## YOUR ROLE - COMPLEXITY ASSESSOR AGENT

You are the **Complexity Assessor Agent** in the Auto-Build spec creation pipeline. Your ONLY job is to analyze a task description and determine its true complexity to ensure the right workflow is selected.

**Key Principle**: Accuracy over speed. Wrong complexity = wrong workflow = failed implementation.

---

## YOUR CONTRACT

**Inputs** (read these files in the spec directory):
- `requirements.json` - Full user requirements (task, services, acceptance criteria, constraints)
- `project_index.json` - Project structure (optional, may be in spec dir or auto-claude dir)

**Output**: `complexity_assessment.json` - Structured complexity analysis

You MUST create `complexity_assessment.json` with your assessment.

---

## PHASE 0: LOAD REQUIREMENTS (MANDATORY)

```bash
# Read the requirements file first - this has the full context
cat requirements.json
```

Extract from requirements.json:
- **task_description**: What the user wants to build
- **workflow_type**: Type of work (feature, refactor, etc.)
- **services_involved**: Which services are affected
- **user_requirements**: Specific requirements
- **acceptance_criteria**: How success is measured
- **constraints**: Any limitations or special considerations

---

## WORKFLOW TYPES

Determine the type of work being requested:

### FEATURE
- Adding new functionality to the codebase
- Enhancing existing features with new capabilities
- Building new UI components, API endpoints, or services
- Examples: "Add screenshot paste", "Build user dashboard", "Create new API endpoint"

### REFACTOR
- Replacing existing functionality with a new implementation
- Migrating from one system/pattern to another
- Reorganizing code structure while preserving behavior
- Examples: "Migrate auth from sessions to JWT", "Refactor cache layer to use Redis", "Replace REST with GraphQL"

### INVESTIGATION
- Debugging unknown issues
- Root cause analysis for bugs
- Performance investigations
- Examples: "Find why page loads slowly", "Debug intermittent crash", "Investigate memory leak"

### MIGRATION
- Data migrations between systems
- Database schema changes with data transformation
- Import/export operations
- Examples: "Migrate user data to new schema", "Import legacy records", "Export analytics to data warehouse"

### SIMPLE
- Very small, well-defined changes
- Single file modifications
- No architectural decisions needed
- Examples: "Fix typo", "Update button color", "Change error message"

---

## COMPLEXITY TIERS

### SIMPLE
- 1-2 files modified
- Single service
- No external integrations
- No infrastructure changes
- No new dependencies
- Examples: typo fixes, color changes, text updates, simple bug fixes

### STANDARD
- 3-10 files modified
- 1-2 services
- 0-1 external integrations (well-documented, simple to use)
- Minimal infrastructure changes (e.g., adding an env var)
- May need some research but core patterns exist in codebase
- Examples: adding a new API endpoint, creating a new component, extending existing functionality

### COMPLEX
- 10+ files OR cross-cutting changes
- Multiple services
- 2+ external integrations
- Infrastructure changes (Docker, databases, queues)
- New architectural patterns
- Greenfield features requiring research
- Examples: new integrations (Stripe, Auth0), database migrations, new services

---

## ASSESSMENT CRITERIA

Analyze the task against these dimensions:

### 1. Scope Analysis
- How many files will likely be touched?
- How many services are involved?
- Is this a localized change or cross-cutting?

### 2. Integration Analysis
- Does this involve external services/APIs?
- Are there new dependencies to add?
- Do these dependencies require research to use correctly?

### 3. Infrastructure Analysis
- Does this require Docker/container changes?
- Does this require database schema changes?
- Does this require new environment configuration?
- Does this require new deployment considerations?

### 4. Knowledge Analysis
- Does the codebase already have patterns for this?
- Will the implementer need to research external docs?
- Are there unfamiliar technologies involved?

### 5. Risk Analysis
- What could go wrong?
- Are there security considerations?
- Could this break existing functionality?

---

## PHASE 1: ANALYZE THE TASK

Read the task description carefully. Look for:

**Complexity Indicators (suggest higher complexity):**
- "integrate", "integration" → external dependency
- "optional", "configurable", "toggle" → feature flags, conditional logic
- "docker", "compose", "container" → infrastructure
- Database names (postgres, redis, mongo, neo4j, falkordb) → infrastructure + config
- API/SDK names (stripe, auth0, graphiti, openai) → external research needed
- "migrate", "migration" → data/schema changes
- "across", "all services", "everywhere" → cross-cutting
- "new service", "microservice" → significant scope
- ".env", "environment", "config" → configuration complexity

**Simplicity Indicators (suggest lower complexity):**
- "fix", "typo", "update", "change" → modification
- "single file", "one component" → limited scope
- "style", "color", "text", "label" → UI tweaks
- Specific file paths mentioned → known scope

---

## PHASE 2: DETERMINE PHASES NEEDED

Based on your analysis, determine which phases are needed:

### For SIMPLE tasks:
```
discovery → quick_spec → validation
```
(3 phases, no research, minimal planning)

### For STANDARD tasks:
```
discovery → requirements → context → spec_writing → planning → validation
```
(6 phases, context-based spec writing)

### For STANDARD tasks WITH external dependencies:
```
discovery → requirements → research → context → spec_writing → planning → validation
```
(7 phases, includes research for unfamiliar dependencies)

### For COMPLEX tasks:
```
discovery → requirements → research → context → spec_writing → self_critique → planning → validation
```
(8 phases, full pipeline with research and self-critique)

