Mike Swanson
75ce1c2fd5
Enhanced code review and frontend validation with intelligent triggers: Code Review Agent Enhancement: - Added Sequential Thinking MCP integration for complex issues - Triggers on 2+ rejections or 3+ critical issues - New escalation format with root cause analysis - Comprehensive solution strategies with trade-off evaluation - Educational feedback to break rejection cycles - Files: .claude/agents/code-review.md (+308 lines) - Docs: CODE_REVIEW_ST_ENHANCEMENT.md, CODE_REVIEW_ST_TESTING.md Frontend Design Skill Enhancement: - Automatic invocation for ANY UI change - Comprehensive validation checklist (200+ checkpoints) - 8 validation categories (visual, interactive, responsive, a11y, etc.) - 3 validation levels (quick, standard, comprehensive) - Integration with code review workflow - Files: .claude/skills/frontend-design/SKILL.md (+120 lines) - Docs: UI_VALIDATION_CHECKLIST.md (462 lines), AUTOMATIC_VALIDATION_ENHANCEMENT.md (587 lines) Settings Optimization: - Repaired .claude/settings.local.json (fixed m365 pattern) - Reduced permissions from 49 to 33 (33% reduction) - Removed duplicates, sorted alphabetically - Created SETTINGS_PERMISSIONS.md documentation Checkpoint Command Enhancement: - Dual checkpoint system (git + database) - Saves session context to API for cross-machine recall - Includes git metadata in database context - Files: .claude/commands/checkpoint.md (+139 lines) Decision Rationale: - Sequential Thinking MCP breaks rejection cycles by identifying root causes - Automatic frontend validation catches UI issues before code review - Dual checkpoints enable complete project memory across machines - Settings optimization improves maintainability Total: 1,200+ lines of documentation and enhancements Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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{"type":"queue-operation","operation":"dequeue","timestamp":"2026-01-12T18:40:50.237Z","sessionId":"afb9f438-c470-4f0d-ae8d-77645d434d6d"}
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{"parentUuid":null,"isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"afb9f438-c470-4f0d-ae8d-77645d434d6d","version":"2.1.5","gitBranch":"","type":"user","message":{"role":"user","content":"Summarize the key findings from the \"requirements\" 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**requirements.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 → 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\",\n \"workflow_type\": \"refactoring\"\n}\n```\n\n## Summary:\n"},"uuid":"68365639-bb63-4f4b-89d8-18095e0b6c32","timestamp":"2026-01-12T18:40:50.645Z"}
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{"parentUuid":"68365639-bb63-4f4b-89d8-18095e0b6c32","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"afb9f438-c470-4f0d-ae8d-77645d434d6d","version":"2.1.5","gitBranch":"","message":{"model":"claude-sonnet-4-5-20250929","id":"msg_01Fn2suHpYgetNfipKVECYAX","type":"message","role":"assistant","content":[{"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:\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\nLet me analyze the requirements.json content:\n\n**Project Goal**: Build a multi-LLM agentic development platform that extends Autocoder 2.0/Auto-Claude concepts\n\n**Key Decisions & Rationale**:\n1. Multi-LLM approach (not single model) - leverage collective intelligence\n2. Strategic model routing based on task type - optimize cost and quality\n3. Adaptive specs (not frozen requirements) - allow evolution during development\n4. Local-first option via Ollama - reduce API costs\n\n**Core Components Identified**:\n1. Multi-LLM Provider Support (Claude, Gemini, Grok, Ollama)\n2. Version Control Integration (Git/Gitea)\n3. Adaptive Spec System\n4. Agent Skill System\n5. Project Steering & Course Correction\n6. Intelligent Orchestration\n7. Quality Assurance\n\n**Technical Architecture**:\n- Backend: Python (FastAPI)\n- Frontend: React/TypeScript\n- Unified API wrapper for all providers\n- State management system\n- Database for specs/decisions/history\n- MCP Server for feature graph and skills\n\n**Critical Requirements**:\n- Support minimum 3 LLM providers\n- Cost optimization through intelligent routing\n- Cross-model validation for quality\n- Git workflow automation\n- Spec versioning and re-interviewing\n- Extensible skill architecture\n- Mid-project steering capability\n\n**Success Criteria**:\n- Complete full project lifecycle\n- Demonstrate cost savings\n- Show quality improvements via cross-validation\n- Handle requirement changes gracefully\n\n**Workflow Type**: Refactoring (indicates this builds on existing concepts)\n\nLet me structure this concisely with bullets focusing on actionable insights.","signature":"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"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":3445,"cache_read_input_tokens":13209,"cache_creation":{"ephemeral_5m_input_tokens":3445,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard"}},"requestId":"req_011CX3yRbEbWrpFSV6PCQqet","type":"assistant","uuid":"f2d142f5-2ea8-4f15-a054-34262afd1d2f","timestamp":"2026-01-12T18:41:00.318Z"}
