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Enforces consistent structured logging with request correlation IDs, standardized log schema, middleware integration, and best practices. Use for "structured logging", "log standardization", "request tracing", or "log correlation".
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检查Java Spring Boot服务的身份认证、访问权限和输入验证等安全措施，涵盖秘密信息管理、日志、API调用频率限制及依赖库漏洞。
在Django发布或提交变更提案前，依次检查环境、代码质量、数据库变更、测试、漏洞及配置，汇总各阶段是否通过和需要修复的问题。
Enable debug logging in the current Claude Code session, inspect errors and warnings after reproducing a problem, and get a plain-language explanation of findings with concrete fixes or next steps.
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Set up AWS CloudTrail to record activity across accounts, protect audit logs, and alert on sensitive operations. Includes guidance for querying past events with Athena or CloudTrail Lake and checking log integrity during investigations.
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Implement and review API security within an application's existing authentication and data model. Covers token verification, resource permissions, input validation, request limits, secrets, and logging, with concrete checks for unauthorized access and invalid requests.
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Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.
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Guide the design and review of software that handles personal data using GDPR engineering practices. Covers minimal data collection, access controls, logging, encryption, retention, deletion, and checks for code changes and infrastructure.
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Generate Python code for Dataverse integrations with error handling, retries, logging, and shared connection management. Include type hints, usage examples, and queries that retrieve only the records and columns needed for the task.
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Strengthen LDAP and Active Directory services by reviewing encrypted connections, signing, channel binding, access controls, and audit logging. Use the guidance to organize security settings, validate them in a test environment, and document implementation evidence.
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Analyze Windows PowerShell event logs to reconstruct scripts split across records and identify encoded commands, suspicious downloads, and security scanning bypass attempts. Produce a report with recovered scripts, risk scores, and mappings to documented attack techniques.
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Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.
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Handle website tasks in Dot’s Firefox browser, including logging in, reading pages, filling forms, and clicking through workflows. Separate browser profiles preserve each account’s cookies and login state between sessions.
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Audit Python SDK logs, exceptions, and telemetry for sensitive data exposure. Trace values to their sources, fix confirmed leaks, and test that redaction hides sensitive information while preserving the caller's expected behavior.
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Review Google Cloud operations and develop guidance for monitoring, incident response, resource sizing, and automated deployments. Use framework-based questions and checklists to identify gaps in production readiness and plan ongoing operational improvements.
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Set up an organization-wide Google Cloud foundation with 17 baseline security policies, environment folders and projects, billing links, and centralized audit logging and monitoring. Check the resulting setup against a defined validation checklist.
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Diagnose GKE nodes marked NotReady or Unknown using node conditions, events, logs, and resource metrics. The skill gathers evidence, distinguishes temporary transitions from faults, and proposes fixes for a person to apply.
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Investigate crashes, out-of-memory errors, and device initialization failures on GKE TPU v6e nodes. The skill checks logs and custom metrics collection for conflicting operations, then guides temporary mitigation by disabling an identified collector.
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Helps implement iii workers in Node.js, browsers, Python, and Rust with language-specific guidance on installation, function registration, calls, data channels, and logging. Explains package choices and connection constraints for each environment.
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Launch machine learning experiments on local GPUs, remote servers, Vast.ai, or Modal. It checks the configured environment, deploys code, saves logs, and verifies startup, with optional W&B metric logging and automatic Vast.ai result collection and instance cleanup.
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Track machine learning experiments with SwanLab by recording training settings, metrics, and media such as images and audio. Learn to compare runs, inspect training charts, and connect existing training code to local, cloud, or self-hosted dashboards.
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Organize PyTorch training code with LightningModule and Trainer. This skill guides training, validation, logging, model checkpoint saving, early stopping when progress stalls, and configuration for running the same model across multiple GPUs.
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Save web pages as clean Markdown with source details such as title, author, and capture date. Chrome renders JavaScript content, while a wait mode lets you finish logging in or loading content before capture.
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Audit or fix sensitive-data exposure in JS SDK diagnostics, exceptions, logging, and telemetry.
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