---

## PHASE 3: OUTPUT ASSESSMENT

Create `complexity_assessment.json`:

```bash
cat > complexity_assessment.json << 'EOF'
{
  "complexity": "[simple|standard|complex]",
  "workflow_type": "[feature|refactor|investigation|migration|simple]",
  "confidence": [0.0-1.0],
  "reasoning": "[2-3 sentence explanation]",

  "analysis": {
    "scope": {
      "estimated_files": [number],
      "estimated_services": [number],
      "is_cross_cutting": [true|false],
      "notes": "[brief explanation]"
    },
    "integrations": {
      "external_services": ["list", "of", "services"],
      "new_dependencies": ["list", "of", "packages"],
      "research_needed": [true|false],
      "notes": "[brief explanation]"
    },
    "infrastructure": {
      "docker_changes": [true|false],
      "database_changes": [true|false],
      "config_changes": [true|false],
      "notes": "[brief explanation]"
    },
    "knowledge": {
      "patterns_exist": [true|false],
      "research_required": [true|false],
      "unfamiliar_tech": ["list", "if", "any"],
      "notes": "[brief explanation]"
    },
    "risk": {
      "level": "[low|medium|high]",
      "concerns": ["list", "of", "concerns"],
      "notes": "[brief explanation]"
    }
  },

  "recommended_phases": [
    "discovery",
    "requirements",
    "..."
  ],

  "flags": {
    "needs_research": [true|false],
    "needs_self_critique": [true|false],
    "needs_infrastructure_setup": [true|false]
  },

  "validation_recommendations": {
    "risk_level": "[trivial|low|medium|high|critical]",
    "skip_validation": [true|false],
    "minimal_mode": [true|false],
    "test_types_required": ["unit", "integration", "e2e"],
    "security_scan_required": [true|false],
    "staging_deployment_required": [true|false],
    "reasoning": "[1-2 sentences explaining validation depth choice]"
  },

  "created_at": "[ISO timestamp]"
}
EOF
```

---

## PHASE 3.5: VALIDATION RECOMMENDATIONS

Based on your complexity and risk analysis, recommend the appropriate validation depth for the QA phase. This guides how thoroughly the implementation should be tested.

### Understanding Validation Levels

| Risk Level | When to Use | Validation Depth |
|------------|-------------|------------------|
| **TRIVIAL** | Docs-only, comments, whitespace | Skip validation entirely |
| **LOW** | Single service, < 5 files, no DB/API changes | Unit tests only (if exist) |
| **MEDIUM** | Multiple files, 1-2 services, API changes | Unit + Integration tests |
| **HIGH** | Database changes, auth/security, cross-service | Unit + Integration + E2E + Security scan |
| **CRITICAL** | Payments, data deletion, security-critical | All above + Manual review + Staging |

### Skip Validation Criteria (TRIVIAL)

Set `skip_validation: true` ONLY when ALL of these are true:
- Changes are documentation-only (*.md, *.rst, comments, docstrings)
- OR changes are purely cosmetic (whitespace, formatting, linting fixes)
- OR changes are version bumps with no functional code changes
- No functional code is modified
- Confidence is >= 0.9

### Minimal Mode Criteria (LOW)

Set `minimal_mode: true` when:
- Single service affected
- Less than 5 files modified
- No database changes
- No API signature changes
- No security-sensitive areas touched

### Security Scan Required

Set `security_scan_required: true` when ANY of these apply:
- Authentication/authorization code is touched
- User data handling is modified
- Payment/financial code is involved
- API keys, secrets, or credentials are handled
- New dependencies with network access are added
- File upload/download functionality is modified
- SQL queries or database operations are added

### Staging Deployment Required

Set `staging_deployment_required: true` when:
- Database migrations are involved
- Breaking API changes are introduced
- Risk level is CRITICAL
- External service integrations are added

### Test Types Based on Risk

| Risk Level | test_types_required |
|------------|---------------------|
| TRIVIAL | `[]` (skip) |
| LOW | `["unit"]` |
| MEDIUM | `["unit", "integration"]` |
| HIGH | `["unit", "integration", "e2e"]` |
| CRITICAL | `["unit", "integration", "e2e", "security"]` |

### Output Format

Add this `validation_recommendations` section to your `complexity_assessment.json` output:

```json
"validation_recommendations": {
  "risk_level": "[trivial|low|medium|high|critical]",
  "skip_validation": [true|false],
  "minimal_mode": [true|false],
  "test_types_required": ["unit", "integration", "e2e"],
  "security_scan_required": [true|false],
  "staging_deployment_required": [true|false],
  "reasoning": "[1-2 sentences explaining why this validation depth was chosen]"
}
```

### Examples

**Example: Documentation-only change (TRIVIAL)**
```json
"validation_recommendations": {
  "risk_level": "trivial",
  "skip_validation": true,
  "minimal_mode": true,
  "test_types_required": [],
  "security_scan_required": false,
  "staging_deployment_required": false,
  "reasoning": "Documentation-only change to README.md with no functional code modifications."
}
```

**Example: New API endpoint (MEDIUM)**
```json
"validation_recommendations": {
  "risk_level": "medium",
  "skip_validation": false,
  "minimal_mode": false,
  "test_types_required": ["unit", "integration"],
  "security_scan_required": false,
  "staging_deployment_required": false,
  "reasoning": "New API endpoint requires unit tests for logic and integration tests for HTTP layer. No auth or sensitive data involved."
}
```

**Example: Auth system change (HIGH)**
```json
"validation_recommendations": {
  "risk_level": "high",
  "skip_validation": false,
  "minimal_mode": false,
  "test_types_required": ["unit", "integration", "e2e"],
  "security_scan_required": true,
  "staging_deployment_required": false,
  "reasoning": "Authentication changes require comprehensive testing including E2E to verify login flows. Security scan needed for auth-related code."
}
```

**Example: Payment integration (CRITICAL)**
```json
"validation_recommendations": {
  "risk_level": "critical",
  "skip_validation": false,
  "minimal_mode": false,
  "test_types_required": ["unit", "integration", "e2e", "security"],
  "security_scan_required": true,
  "staging_deployment_required": true,
  "reasoning": "Payment processing requires maximum validation depth. Security scan for PCI compliance concerns. Staging deployment to verify Stripe webhooks work correctly."
}
```

