“logging”的搜索结果

根据任务内容、过去的成功模式和预测结果选择负责的智能体与AI模型，支持查看分配理由，并记录完成结果，为后续任务分配提供依据。

• 开始工作前选择智能体和模型
• 查看任务分配的判断理由
• 按固定规则执行支持的代码修改

Run one cycle of autonomous task work in Claude Code: check progress, select and execute the next action, log the work, and schedule another cycle. Stop when all tasks are complete or the iteration limit is reached.

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• Continuing work from a Claude Code task list
• Working through Markdown checklist items
• Running tasks managed through MCP

检查文本、代码和配置中的邮箱、地址、API密钥等个人信息及凭据，标出位置并建议遮盖方式，便于在提交代码、保存日志或发送回复前核查。

• 提交代码前检查个人信息
• 保存日志前检查敏感信息
• 发送模型回复前进行核查

Review how a website behaves when images fail to load and add fallback images or meaningful placeholder content. Check loading behavior, consider loading placeholders, and use error logging and browser developer tools to investigate and verify fixes.

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• Adding fallback images for broken image links
• Reviewing placeholders shown while images load
• Investigating image loading failures with logs and DevTools

Assess an application against OWASP Top 10:2025 risk categories using Strix. Get findings backed by working exploit examples, organized by category, with clear explanations of what was tested and what remains unassessed.

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• Assessing an app against OWASP Top 10:2025
• Testing access controls across users and tenants
• Organizing proven vulnerabilities by risk category

Inspect NSLogger logs from iOS and macOS apps, filter messages by severity, tag, text, or thread, summarize counts, export text, JSON, or CSV, and receive live logs for debugging.

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• Review errors in a captured app session
• Find network failures by tag and message text
• Export logs as JSON or CSV for analysis

Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.

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• Restructure PyTorch training code into LightningModules
• Build reusable data preparation and loading pipelines
• Configure checkpoints, early stopping, and experiment logs

Design, build, debug, and optimize connections to external APIs. The skill covers REST, GraphQL, event notifications through webhooks, authentication, retries, request limits, and logging to help produce integrations whose failures can be traced.

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• Designing a new external API integration
• Building an API client with retries and authentication
• Investigating failed requests or missing results

Design layered protections for AI agents against malicious instructions, tool misuse, and data leakage. Provides guidance and code examples for input checks, isolated tool execution, permission limits, and audit logging to help review and strengthen agent security.

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• Review defenses against malicious instructions
• Isolate tool execution and restrict permissions
• Assess data leakage risks between tenants

Evaluate job offers by separating guaranteed pay, conditional bonuses, and estimated equity value. Compare terms, develop an evidence-based counteroffer, draft emails and call scripts, rehearse recruiter conversations, and prepare deadline extensions, acceptance, or decline messages.

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• Comparing pay and terms across job offers
• Drafting a counteroffer email
• Rehearsing a salary negotiation call

Add retries and timeouts to Python service calls, with increasing and randomized waits between attempts. Use examples to distinguish temporary failures from permanent errors, limit retry duration, and record retries in logs.

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• Adding retries to external API calls
• Setting timeouts for network operations
• Handling rate limits and temporary service failures

Design application and API behavior when operations fail. Choose between exceptions and Result types, which explicitly represent success or failure, and improve retries, error messages, logging, and resource cleanup so failures are easier to handle and investigate.

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• Add error handling to a new feature
• Design API behavior for failures
• Improve error messages and logs for production debugging

Build Node.js APIs and backend services with Express/Fastify. The skill guides authentication, database integration, error handling, and operational practices such as logging and health checks, which report whether a service is working.

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• Build a REST API with Express/Fastify
• Add authentication and database integration
• Organize middleware and error handling

Build Spring Boot applications with guidance on project structure, configuration, APIs, database access, logging, testing, and security. Apply practices such as constructor injection, passing required components through constructors, and consistent handling of validation errors.

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• Planning a Spring Boot project structure
• Implementing API validation and error responses
• Reviewing database access and transactions

Build and troubleshoot C#/.NET MCP servers that expose tools and information to AI agents. Guidance targets the official 2.x SDK and covers transport setup, authentication, interactive features, basic clients, and migration issues caused by changed defaults.

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• Build a C#/.NET MCP server
• Add tools, prompts, and resources
• Diagnose STDIO and HTTP connection errors

Turn a business use case into a Dataverse solution using Python. Get table and relationship designs, implementation code, usage instructions, and recommendations for error recovery, logging, monitoring, and performance based on the expected workload.

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• Designing customer and document management solutions
• Building data migration or scheduled synchronization jobs
• Implementing order processing and approval workflows

Review and improve .NET/C# code against project conventions. Cover public API documentation, dependency injection for supplying services to classes, asynchronous operations, tests, configuration, logging, and input validation, including guidance for Semantic Kernel integrations.

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• Review C# code against project conventions
• Improve XML documentation for public APIs
• Review asynchronous operations and dependency injection

Collect and review SGLang startup logs to identify unnecessary output and trace its source. Get saved logs and specific cleanup proposals while preserving useful diagnostics, with runtime changes left for user approval.

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• Investigating repeated messages in startup logs
• Checking which startup warnings need attention
• Comparing startup logs across models and GPU counts

Investigate SGLang runs that freeze across multiple GPUs. It uses stack traces, communication logs, GPU memory dumps, and comparisons between processes to locate state differences, trace their causes, and guide fixes.

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• Locate where a multi-GPU inference run freezes
• Compare logs from distributed processes
• Trace the first difference in process state

Investigate CUDA crashes in SGLang using logs and inputs saved before execution. Inspect tensor shapes, data types, and numerical anomalies, with guidance for separate process logs and additional CUDA debugging tools.

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• Investigating illegal memory access and device-side assertions
• Tracing NaN or Inf values through kernel calls
• Saving inputs before a CUDA crash

Detect attacks that misuse legitimate Windows tools by examining command arguments, parent and child processes, and network connections. Create Sysmon logging configurations and Sigma detection rules, analyze endpoint logs, and review application controls while accounting for legitimate usage.

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• Creating detection rules for misuse of Windows tools
• Investigating unusual process chains in alerts
• Hunting for fileless attacks in endpoint logs

Build detection rules for suspicious Windows scheduled tasks by correlating Sysmon and Windows Security logs. Examine parent processes, file paths, encoded commands, and remote task creation to uncover persistent access and movement between systems.

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• Investigating suspicious scheduled task creation
• Hunting for tasks that run encoded commands
• Detecting remote task creation across systems

Examine Windows and Active Directory logs for attempts to extract passwords and other credentials. Identify suspicious memory access and database exports, correlate events with user and host context, and produce findings with severity ratings and detection queries.

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• Investigating alerts about suspicious LSASS access
• Finding signs of SAM or NTDS.dit extraction
• Creating detection queries for Splunk or Elastic

Investigate suspected email account takeovers by examining audit logs, sign-ins, inbox rules, and app permissions. Identify suspicious forwarding, unlikely travel between sign-ins, and unusual access, then summarize findings and recommended containment actions in a severity-rated report.

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• Investigating suspected business email compromise
• Checking for suspicious external forwarding rules
• Reviewing unusual sign-ins and app permissions