incident-response-smart-fix by sickn33/antigravity-awesome-skills
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill incident-response-smart-fix[扩展思考:此工作流实现了一个复杂的调试与解决管道,它利用AI辅助调试工具和可观测性平台来系统地诊断和解决生产环境问题。该智能调试策略将自动化根本原因分析与人类专业知识相结合,采用了包括AI代码助手(GitHub Copilot、Claude Code)、可观测性平台(Sentry、DataDog、OpenTelemetry)、用于回归追踪的git bisect自动化,以及分布式追踪和结构化日志等生产环境安全调试技术在内的2024/2025年现代实践。该流程遵循严格的四阶段方法:(1) 问题分析阶段 - 错误侦探和调试器智能体分析错误追踪、日志、复现步骤和可观测性数据,以理解故障的完整上下文,包括上游/下游影响;(2) 根本原因调查阶段 - 调试器和代码审查员智能体执行深度代码分析、自动化git bisect以识别引入问题的提交、依赖兼容性检查以及状态检查,以隔离确切的故障机制;(3) 修复实施阶段 - 领域特定智能体(python-pro、typescript-pro、rust-expert等)实施最小化修复,并包含单元测试、集成测试和边界情况测试在内的全面测试覆盖,同时遵循生产环境安全实践;(4) 验证阶段 - 测试自动化器和性能工程师智能体运行回归测试套件、性能基准测试、安全扫描,并验证未引入新问题。跨越多个系统的复杂问题需要在专家智能体(数据库优化器 → 性能工程师 → devops故障排除员)之间进行协同协调,并明确传递上下文和共享状态。该工作流强调理解根本原因而非仅仅处理症状,实施持久的架构改进,通过增强的监控和告警实现自动化检测,并通过类型系统增强、静态分析规则和改进的错误处理模式来预防未来发生。成功的衡量标准不仅在于问题解决,还在于平均恢复时间(MTTR)的缩短、类似问题的预防以及系统弹性的提升。]
resources/implementation-playbook.md。resources/implementation-playbook.md 包含详细的模式和示例。每周安装次数
90
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触达数万 AI 开发者,精准高效
GitHub 星标数
27.6K
首次出现时间
2026年1月28日
安全审计
安装于
opencode86
gemini-cli85
cursor84
codex82
claude-code81
github-copilot81
[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024/2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for regression tracking, and production-safe debugging techniques like distributed tracing and structured logging. The process follows a rigorous four-phase approach: (1) Issue Analysis Phase - error-detective and debugger agents analyze error traces, logs, reproduction steps, and observability data to understand the full context of the failure including upstream/downstream impacts, (2) Root Cause Investigation Phase - debugger and code-reviewer agents perform deep code analysis, automated git bisect to identify introducing commit, dependency compatibility checks, and state inspection to isolate the exact failure mechanism, (3) Fix Implementation Phase - domain-specific agents (python-pro, typescript-pro, rust-expert, etc.) implement minimal fixes with comprehensive test coverage including unit, integration, and edge case tests while following production-safe practices, (4) Verification Phase - test-automator and performance-engineer agents run regression suites, performance benchmarks, security scans, and verify no new issues are introduced. Complex issues spanning multiple systems require orchestrated coordination between specialist agents (database-optimizer → performance-engineer → devops-troubleshooter) with explicit context passing and state sharing. The workflow emphasizes understanding root causes over treating symptoms, implementing lasting architectural improvements, automating detection through enhanced monitoring and alerting, and preventing future occurrences through type system enhancements, static analysis rules, and improved error handling patterns. Success is measured not just by issue resolution but by reduced mean time to recovery (MTTR), prevention of similar issues, and improved system resilience.]
resources/implementation-playbook.md.resources/implementation-playbook.md for detailed patterns and examples.Weekly Installs
90
Repository
GitHub Stars
27.6K
First Seen
Jan 28, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
opencode86
gemini-cli85
cursor84
codex82
claude-code81
github-copilot81
Azure 升级评估与自动化工具 - 轻松迁移 Functions 计划、托管层级和 SKU
104,900 周安装