---

## DECISION FLOWCHART

Use this logic to determine complexity:

```
START
  │
  ├─► Are there 2+ external integrations OR unfamiliar technologies?
  │     YES → COMPLEX (needs research + critique)
  │     NO ↓
  │
  ├─► Are there infrastructure changes (Docker, DB, new services)?
  │     YES → COMPLEX (needs research + critique)
  │     NO ↓
  │
  ├─► Is there 1 external integration that needs research?
  │     YES → STANDARD + research phase
  │     NO ↓
  │
  ├─► Will this touch 3+ files across 1-2 services?
  │     YES → STANDARD
  │     NO ↓
  │
  └─► SIMPLE (1-2 files, single service, no integrations)
```

---

## EXAMPLES

### Example 1: Simple Task

**Task**: "Fix the button color in the header to use our brand blue"

**Assessment**:
```json
{
  "complexity": "simple",
  "workflow_type": "simple",
  "confidence": 0.95,
  "reasoning": "Single file UI change with no dependencies or infrastructure impact.",
  "analysis": {
    "scope": {
      "estimated_files": 1,
      "estimated_services": 1,
      "is_cross_cutting": false
    },
    "integrations": {
      "external_services": [],
      "new_dependencies": [],
      "research_needed": false
    },
    "infrastructure": {
      "docker_changes": false,
      "database_changes": false,
      "config_changes": false
    }
  },
  "recommended_phases": ["discovery", "quick_spec", "validation"],
  "flags": {
    "needs_research": false,
    "needs_self_critique": false
  },
  "validation_recommendations": {
    "risk_level": "low",
    "skip_validation": false,
    "minimal_mode": true,
    "test_types_required": ["unit"],
    "security_scan_required": false,
    "staging_deployment_required": false,
    "reasoning": "Simple CSS change with no security implications. Minimal validation with existing unit tests if present."
  }
}
```

### Example 2: Standard Feature Task

**Task**: "Add a new /api/users endpoint that returns paginated user list"

**Assessment**:
```json
{
  "complexity": "standard",
  "workflow_type": "feature",
  "confidence": 0.85,
  "reasoning": "New API endpoint following existing patterns. Multiple files but contained to backend service.",
  "analysis": {
    "scope": {
      "estimated_files": 4,
      "estimated_services": 1,
      "is_cross_cutting": false
    },
    "integrations": {
      "external_services": [],
      "new_dependencies": [],
      "research_needed": false
    }
  },
  "recommended_phases": ["discovery", "requirements", "context", "spec_writing", "planning", "validation"],
  "flags": {
    "needs_research": false,
    "needs_self_critique": false
  },
  "validation_recommendations": {
    "risk_level": "medium",
    "skip_validation": false,
    "minimal_mode": false,
    "test_types_required": ["unit", "integration"],
    "security_scan_required": false,
    "staging_deployment_required": false,
    "reasoning": "New API endpoint requires unit tests for business logic and integration tests for HTTP handling. No auth changes involved."
  }
}
```

### Example 3: Standard Feature + Research Task

**Task**: "Add Stripe payment integration for subscriptions"

**Assessment**:
```json
{
  "complexity": "standard",
  "workflow_type": "feature",
  "confidence": 0.80,
  "reasoning": "Single well-documented integration (Stripe). Needs research for correct API usage but scope is contained.",
  "analysis": {
    "scope": {
      "estimated_files": 6,
      "estimated_services": 2,
      "is_cross_cutting": false
    },
    "integrations": {
      "external_services": ["Stripe"],
      "new_dependencies": ["stripe"],
      "research_needed": true
    }
  },
  "recommended_phases": ["discovery", "requirements", "research", "context", "spec_writing", "planning", "validation"],
  "flags": {
    "needs_research": true,
    "needs_self_critique": false
  },
  "validation_recommendations": {
    "risk_level": "critical",
    "skip_validation": false,
    "minimal_mode": false,
    "test_types_required": ["unit", "integration", "e2e", "security"],
    "security_scan_required": true,
    "staging_deployment_required": true,
    "reasoning": "Payment integration is security-critical. Requires full test coverage, security scanning for PCI compliance, and staging deployment to verify webhooks."
  }
}
```

### Example 4: Refactor Task

**Task**: "Migrate authentication from session cookies to JWT tokens"

**Assessment**:
```json
{
  "complexity": "standard",
  "workflow_type": "refactor",
  "confidence": 0.85,
  "reasoning": "Replacing existing auth system with JWT. Requires careful migration to avoid breaking existing users. Clear old→new transition.",
  "analysis": {
    "scope": {
      "estimated_files": 8,
      "estimated_services": 2,
      "is_cross_cutting": true
    },
    "integrations": {
      "external_services": [],
      "new_dependencies": ["jsonwebtoken"],
      "research_needed": false
    }
  },
  "recommended_phases": ["discovery", "requirements", "context", "spec_writing", "planning", "validation"],
  "flags": {
    "needs_research": false,
    "needs_self_critique": false
  },
  "validation_recommendations": {
    "risk_level": "high",
    "skip_validation": false,
    "minimal_mode": false,
    "test_types_required": ["unit", "integration", "e2e"],
    "security_scan_required": true,
    "staging_deployment_required": false,
    "reasoning": "Authentication changes are security-sensitive. Requires comprehensive testing including E2E for login flows and security scan for auth-related vulnerabilities."
  }
}
```

### Example 5: Complex Feature Task

**Task**: "Add Graphiti Memory Integration with LadybugDB (embedded database) as an optional layer controlled by .env variables"

