userType: external

type: user

role: user

content:
Summarize the key findings from the "context" 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:
**context.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 \u2192 implementation \u2192 steering \u2192 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",
  "scoped_services": [],
  "files_to_modify": [],
  "files_to_reference": [],
  "created_at": "2026-01-12T11:50:41.978005"
}
```

## Summary:


type: message

role: assistant

type: thinking

thinking:
The user wants me to summarize the key findings from a "context" phase. Looking at the provided context.json, this appears to be a task description for building a multi-LLM agentic development platform.

Let me extract the most critical information that subsequent phases would need:

1. **Core Objective**: Build a multi-LLM orchestration system for agentic development
2. **Key Technical Decisions**:
   - Multi-provider support (Claude, Gemini, Grok, Ollama)
   - Strategic model routing based on task type
   - Backend: Python/FastAPI
   - Frontend: React/TypeScript
   - Database for state management
   - MCP Server for feature graph/skills

3. **Critical Features**:
   - Model routing and task assignment
   - Adaptive spec system with re-interviewing
   - Git/Gitea integration
   - Extensible skill system
   - Project steering/course correction
   - Multi-model validation

4. **Key Constraints/Requirements**:
   - Must support at least 3 LLM providers
   - Cost optimization is critical
   - Local-first option via Ollama
   - Full transparency/traceability
   - Handle mid-project changes gracefully

5. **Architectural Patterns**:
   - Unified API wrapper for all providers
   - Task decomposition with parallel execution
   - Checkpoint/milestone system
   - Progressive refinement cycles

Let me structure this concisely with bullet points.

service_tier: standard

type: assistant


type: text

text:
# Context Phase: Key Findings

## Project Objective
Build a **multi-LLM agentic development platform** that orchestrates multiple AI models (Claude, Gemini, Grok, Ollama) for intelligent task routing, cost optimization, and quality assurance through cross-model validation.

## Critical Technical Decisions

**Tech Stack:**
- Backend: Python (FastAPI) for orchestration layer
- Frontend: React/TypeScript for project management UI
- Database: Store specs, decisions, task history, model interactions
- MCP Server: Feature graph and skill management
- Unified API wrapper supporting all LLM providers

**Model Routing Strategy:**
- Ollama → lightweight local tasks (file analysis, templating)
- Claude Sonnet/Opus → complex reasoning, architecture
- Gemini → multimodal analysis, large context windows
- Grok → real-time information, alternative perspectives

## Core Feature Requirements

**1. Multi-LLM Orchestration**
- Strategic task assignment based on model strengths
- Cross-model validation for quality assurance
- Fallback strategies when models fail
- Cost optimization through intelligent routing

**2. Adaptive Spec System**
- Initial spec-app style interview
- Re-interview capability for evolving requirements
- Spec versioning to track requirement evolution
- Continuous alignment with user intent

**3. Version Control Integration**
- Native Git/Gitea support
- Automatic branching strategies
- Conventional commit message generation
- PR creation with descriptions
- Branch-based code review

**4. Extensible Skill System**
- Model-specific or model-agnostic skills
- Examples: code review, security scanning, performance optimization
- Custom skill framework
- Skill composition/chaining

**5. Project Steering**
- Mid-flight direction changes
- Checkpoint system at milestones
- Context preservation across pivots
- Impact analysis for changes
- Graceful refactoring vs. starting over

**6. Quality Assurance**
- Multi-model consensus for critical decisions
- Automated testing integration
- Code quality metrics
- Security scanning
- Performance profiling

## Key Constraints

- Must support ≥3 LLM providers working together
- Cost optimization is critical success factor
- Local-first capability required (Ollama)
- Full transparency into model decisions
- Handle evolving requirements gracefully

## Implementation Priorities

**Success Metrics:**
1. Complete full lifecycle: spec → implementation → steering → completion
2. Demonstrate cost savings through model routing
3. Prove quality improvements via cross-validation
4. Handle mid-project requirement changes
5. Integrate 3+ LLM providers successfully

**Key Differentiators:**
- Model democracy (no single source of truth)
- Adaptive, not frozen requirements
- Local-first option for cost savings
- Course correction capabilities
- Full traceability

## Actionable Insights

- Build unified API abstraction layer first (critical for multi-provider support)
- Implement task decomposition engine early (enables parallel execution)
- Design state management for tracking decisions across models
- Create checkpoint system for milestone reviews
- Focus on graceful degradation and fallback strategies