gh-dansasser-claude-code-marketplace-plugins-claude-ollama-agents/commands/deep-analyze.md

Deep Multi-Perspective Analysis

Comprehensive analysis using parallel orchestrator with multiple perspectives.

Usage: /deep-analyze <file> [perspectives]

Perspectives: (comma-separated, max 4)

• security: Security vulnerabilities and threat modeling
• architecture: Design patterns and structural analysis
• implementation: Code quality and best practices
• testing: Test coverage and validation strategies
• performance: Bottlenecks and optimization opportunities

Examples:

• /deep-analyze implementation-plan.md - Auto-select perspectives
• /deep-analyze src/auth.py security,testing - Focus on security and testing
• /deep-analyze architecture.md architecture,scalability - Architecture focused

---

You are performing deep multi-perspective analysis using the parallel orchestrator agent.

Target: $1 Perspectives: ${2:-auto}

Your Process:

1. Validate Target:

   • Verify file/directory exists (use Read/Glob tools)
   • Check size and estimate tokens
   • Ensure suitable for deep analysis (not trivial files)

2. Determine Perspectives:

   Auto-Select (if perspectives=$ARGUMENTS or empty): Based on file type:

   • Code files (.py, .js, .ts, etc.): security, quality, testing
   • Architecture docs: architecture, scalability, security
   • Implementation plans: security, architecture, implementation
   • API specs: security, architecture, performance

   User-Specified: Parse comma-separated list from $2 Validate 2-4 perspectives

3. Invoke Parallel Orchestrator Agent:

   Use Task tool to invoke ollama-parallel-orchestrator:

   • Target file: $1
   • Perspectives: Parsed list (2-4 perspectives)
   • Agent will:
     • Decompose into parallel analyses
     • Execute concurrently
     • Track sessions
     • Synthesize results

4. Perspectives Explained:

   Security:

   • Vulnerabilities and attack vectors
   • Threat modeling
   • Authentication/authorization
   • Input validation
   • Secrets management

   Architecture:

   • Design patterns
   • Structural organization
   • Separation of concerns
   • Modularity and coupling
   • Scalability considerations

   Implementation:

   • Code quality and readability
   • Best practices adherence
   • Error handling
   • Edge case coverage
   • Refactoring opportunities

   Testing:

   • Test coverage assessment
   • Testing strategy
   • Edge cases and corner cases
   • Integration points
   • Test quality

   Performance:

   • Bottleneck identification
   • Algorithm efficiency
   • Resource utilization
   • Caching opportunities
   • Optimization recommendations

5. Your Role:

   • Invoke ollama-parallel-orchestrator agent via Task tool
   • Receive comprehensive synthesized analysis
   • Format report for user
   • Highlight critical findings
   • Present prioritized recommendations

6. Expected Report Format (from agent):

   # Deep Analysis Report
   
   **Target:** $1
   **Perspectives:** [list]
   **Orchestration ID:** [id]
   
   ## Executive Summary
   [High-level summary across all perspectives]
   
   ## Critical Findings
   ### Security Critical
   - [Issues requiring immediate attention]
   
   ### Architecture Critical
   - [Structural issues with major impact]
   
   ### Implementation Critical
   - [Code quality issues needing urgent fix]
   
   ## Analysis by Perspective
   [Detailed findings from each perspective]
   
   ## Cross-Perspective Insights
   [Common themes and patterns]
   
   ## Prioritized Recommendations
   1. [Highest priority]
   2. [Second priority]
   ...
   
   ## Next Steps
   [Actionable items]
   

7. Session Tracking:

   • Agent saves results to ~/.claude/orchestrations/[id].json
   • Session includes all perspective analyses
   • Synthesis strategy applied
   • Full audit trail maintained

When to Use Deep Analysis:

• Comprehensive code reviews
• Architecture decision making
• Security audits
• Pre-production validation
• Complex refactoring planning
• Technical debt assessment

When NOT to Use:

• Simple file reviews (use /analyze instead)
• Quick checks (use /review quick)
• Small files < 100 lines
• Trivial changes

Token Efficiency:

• Deep analysis delegates to ollama-parallel-orchestrator
• Saves ~70% of Claude's context
• Enables multiple comprehensive analyses per session
• Parallel execution faster than sequential

Remember: This invokes the most comprehensive analysis. The parallel orchestrator handles all complexity. You just present the synthesized results clearly.