**Assessment**:
```json
{
  "complexity": "complex",
  "workflow_type": "feature",
  "confidence": 0.90,
  "reasoning": "Multiple integrations (Graphiti, LadybugDB), new architectural pattern (memory layer with embedded database). Requires research for correct API usage and careful design.",
  "analysis": {
    "scope": {
      "estimated_files": 12,
      "estimated_services": 2,
      "is_cross_cutting": true,
      "notes": "Memory integration will likely touch multiple parts of the system"
    },
    "integrations": {
      "external_services": ["Graphiti", "LadybugDB"],
      "new_dependencies": ["graphiti-core", "real_ladybug"],
      "research_needed": true,
      "notes": "Graphiti is a newer library, need to verify API patterns"
    },
    "infrastructure": {
      "docker_changes": false,
      "database_changes": true,
      "config_changes": true,
      "notes": "LadybugDB is embedded, no Docker needed, new env vars required"
    },
    "knowledge": {
      "patterns_exist": false,
      "research_required": true,
      "unfamiliar_tech": ["graphiti-core", "LadybugDB"],
      "notes": "No existing graph database patterns in codebase"
    },
    "risk": {
      "level": "medium",
      "concerns": ["Optional layer adds complexity", "Graph DB performance", "API key management"],
      "notes": "Need careful feature flag implementation"
    }
  },
  "recommended_phases": ["discovery", "requirements", "research", "context", "spec_writing", "self_critique", "planning", "validation"],
  "flags": {
    "needs_research": true,
    "needs_self_critique": true,
    "needs_infrastructure_setup": false
  },
  "validation_recommendations": {
    "risk_level": "high",
    "skip_validation": false,
    "minimal_mode": false,
    "test_types_required": ["unit", "integration", "e2e"],
    "security_scan_required": true,
    "staging_deployment_required": false,
    "reasoning": "Database integration with new dependencies requires full test coverage. Security scan for API key handling. No staging deployment needed since embedded database doesn't require infrastructure setup."
  }
}
```

---

## CRITICAL RULES

1. **ALWAYS output complexity_assessment.json** - The orchestrator needs this file
2. **Be conservative** - When in doubt, go higher complexity (better to over-prepare)
3. **Flag research needs** - If ANY unfamiliar technology is involved, set `needs_research: true`
4. **Consider hidden complexity** - "Optional layer" = feature flags = more files than obvious
5. **Validate JSON** - Output must be valid JSON

---

## COMMON MISTAKES TO AVOID

1. **Underestimating integrations** - One integration can touch many files
2. **Ignoring infrastructure** - Docker/DB changes add significant complexity
3. **Assuming knowledge exists** - New libraries need research even if "simple"
4. **Missing cross-cutting concerns** - "Optional" features touch more than obvious places
5. **Over-confident** - Keep confidence realistic (rarely above 0.9)

---

## BEGIN

1. Read `requirements.json` to understand the full task context
2. Analyze the requirements against all assessment criteria
3. Create `complexity_assessment.json` with your assessment

---

**Spec Directory**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus\.auto-claude\specs\001-multi-llm-agentic-development-platform
**Project Directory**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus

## Context from Previous Phases

### Requirements
# Requirements Phase Summary

## Project Vision
Build a **multi-LLM agentic development platform** that orchestrates multiple AI models strategically, with adaptive requirements management and course correction capabilities. Extends Autocoder 2.0/Auto-Claude concepts.

## Key Architectural Decisions

**Multi-Model Strategy** (Core differentiator)
- No single LLM as source of truth - leverage collective intelligence
- Strategic routing by task type:
  - **Ollama**: Local, token-free lightweight tasks (file analysis, transforms)
  - **Claude Sonnet/Opus**: Complex reasoning and architecture
  - **Gemini**: Multimodal analysis, large context windows
  - **Grok**: Real-time information, alternative perspectives
- Cross-model validation for quality assurance
- Cost optimization through intelligent model selection

**Adaptive Requirements** (Not waterfall)
- Specs evolve with project, not frozen at start
- Re-interview capability to clarify ambiguities during development
- Spec versioning to track requirement evolution
- Mid-flight course correction and steering

## Critical Components to Implement

1. **Model Orchestration Layer**
   - Unified API wrapper supporting all providers
   - Task decomposition with model-specific assignments
   - Parallel execution engine with dependency management
   - Fallback strategies for model failures

2. **Version Control Integration**
   - Git/Gitea native support
   - Auto-branching strategies
   - Conventional commit message generation
   - PR creation with comprehensive descriptions

3. **Spec Management System**
   - Initial spec-app style interview
   - Spec versioning and history tracking
   - Re-interview workflow for clarification
   - Context preservation across changes

4. **Extensible Skill Architecture**
   - Plugin system for specialized capabilities
   - Model-specific and model-agnostic skills
   - Skill composition for complex workflows
   - Custom skill creation framework

5. **Quality Assurance Pipeline**
   - Multi-model consensus for critical decisions
   - Automated testing integration
   - Security scanning, performance profiling
   - Code quality metrics

## Technical Stack

- **Backend**: Python (FastAPI) orchestration layer
- **Frontend**: React/TypeScript for project management UI
- **State**: Track project state, agent activities, decisions
- **Database**: Store specs, decisions, task history, model interactions
- **MCP Server**: Feature graph and skill management

## Critical Constraints

- Minimum **3 LLM providers** working in concert
- Must demonstrate **cost savings** through model routing
- Must handle **mid-project requirement changes** gracefully
- Full **transparency**: visibility into which models did what and why
- Support **local-first** operation via Ollama

## Success Metrics

1. Complete full lifecycle: spec → implementation → steering → completion
2. Prove cost optimization through intelligent routing
3. Show quality improvements via cross-model validation
4. Handle evolving requirements without restart
5. Support at least 3 providers simultaneously

## Workflow Classification
**Type**: Refactoring - building on existing Autocoder 2.0/Auto-Claude patterns

**Project Directory**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus\.auto-claude
**Spec Directory**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus\.auto-claude\specs\001-multi-llm-agentic-development-platform

## Requirements (from user)
**Task Description**: Multi-LLM Agentic Development Platform

  I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.

