userType: external

type: user

role: user

content:
Summarize the key findings from the "requirements" phase in 500 words or less.

Focus on extracting ONLY the most critical information that subsequent phases need:
- Key decisions made and their rationale
- Critical files, components, or patterns identified
- Important constraints or requirements discovered
- Actionable insights for implementation

Be concise and use bullet points. Skip boilerplate and meta-commentary.

## Phase Output:
**requirements.json**:
```
{
  "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",
  "workflow_type": "refactoring"
}
```

## Summary:


type: message

role: assistant

type: thinking

thinking:
The user wants me to summarize the requirements phase output, which contains a single JSON file with the task description and workflow type. I need to extract:
- Key decisions made and their rationale
- Critical files, components, or patterns identified
- Important constraints or requirements discovered
- Actionable insights for implementation

Let me analyze the requirements.json content:

**Project Goal**: Build a multi-LLM agentic development platform that extends Autocoder 2.0/Auto-Claude concepts

**Key Decisions & Rationale**:
1. Multi-LLM approach (not single model) - leverage collective intelligence
2. Strategic model routing based on task type - optimize cost and quality
3. Adaptive specs (not frozen requirements) - allow evolution during development
4. Local-first option via Ollama - reduce API costs

**Core Components Identified**:
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

**Technical Architecture**:
- Backend: Python (FastAPI)
- Frontend: React/TypeScript
- Unified API wrapper for all providers
- State management system
- Database for specs/decisions/history
- MCP Server for feature graph and skills

**Critical Requirements**:
- Support minimum 3 LLM providers
- Cost optimization through intelligent routing
- Cross-model validation for quality
- Git workflow automation
- Spec versioning and re-interviewing
- Extensible skill architecture
- Mid-project steering capability

**Success Criteria**:
- Complete full project lifecycle
- Demonstrate cost savings
- Show quality improvements via cross-validation
- Handle requirement changes gracefully

**Workflow Type**: Refactoring (indicates this builds on existing concepts)

Let me structure this concisely with bullets focusing on actionable insights.

service_tier: standard

type: assistant


type: text

text:
# 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