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incident-response-smart-fix skill

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This skill guides intelligent issue resolution with multi-agent orchestration, applying AI-assisted debugging, observability, and automated root cause analysis

This is most likely a fork of the incident-response-smart-fix skill from xfstudio

npx playbooks add skill sickn33/antigravity-awesome-skills --skill incident-response-smart-fix

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SKILL.md

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---
name: incident-response-smart-fix
description: "[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res"
---

# Intelligent Issue Resolution with Multi-Agent Orchestration

[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.]

## Use this skill when

- Working on intelligent issue resolution with multi-agent orchestration tasks or workflows
- Needing guidance, best practices, or checklists for intelligent issue resolution with multi-agent orchestration

## Do not use this skill when

- The task is unrelated to intelligent issue resolution with multi-agent orchestration
- You need a different domain or tool outside this scope

## Instructions

- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open `resources/implementation-playbook.md`.

## Resources

- `resources/implementation-playbook.md` for detailed patterns and examples.

Overview

This skill implements an AI-driven, multi-agent pipeline for diagnosing and resolving production incidents. It combines observability, automated root-cause analysis, git bisect automation, and domain-specific code agents to deliver minimal, tested fixes and verification. The workflow emphasizes durable remediation, reduced MTTR, and prevention through monitoring and static checks.

How this skill works

The skill orchestrates specialist agents across four phases: Issue Analysis, Root Cause Investigation, Fix Implementation, and Verification. Agents ingest traces, logs, repro steps, and telemetry from observability platforms, run automated bisects and dependency checks, propose minimal changes, and generate tests. Final verification runs regression suites, benchmarks, and security scans before recommending deployment and monitoring improvements.

When to use it

Diagnosing production errors that require cross-system context (services, DBs, queues).
Automating regression identification and the commit that introduced a bug via git bisect.
Implementing minimal, well-tested fixes with automated verification before rollout.
Coordinating multiple specialists (DB, infra, performance, security) for complex incidents.
Designing post-incident prevention: alerts, static checks, and type/system improvements.

Best practices

Clarify incident scope, SLO impact, and safe testing environments up front.
Feed complete observability context: traces, structured logs, error timestamps, and related deploys.
Prefer minimal, reversible fixes with comprehensive unit and integration tests.
Automate git bisect and dependency compatibility checks to reduce manual search time.
Enforce production-safe debugging: read-only state inspection where possible, feature-flags for fixes, and gradual rollouts.
Capture remediation steps and convert lessons into monitoring rules, static analysis, and runbook updates.

Example use cases

A critical API regression appeared after a deploy—use bisect automation to find the introducing commit, apply a minimal fix, and run integration tests before canary rollout.
Memory leak across a microservice chain—use distributed tracing and profiler agents to pinpoint the offender, implement fixes with performance tests, and update alerts.
Intermittent DB deadlocks—coordinate database-optimizer and devops-troubleshooter agents to analyze queries, propose schema or index changes, and verify under load.
Dependency upgrade caused failing edge-case behavior—use compatibility checks, patch code, add tests, and create an alert for similar future incidents.

FAQ

What inputs are required to run this workflow?

Provide observability data (traces, logs), repro steps or failing test, recent deploy/commit history, and access to a safe debugging environment or read-only production telemetry.

How does the skill ensure fixes are safe for production?

It enforces minimal changes, comprehensive automated tests, feature flags or canary rollouts, and a verification phase with regression, perf, and security scans before recommending full deployment.