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{"parentUuid":"f2d142f5-2ea8-4f15-a054-34262afd1d2f","isSidechain":false,"userType":"external","cwd":"C:\\Users\\MikeSwanson\\AppData\\Local\\Programs\\auto-claude-ui\\resources\\backend","sessionId":"afb9f438-c470-4f0d-ae8d-77645d434d6d","version":"2.1.5","gitBranch":"","message":{"model":"claude-sonnet-4-5-20250929","id":"msg_01Fn2suHpYgetNfipKVECYAX","type":"message","role":"assistant","content":[{"type":"text","text":"# Requirements Phase Summary\n\n## Project Vision\nBuild 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.\n\n## Key Architectural Decisions\n\n**Multi-Model Strategy** (Core differentiator)\n- No single LLM as source of truth - leverage collective intelligence\n- Strategic routing by task type:\n - **Ollama**: Local, token-free lightweight tasks (file analysis, transforms)\n - **Claude Sonnet/Opus**: Complex reasoning and architecture\n - **Gemini**: Multimodal analysis, large context windows\n - **Grok**: Real-time information, alternative perspectives\n- Cross-model validation for quality assurance\n- Cost optimization through intelligent model selection\n\n**Adaptive Requirements** (Not waterfall)\n- Specs evolve with project, not frozen at start\n- Re-interview capability to clarify ambiguities during development\n- Spec versioning to track requirement evolution\n- Mid-flight course correction and steering\n\n## Critical Components to Implement\n\n1. **Model Orchestration Layer**\n - Unified API wrapper supporting all providers\n - Task decomposition with model-specific assignments\n - Parallel execution engine with dependency management\n - Fallback strategies for model failures\n\n2. **Version Control Integration**\n - Git/Gitea native support\n - Auto-branching strategies\n - Conventional commit message generation\n - PR creation with comprehensive descriptions\n\n3. **Spec Management System**\n - Initial spec-app style interview\n - Spec versioning and history tracking\n - Re-interview workflow for clarification\n - Context preservation across changes\n\n4. **Extensible Skill Architecture**\n - Plugin system for specialized capabilities\n - Model-specific and model-agnostic skills\n - Skill composition for complex workflows\n - Custom skill creation framework\n\n5. **Quality Assurance Pipeline**\n - Multi-model consensus for critical decisions\n - Automated testing integration\n - Security scanning, performance profiling\n - Code quality metrics\n\n## Technical Stack\n\n- **Backend**: Python (FastAPI) orchestration layer\n- **Frontend**: React/TypeScript for project management UI\n- **State**: Track project state, agent activities, decisions\n- **Database**: Store specs, decisions, task history, model interactions\n- **MCP Server**: Feature graph and skill management\n\n## Critical Constraints\n\n- Minimum **3 LLM providers** working in concert\n- Must demonstrate **cost savings** through model routing\n- Must handle **mid-project requirement changes** gracefully\n- Full **transparency**: visibility into which models did what and why\n- Support **local-first** operation via Ollama\n\n## Success Metrics\n\n1. Complete full lifecycle: spec → implementation → steering → completion\n2. Prove cost optimization through intelligent routing\n3. Show quality improvements via cross-model validation\n4. Handle evolving requirements without restart\n5. Support at least 3 providers simultaneously\n\n## Workflow Classification\n**Type**: Refactoring - building on existing Autocoder 2.0/Auto-Claude patterns"}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":3445,"cache_read_input_tokens":13209,"cache_creation":{"ephemeral_5m_input_tokens":3445,"ephemeral_1h_input_tokens":0},"output_tokens":1173,"service_tier":"standard"}},"requestId":"req_011CX3yRbEbWrpFSV6PCQqet","type":"assistant","uuid":"2b963824-c5b7-4e36-b23d-125c33f11583","timestamp":"2026-01-12T18:41:10.805Z"}
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