claudetools/imported-conversations/auto-claude-variants/ui-backend/9b34c95d-e622-4d54-b928-8a3b991a4ce0.jsonl

{"type":"queue-operation","operation":"dequeue","timestamp":"2026-01-12T18:50:45.447Z","sessionId":"9b34c95d-e622-4d54-b928-8a3b991a4ce0"}
{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"9b34c95d-e622-4d54-b928-8a3b991a4ce0","version":"2.1.5","gitBranch":"","type":"user","message":{"role":"user","content":"Summarize the key findings from the \"context\" phase in 500 words or less.\n\nFocus on extracting ONLY the most critical information that subsequent phases need:\n- Key decisions made and their rationale\n- Critical files, components, or patterns identified\n- Important constraints or requirements discovered\n- Actionable insights for implementation\n\nBe concise and use bullet points. Skip boilerplate and meta-commentary.\n\n## Phase Output:\n**context.json**:\n```\n{\n  \"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\",\n  \"scoped_services\": [],\n  \"files_to_modify\": [],\n  \"files_to_reference\": [],\n  \"created_at\": \"2026-01-12T11:50:41.978005\"\n}\n```\n\n## Summary:\n"},"uuid":"c917631d-7e2c-49f5-ab79-37c8b9cce33c","timestamp":"2026-01-12T18:50:45.694Z"}
{"parentUuid":"c917631d-7e2c-49f5-ab79-37c8b9cce33c","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"9b34c95d-e622-4d54-b928-8a3b991a4ce0","version":"2.1.5","gitBranch":"","message":{"model":"claude-sonnet-4-5-20250929","id":"msg_015U9o5znKZ1uWHkxKPC7rMq","type":"message","role":"assistant","content":[{"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.\n\nLet me extract the most critical information that subsequent phases would need:\n\n1. **Core Objective**: Build a multi-LLM orchestration system for agentic development\n2. **Key Technical Decisions**:\n   - Multi-provider support (Claude, Gemini, Grok, Ollama)\n   - Strategic model routing based on task type\n   - Backend: Python/FastAPI\n   - Frontend: React/TypeScript\n   - Database for state management\n   - MCP Server for feature graph/skills\n\n3. **Critical Features**:\n   - Model routing and task assignment\n   - Adaptive spec system with re-interviewing\n   - Git/Gitea integration\n   - Extensible skill system\n   - Project steering/course correction\n   - Multi-model validation\n\n4. **Key Constraints/Requirements**:\n   - Must support at least 3 LLM providers\n   - Cost optimization is critical\n   - Local-first option via Ollama\n   - Full transparency/traceability\n   - Handle mid-project changes gracefully\n\n5. **Architectural Patterns**:\n   - Unified API wrapper for all providers\n   - Task decomposition with parallel execution\n   - Checkpoint/milestone system\n   - Progressive refinement cycles\n\nLet me structure this concisely with bullet points.","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":1777,"cache_read_input_tokens":14930,"cache_creation":{"ephemeral_5m_input_tokens":1777,"ephemeral_1h_input_tokens":0},"output_tokens":1,"service_tier":"standard"}},"requestId":"req_011CX3zBTBPEvkM9XV556ptE","type":"assistant","uuid":"5626e88c-642b-48a4-b421-aa7878130841","timestamp":"2026-01-12T18:50:53.303Z"}
{"parentUuid":"5626e88c-642b-48a4-b421-aa7878130841","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"9b34c95d-e622-4d54-b928-8a3b991a4ce0","version":"2.1.5","gitBranch":"","message":{"model":"claude-sonnet-4-5-20250929","id":"msg_015U9o5znKZ1uWHkxKPC7rMq","type":"message","role":"assistant","content":[{"type":"text","text":"# Context Phase: Key Findings\n\n## Project Objective\nBuild 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.\n\n## Critical Technical Decisions\n\n**Tech Stack:**\n- Backend: Python (FastAPI) for orchestration layer\n- Frontend: React/TypeScript for project management UI\n- Database: Store specs, decisions, task history, model interactions\n- MCP Server: Feature graph and skill management\n- Unified API wrapper supporting all LLM providers\n\n**Model Routing Strategy:**\n- Ollama → lightweight local tasks (file analysis, templating)\n- Claude Sonnet/Opus → complex reasoning, architecture\n- Gemini → multimodal analysis, large context windows\n- Grok → real-time information, alternative perspectives\n\n## Core Feature Requirements\n\n**1. Multi-LLM Orchestration**\n- Strategic task assignment based on model strengths\n- Cross-model validation for quality assurance\n- Fallback strategies when models fail\n- Cost optimization through intelligent routing\n\n**2. Adaptive Spec System**\n- Initial spec-app style interview\n- Re-interview capability for evolving requirements\n- Spec versioning to track requirement evolution\n- Continuous alignment with user intent\n\n**3. Version Control Integration**\n- Native Git/Gitea support\n- Automatic branching strategies\n- Conventional commit message generation\n- PR creation with descriptions\n- Branch-based code review\n\n**4. Extensible Skill System**\n- Model-specific or model-agnostic skills\n- Examples: code review, security scanning, performance optimization\n- Custom skill framework\n- Skill composition/chaining\n\n**5. Project Steering**\n- Mid-flight direction changes\n- Checkpoint system at milestones\n- Context preservation across pivots\n- Impact analysis for changes\n- Graceful refactoring vs. starting over\n\n**6. Quality Assurance**\n- Multi-model consensus for critical decisions\n- Automated testing integration\n- Code quality metrics\n- Security scanning\n- Performance profiling\n\n## Key Constraints\n\n- Must support ≥3 LLM providers working together\n- Cost optimization is critical success factor\n- Local-first capability required (Ollama)\n- Full transparency into model decisions\n- Handle evolving requirements gracefully\n\n## Implementation Priorities\n\n**Success Metrics:**\n1. Complete full lifecycle: spec → implementation → steering → completion\n2. Demonstrate cost savings through model routing\n3. Prove quality improvements via cross-validation\n4. Handle mid-project requirement changes\n5. Integrate 3+ LLM providers successfully\n\n**Key Differentiators:**\n- Model democracy (no single source of truth)\n- Adaptive, not frozen requirements\n- Local-first option for cost savings\n- Course correction capabilities\n- Full traceability\n\n## Actionable Insights\n\n- Build unified API abstraction layer first (critical for multi-provider support)\n- Implement task decomposition engine early (enables parallel execution)\n- Design state management for tracking decisions across models\n- Create checkpoint system for milestone reviews\n- Focus on graceful degradation and fallback strategies"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":1777,"cache_read_input_tokens":14930,"cache_creation":{"ephemeral_5m_input_tokens":1777,"ephemeral_1h_input_tokens":0},"output_tokens":1070,"service_tier":"standard"}},"requestId":"req_011CX3zBTBPEvkM9XV556ptE","type":"assistant","uuid":"1d3fc74b-6ece-4523-87f9-e3826adcb3ea","timestamp":"2026-01-12T18:51:04.039Z"}