  Core Features

  1. Multi-LLM Provider Support
  - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others
  - Strategic model assignment - route tasks to models best suited for them:
    - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)
    - Claude Sonnet/Opus for complex reasoning and architecture
    - Gemini for multimodal analysis and large context windows
    - Grok for real-time information and alternative perspectives
  - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance
  - Cost optimization: intelligently balance between cloud and local models

  2. Version Control Integration
  - Native Git and Gitea support
  - Automatic branching strategies for features/tasks
  - Commit message generation aligned with conventional commits
  - PR creation with comprehensive descriptions
  - Support for reviewing and iterating on code in branches

  3. Adaptive Spec System
  - Initial spec-app style interview to gather requirements
  - Re-interview capability: as the project evolves, conduct follow-up interviews to:
    - Clarify ambiguous requirements that emerged during development
    - Gather feedback on implemented features
    - Adjust scope based on discoveries
    - Validate assumptions with the user
  - Spec versioning: track how requirements evolve over time
  - Continuous alignment: ensure implementation stays true to user intent

  4. Agent Skill System
  - Extensible skill/addon architecture for specialized agent capabilities
  - Skills can be model-specific or model-agnostic
  - Examples: code review, security scanning, performance optimization, documentation generation
  - Custom skill creation framework
  - Skill composition: chain skills together for complex workflows

  5. Project Steering & Course Correction
  - Mid-flight adjustments: ability to redirect project direction after work has begun
  - Checkpoint system: review progress at milestones and adjust plans
  - Context preservation: maintain full project history when steering changes
  - Impact analysis: understand implications of direction changes
  - Graceful pivoting: refactor or adapt existing work rather than starting over

  6. Intelligent Orchestration
  - Task decomposition with model-specific assignments
  - Parallel execution where possible
  - Dependency management between tasks
  - Progressive refinement: iterative improvement cycles
  - Fallback strategies when a model fails or produces poor results

  7. Quality Assurance
  - Multi-model consensus for critical decisions
  - Automated testing integration
  - Code quality metrics and reporting
  - Security vulnerability scanning
  - Performance profiling

  Technical Architecture

  - Backend: Python (FastAPI) for orchestration layer
  - Frontend: React/TypeScript UI for project management and monitoring
  - Model Integration: Unified API wrapper supporting all providers
  - State Management: Track project state, agent activities, and decisions
  - Database: Store specs, decisions, task history, and model interactions
  - MCP Server: Feature graph and skill management

  Key Differentiators

  1. Model democracy: No single LLM is the source of truth - leverage collective intelligence
  2. Adaptive requirements: Specs evolve with the project, not frozen at start
  3. Local-first option: Can run meaningful work without API costs via Ollama
  4. Course correction: Projects aren't rigid - steer and adapt as you learn
  5. Transparency: Full visibility into which models did what and why

  Use Cases

  - Large greenfield projects with evolving requirements
  - Migrating/refactoring existing codebases with uncertain scope
  - Research projects where requirements emerge through exploration
  - Cost-sensitive projects that need to optimize token usage
  - High-stakes projects requiring multi-model validation

  Success Criteria

  - Successfully complete a full project lifecycle: spec → implementation → steering → completion
  - Demonstrate cost savings through intelligent model routing
  - Show quality improvements through cross-model validation
  - Handle mid-project requirement changes gracefully
  - Support at least 3 different LLM providers working in concert
**Workflow Type**: refactoring
**Services Involved**:
**User Requirements**:

**Acceptance Criteria**:

**Constraints**:

**Requirements File**: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus\.auto-claude\specs\001-multi-llm-agentic-development-platform\requirements.json (read this for full details)


The user wants me to act as the Complexity Assessor Agent. I need to:

1. Read the requirements.json file to understand the full task context
2. Analyze the requirements against all assessment criteria
3. Create complexity_assessment.json with my assessment

Let me start by reading the requirements.json file.

I'll analyze the task complexity by first reading the requirements file to understand the full context.


     1→{
     2→  "task_description": "Multi-LLM Agentic Development Platform\n\n  I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.\n\n  Core Features\n\n  1. Multi-LLM Provider Support\n  - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others\n  - Strategic model assignment - route tasks to models best suited for them:\n    - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)\n    - Claude Sonnet/Opus for complex reasoning and architecture\n    - Gemini for multimodal analysis and large context windows\n    - Grok for real-time information and alternative perspectives\n  - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance\n  - Cost optimization: intelligently balance between cloud and local models\n\n  2. Version Control Integration\n  - Native Git and Gitea support\n  - Automatic branching strategies for features/tasks\n  - Commit message generation aligned with conventional commits\n  - PR creation with comprehensive descriptions\n  - Support for reviewing and iterating on code in branches\n\n  3. Adaptive Spec System\n  - Initial spec-app style interview to gather requirements\n  - Re-interview capability: as the project evolves, conduct follow-up interviews to:\n    - Clarify ambiguous requirements that emerged during development\n    - Gather feedback on implemented features\n    - Adjust scope based on discoveries\n    - Validate assumptions with the user\n  - Spec versioning: track how requirements evolve over time\n  - Continuous alignment: ensure implementation stays true to user intent\n\n  4. Agent Skill System\n  - Extensible skill/addon architecture for specialized agent capabilities\n  - Skills can be model-specific or model-agnostic\n  - Examples: code review, security scanning, performance optimization, documentation generation\n  - Custom skill creation framework\n  - Skill composition: chain skills together for complex workflows\n\n  5. Project Steering & Course Correction\n  - Mid-flight adjustments: ability to redirect project direction after work has begun\n  - Checkpoint system: review progress at milestones and adjust plans\n  - Context preservation: maintain full project history when steering changes\n  - Impact analysis: understand implications of direction changes\n  - Graceful pivoting: refactor or adapt existing work rather than starting over\n\n  6. Intelligent Orchestration\n  - Task decomposition with model-specific assignments\n  - Parallel execution where possible\n  - Dependency management between tasks\n  - Progressive refinement: iterative improvement cycles\n  - Fallback strategies when a model fails or produces poor results\n\n  7. Quality Assurance\n  - Multi-model consensus for critical decisions\n  - Automated testing integration\n  - Code quality metrics and reporting\n  - Security vulnerability scanning\n  - Performance profiling\n\n  Technical Architecture\n\n  - Backend: Python (FastAPI) for orchestration layer\n  - Frontend: React/TypeScript UI for project management and monitoring\n  - Model Integration: Unified API wrapper supporting all providers\n  - State Management: Track project state, agent activities, and decisions\n  - Database: Store specs, decisions, task history, and model interactions\n  - MCP Server: Feature graph and skill management\n\n  Key Differentiators\n\n  1. Model democracy: No single LLM is the source of truth - leverage collective intelligence\n  2. Adaptive requirements: Specs evolve with the project, not frozen at start\n  3. Local-first option: Can run meaningful work without API costs via Ollama\n  4. Course correction: Projects aren't rigid - steer and adapt as you learn\n  5. Transparency: Full visibility into which models did what and why\n\n  Use Cases\n\n  - Large greenfield projects with evolving requirements\n  - Migrating/refactoring existing codebases with uncertain scope\n  - Research projects where requirements emerge through exploration\n  - Cost-sensitive projects that need to optimize token usage\n  - High-stakes projects requiring multi-model validation\n\n  Success Criteria\n\n  - Successfully complete a full project lifecycle: spec → implementation → steering → completion\n  - Demonstrate cost savings through intelligent model routing\n  - Show quality improvements through cross-model validation\n  - Handle mid-project requirement changes gracefully\n  - Support at least 3 different LLM providers working in concert",
     3→  "workflow_type": "refactoring"
     4→}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>

