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/smart-fix

From dev-accelerator
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Description

Intelligently fix the issue using automatic agent selection with explicit Task tool invocations:

Model
opus
Namespace
workflows/
Command Content

Intelligently fix the issue using automatic agent selection with explicit Task tool invocations:

[Extended thinking: This workflow analyzes the issue and automatically routes to the most appropriate specialist agent(s). Complex issues may require multiple agents working together.]

First, analyze the issue to categorize it, then use Task tool with the appropriate agent:

Analysis Phase

Examine the issue: "$ARGUMENTS" to determine the problem domain.

Agent Selection and Execution

For Deployment/Infrastructure Issues

If the issue involves deployment failures, infrastructure problems, or DevOps concerns:

  • Use Task tool with subagent_type="devops-troubleshooter"
  • Prompt: "Debug and fix this deployment/infrastructure issue: $ARGUMENTS"

For Code Errors and Bugs

If the issue involves application errors, exceptions, or functional bugs:

  • Use Task tool with subagent_type="debugger"
  • Prompt: "Analyze and fix this code error: $ARGUMENTS. Provide root cause analysis and solution."

For Database Performance

If the issue involves slow queries, database bottlenecks, or data access patterns:

  • Use Task tool with subagent_type="database-optimizer"
  • Prompt: "Optimize database performance for: $ARGUMENTS. Include query analysis, indexing strategies, and schema improvements."

For Application Performance

If the issue involves slow response times, high resource usage, or performance degradation:

  • Use Task tool with subagent_type="performance-engineer"
  • Prompt: "Profile and optimize application performance issue: $ARGUMENTS. Identify bottlenecks and provide optimization strategies."

For Legacy Code Issues

If the issue involves outdated code, deprecated patterns, or technical debt:

  • Use Task tool with subagent_type="legacy-modernizer"
  • Prompt: "Modernize and fix legacy code issue: $ARGUMENTS. Provide migration path and updated implementation."

Multi-Domain Coordination

For complex issues spanning multiple domains:

  1. Use primary agent based on main symptom
  2. Use secondary agents for related aspects
  3. Coordinate fixes across all affected areas
  4. Verify integration between different fixes

Issue: $ARGUMENTS

Stats
Stars21
Forks1
Last CommitJan 23, 2026

Other plugins with /smart-fix

/smart-fix

[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 2025/2026 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.]

incident-response
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/smart-fix

[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.]

incident-response
0