknesses
• Consolidating findings from Slither and Mythril

Build AWS applications with managed services that reduce server administration. Follow patterns for Lambda functions, HTTP APIs, database access, and queued background processing, with guidance on deployment and delays when functions first start.

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• Build HTTP APIs with Lambda and API Gateway
• Read and write application data in DynamoDB
• Process SQS messages with retries and a dead-letter queue

Coordinates specialist agents in Claude Code to work from product requirements through design, implementation, testing, deployment, and operations. Includes task queues, saved progress, parallel reviews, and recovery workflows for managing extended development work.

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• Turning product requirements into development tasks
• Coordinating implementation, testing, and reviews
• Resuming extended work from saved progress

Deploy and manage Vercel projects using access tokens instead of interactive login. Covers selecting the right project and team, preview deployments, environment variables, domains, and checking deployment status and logs.

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• Deploy a preview using an access token
• Add or retrieve environment variables
• Check deployment status and build logs