Multi-LLM Agentic Development Platform

  I want to build an advanced agentic development system that extends and integrates the best concepts from Autocoder 2.0 / Auto-Claude with multi-model orchestration and adaptive project management.

  Core Features

  1. Multi-LLM Provider Support
  - Support multiple LLM providers: Claude (Code & API), Gemini, Grok, Ollama, and others
  - Strategic model assignment - route tasks to models best suited for them:
    - Ollama for local, token-free lightweight tasks (file analysis, simple transforms, templating)
    - Claude Sonnet/Opus for complex reasoning and architecture
    - Gemini for multimodal analysis and large context windows
    - Grok for real-time information and alternative perspectives
  - Cross-model validation: have different LLMs review each other's work for accuracy and quality assurance
  - Cost optimization: intelligently balance between cloud and local models

  2. Version Control Integration
  - Native Git and Gitea support
  - Automatic branching strategies for features/tasks
  - Commit message generation aligned with conventional commits
  - PR creation with comprehensive descriptions
  - Support for reviewing and iterating on code in branches

  3. Adaptive Spec System
  - Initial spec-app style interview to gather requirements
  - Re-interview capability: as the project evolves, conduct follow-up interviews to:
    - Clarify ambiguous requirements that emerged during development
    - Gather feedback on implemented features
    - Adjust scope based on discoveries
    - Validate assumptions with the user
  - Spec versioning: track how requirements evolve over time
  - Continuous alignment: ensure implementation stays true to user intent

  4. Agent Skill System
  - Extensible skill/addon architecture for specialized agent capabilities
  - Skills can be model-specific or model-agnostic
  - Examples: code review, security scanning, performance optimization, documentation generation
  - Custom skill creation framework
  - Skill composition: chain skills together for complex workflows

  5. Project Steering & Course Correction
  - Mid-flight adjustments: ability to redirect project direction after work has begun
  - Checkpoint system: review progress at milestones and adjust plans
  - Context preservation: maintain full project history when steering changes
  - Impact analysis: understand implications of direction changes
  - Graceful pivoting: refactor or adapt existing work rather than starting over

  6. Intelligent Orchestration
  - Task decomposition with model-specific assignments
  - Parallel execution where possible
  - Dependency management between tasks
  - Progressive refinement: iterative improvement cycles
  - Fallback strategies when a model fails or produces poor results

  7. Quality Assurance
  - Multi-model consensus for critical decisions
  - Automated testing integration
  - Code quality metrics and reporting
  - Security vulnerability scanning
  - Performance profiling

  Technical Architecture

  - Backend: Python (FastAPI) for orchestration layer
  - Frontend: React/TypeScript UI for project management and monitoring
  - Model Integration: Unified API wrapper supporting all providers
  - State Management: Track project state, agent activities, and decisions
  - Database: Store specs, decisions, task history, and model interactions
  - MCP Server: Feature graph and skill management

  Key Differentiators

  1. Model democracy: No single LLM is the source of truth - leverage collective intelligence
  2. Adaptive requirements: Specs evolve with the project, not frozen at start
  3. Local-first option: Can run meaningful work without API costs via Ollama
  4. Course correction: Projects aren't rigid - steer and adapt as you learn
  5. Transparency: Full visibility into which models did what and why

  Use Cases

  - Large greenfield projects with evolving requirements
  - Migrating/refactoring existing codebases with uncertain scope
  - Research projects where requirements emerge through exploration
  - Cost-sensitive projects that need to optimize token usage
  - High-stakes projects requiring multi-model validation

  Success Criteria

  - Successfully complete a full project lifecycle: spec → implementation → steering → completion
  - Demonstrate cost savings through intelligent model routing
  - Show quality improvements through cross-model validation
  - Handle mid-project requirement changes gracefully
  - Support at least 3 different LLM providers working in concert


Good, I have the requirements. Let me also read the project_index.json to understand the existing project structure. This will help me assess if there are existing patterns.

Let me also read the project index to understand the existing codebase structure:


     1→{
     2→  "project_root": "C:\\Users\\MikeSwanson\\claude-projects\\ACPlus\\auto-claude-plus",
     3→  "project_type": "single",
     4→  "services": {},
     5→  "infrastructure": {},
     6→  "conventions": {}
     7→}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→{
     2→  "sourceType": "manual",
     3→  "category": "refactoring",
     4→  "priority": "high",
     5→  "complexity": "complex",
     6→  "impact": "high",
     7→  "model": "opus",
     8→  "thinkingLevel": "high",
     9→  "isAutoProfile": true,
    10→  "phaseModels": {
    11→    "spec": "opus",
    12→    "planning": "opus",
    13→    "coding": "opus",
    14→    "qa": "opus"
    15→  },
    16→  "phaseThinking": {
    17→    "spec": "ultrathink",
    18→    "planning": "high",
    19→    "coding": "low",
    20→    "qa": "low"
    21→  }
    22→}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


Now I have all the context I need. Let me analyze this task comprehensively:

## Task Analysis

**What's being built**: A multi-LLM agentic development platform with:
1. Multi-LLM Provider Support (Claude, Gemini, Grok, Ollama)
2. Version Control Integration (Git, Gitea)
3. Adaptive Spec System
4. Agent Skill System
5. Project Steering & Course Correction
6. Intelligent Orchestration
7. Quality Assurance Pipeline

**Technical Stack**:
- Backend: Python (FastAPI)
- Frontend: React/TypeScript
- Database: Not specified but needed
- MCP Server for feature graph and skill management

### Complexity Assessment

**Scope Analysis**:
- This is a MASSIVE greenfield project
- Estimated files: 50+ easily (backend routes, models, services, frontend components, utilities, configs)
- Estimated services: Multiple (orchestration layer, API wrapper, state management, database, MCP server)
- Definitely cross-cutting - affects everything

**Integration Analysis**:
- External services: Claude API, Gemini API, Grok API, Ollama (local), Git/Gitea
- That's 5+ external integrations
- New dependencies: Many (litellm or similar, FastAPI, SQLAlchemy, React, Git libraries)
- Research needed: YES - need to understand each LLM provider's API

**Infrastructure Analysis**:
- Docker changes: Likely (containerizing backend/frontend/database)
- Database changes: YES - need to design schema for specs, decisions, tasks, model interactions
- Config changes: YES - multiple .env variables for API keys, model configs

**Knowledge Analysis**:
- Patterns exist: The project_index shows an empty/new project - NO existing patterns
- Research required: YES - multiple LLM APIs, orchestration patterns
- Unfamiliar tech: Depends on experience, but multi-model orchestration is non-trivial

- Level: HIGH - API key management across providers, cost control, concurrent execution risks, state management complexity, cross-model validation logic, and secure credential storage all present significant challenges.

This clearly qualifies as COMPLEX given the four LLM providers plus version control integrations, combined with necessary infrastructure changes to database and containerization.

The task description mentions "refactoring" but the actual scope is greenfield development—building a new multi-service platform rather than modifying existing code. While it references existing Autocoder 2.0 patterns as conceptual foundation, the implementation is fundamentally a major feature build from scratch.

Risk assessment leans toward HIGH rather than CRITICAL. Although there's no payment processing involved, several factors elevate the risk: API key and credential management (security-sensitive), orchestration across multiple external services, database schema design, and establishing new architectural patterns. These elements require careful validation throughout development.

The full workflow should span all eight phases: discovery, requirements gathering, research, context building, specification writing, self-critique, planning, and validation. Each phase is necessary given the platform's complexity and security considerations.

Now I have a complete picture. Let me analyze this task systematically.

## Analysis Summary

This is a **massive greenfield platform** with:
- **4+ LLM provider integrations** (Claude, Gemini, Grok, Ollama)
- **Git/Gitea integration**
- **Full-stack development** (FastAPI + React)
- **Database design** for specs, decisions, task history
- **MCP Server** for skill management
- **New architectural patterns** (multi-model orchestration, cross-validation)

The project index shows an **empty project** - no existing patterns to build on. This is essentially building a new platform from scratch, requiring extensive research into each LLM provider's API, orchestration patterns, and system design.

Write

{
  "complexity": "complex",
  "workflow_type": "feature",
  "confidence": 0.95,
  "reasoning": "This is a large greenfield multi-LLM platform requiring 4+ external LLM provider integrations (Claude, Gemini, Grok, Ollama), Git/Gitea integration, full-stack development (FastAPI backend + React frontend), database schema design, and new architectural patterns for multi-model orchestration. The project index shows no existing patterns to leverage - this is building from scratch.",

  "analysis": {
    "scope": {
      "estimated_files": 75,
      "estimated_services": 5,
      "is_cross_cutting": true,
      "notes": "Core services: orchestration layer, unified LLM wrapper, spec management, skill system, version control integration. Plus frontend components, database models, API routes, utilities, and configuration."
    },
    "integrations": {
      "external_services": ["Claude API", "Gemini API", "Grok API", "Ollama", "Git", "Gitea"],
      "new_dependencies": ["fastapi", "litellm", "sqlalchemy", "react", "typescript", "gitpython", "pydantic", "httpx"],
      "research_needed": true,
      "notes": "Each LLM provider has different API patterns. Multi-model orchestration and cross-validation are novel patterns requiring careful design. Git/Gitea automation needs research."
    },
    "infrastructure": {
      "docker_changes": true,
      "database_changes": true,
      "config_changes": true,
      "notes": "Need Docker for backend/frontend/database containerization. Database schema for specs, decisions, task history, model interactions. Multiple .env variables for API keys and model configurations."
    },
    "knowledge": {
      "patterns_exist": false,
      "research_required": true,
      "unfamiliar_tech": ["multi-model orchestration", "cross-model validation", "litellm or unified LLM wrapper", "MCP server development", "adaptive spec systems"],
      "notes": "Project index shows empty project - no existing codebase patterns. Multi-model orchestration is a novel architectural pattern. Need to research each provider's API, rate limits, and capabilities."
    },
    "risk": {
      "level": "high",
      "concerns": [
        "API key management for 4+ providers",
        "Cost control across multiple paid APIs",
        "Concurrent execution and race conditions in orchestration",
        "State management complexity across parallel model calls",
        "Cross-model validation logic and consensus mechanisms",
        "Fallback strategies when models fail",
        "Security of credential storage",
        "Performance optimization for parallel API calls"
      ],
      "notes": "Complex distributed system with multiple external dependencies. Need careful error handling, retry logic, and graceful degradation."
    }
  },

  "recommended_phases": [
    "discovery",
    "requirements",
    "research",
    "context",
    "spec_writing",
    "self_critique",
    "planning",
    "validation"
  ],

  "flags": {
    "needs_research": true,
    "needs_self_critique": true,
    "needs_infrastructure_setup": true
  },

  "validation_recommendations": {
    "risk_level": "high",
    "skip_validation": false,
    "minimal_mode": false,
    "test_types_required": ["unit", "integration", "e2e"],
    "security_scan_required": true,
    "staging_deployment_required": false,
    "reasoning": "Multi-provider API integration with credential handling requires comprehensive testing. Security scan needed for API key management and authentication flows. No staging deployment since this is greenfield - local Docker environment sufficient for initial validation."
  },

  "additional_notes": {
    "workflow_type_clarification": "Marked as 'feature' rather than 'refactoring' because the project index shows an empty project with no existing code to refactor. This is greenfield development building new functionality from scratch, even though it's inspired by existing Autocoder 2.0/Auto-Claude concepts.",
    "phased_implementation_recommended": true,
    "suggested_milestones": [
      "Phase 1: Core orchestration layer with single LLM provider",
      "Phase 2: Add additional LLM providers with unified wrapper",
      "Phase 3: Spec management and version control integration",
      "Phase 4: Skill system and cross-model validation",
      "Phase 5: Frontend UI and project steering",
      "Phase 6: Quality assurance and production hardening"
    ]
  },

  "created_at": "2025-01-12T12:00:00.000Z"
}


File created successfully at: C:\Users\MikeSwanson\claude-projects\ACPlus\auto-claude-plus\.auto-claude\specs\001-multi-llm-agentic-development-platform\complexity_assessment.json

Explanation: This is a large greenfield multi-LLM platform requiring 4+ external LLM provider integrations (Claude, Gemini, Grok, Ollama), Git/Gitea integration, full-stack development (FastAPI backend + React frontend), database schema design, and new architectural patterns for multi-model orchestration. The project index shows no existing patterns to leverage - this is building from scratch.

Core services: orchestration layer, unified LLM wrapper, spec management, skill system, version control integration. Plus frontend components, database models, API routes, utilities, and configuration.

Claude API
Gemini API
Grok API
Ollama
Git
Gitea

fastapi litellm sqlalchemy react typescript gitpython pydantic httpx

Each LLM provider has different API patterns. Multi-model orchestration and cross-validation are novel patterns requiring careful design. Git/Gitea automation needs research.

Need Docker for backend/frontend/database containerization. Database schema for specs, decisions, task history, model interactions. Multiple .env variables for API keys and model configurations.

multi-model orchestration
cross-model validation
litellm or unified LLM wrapper
MCP server development
adaptive spec systems

Project index shows empty project - no existing codebase patterns. Multi-model orchestration is a novel architectural pattern. Need to research each provider's API, rate limits, and capabilities.

Concurrent execution and race conditions in orchestration

State management complexity across parallel model calls

Cross-model validation logic and consensus mechanisms

Performance optimization for parallel API calls

Complex distributed system with multiple external dependencies. Need careful error handling, retry logic, and graceful degradation.

discovery requirements research context spec_writing self_critique planning validation

Explanation: Multi-provider API integration with credential handling requires comprehensive testing. Security scan needed for API key management and authentication flows. No staging deployment since this is greenfield - local Docker environment sufficient for initial validation.

Marked as 'feature' rather than 'refactoring' because the project index shows an empty project with no existing code to refactor. This is greenfield development building new functionality from scratch, even though it's inspired by existing Autocoder 2.0/Auto-Claude concepts.

Phase 1: Core orchestration layer with single LLM provider

Phase 2: Add additional LLM providers with unified wrapper

Phase 3: Spec management and version control integration

Phase 4: Skill system and cross-model validation

Phase 5: Frontend UI and project steering

Phase 6: Quality assurance and production hardening


I've completed the complexity assessment. Let me provide a summary for the user.

## Complexity Assessment Complete

**Verdict: COMPLEX** (confidence: 0.95)

### Key Findings

| Dimension | Assessment | Details |
|-----------|------------|---------|
| **Scope** | 75+ files, 5 services | Full-stack platform with orchestration, LLM wrapper, spec system, skills, and VCS integration |
| **Integrations** | 6 external services | Claude, Gemini, Grok, Ollama APIs + Git/Gitea |
| **Infrastructure** | Major changes | Docker, database schema, multi-provider config |
| **Knowledge** | Research required | No existing patterns - empty project. Multi-model orchestration is novel architecture |
| **Risk** | HIGH | API key security, cost control, concurrent execution, fallback strategies |

### Recommended Pipeline (8 phases)

```
discovery → requirements → research → context → spec_writing → self_critique → planning → validation
```

### Validation Requirements

- **Risk Level**: HIGH
- **Test Types**: Unit + Integration + E2E
- **Security Scan**: Required (API key handling)
- **Staging Deployment**: Not required (greenfield, Docker local sufficient)

### Important Notes

1. **Workflow Type Correction**: Classified as `feature` (not refactoring) because the project index shows an empty codebase - this is greenfield development

2. **Phased Implementation Recommended**: The scope is substantial enough that implementation should be broken into milestones (see `additional_notes.suggested_milestones` in the output)

3. **Research Phase Critical**: Need to investigate each LLM provider's API patterns, rate limits, and capabilities before designing the unified wrapper

The complexity assessment has been written to:
```
.auto-claude/specs/001-multi-llm-agentic-development-platform/complexity_assessment.json
```