Claude/difficult project task 011 c uz p9n dnw xsn bv luq93wc #4
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Create clear, trackable roadmap from Phase 0 through v1.0.0 release. Addresses user question: 'do you have a roadmap?' This document provides: - Current status (Phase 0 in progress) - All 6 phases with deliverables and timelines - Success criteria for each phase - Decision points between phases - Timeline summary (~10 weeks to v1.0.0) - How to track progress Makes roadmap visible and actionable for AI-first development. Complements ARCHITECTURE_DESIGN.md with standalone reference. Immediate next steps clearly defined: 1. Complete Deno prototype 2. Build Go prototype 3. Compare and decide 4. Proceed to Phase 1
Add immediately actionable task tracking system with concrete next steps: New files: - STATUS.md: Quick dashboard showing current phase, next task, progress - PHASE_0_TASKS.md: Detailed Phase 0 tasks with acceptance criteria - .github/PROJECT_TRACKING.md: Explanation of systematic tracking - .github/ISSUE_TEMPLATE/task.md: Template for task issues - .github/ISSUE_TEMPLATE/decision.md: Template for ADR issues Updates: - README.md: Added prominent links to status files and current task This addresses user feedback that roadmap didn't provide insights on "what to do" - now there's a concrete, actionable task queue that can be worked through sequentially. Tracking system features: - Immediately actionable (no GitHub setup required first) - Clear acceptance criteria for each task - Progress tracking at a glance - Can be migrated to GitHub Issues later if desired Phase 0 Progress: 0/6 tasks complete Next Task: Define prototype evaluation criteria
PHASE 0 COMPLETE: Technology stack decided through rigorous evaluation. Decision: Go chosen over Deno (82/100 vs 78/100) Key deliverables: - Evaluation criteria (100-point system aligned with Five Cornerstones) - Deno prototype (328 lines TypeScript) - Score: 78/100 ✅ Viable - Go prototype (400 lines across 6 packages) - Score: 82/100 ✅ Recommended - Comprehensive comparison analysis - Formal ADR (Architecture Decision Record) - Phase 0 retrospective Rationale for Go: 1. Production excellence - Smaller binaries (8-15MB vs 40-60MB) 2. Ecosystem maturity - 15 years, proven at scale (Docker, K8s) 3. Modularity - Production-ready package structure from prototype 4. Long-term viability - Easier hiring, Google backing, backward compatibility New files: docs/ - PROTOTYPE_EVALUATION.md (scoring criteria) - PROTOTYPE_COMPARISON.md (comprehensive analysis) - ADR-001-TECHNOLOGY-CHOICE.md (formal decision) - PHASE_0_RETROSPECTIVE.md (learnings) prototypes/deno-poc/ - FINDINGS.md (evaluation: 78/100) prototypes/go-poc/ - Full Go implementation (config, memory, providers, orchestration) - FINDINGS.md (evaluation: 82/100) - README.md Updates: - STATUS.md (Phase 0 complete) - PHASE_0_TASKS.md (6/6 tasks complete) - README.md (Go chosen, ready for Phase 1) Phase 0 metrics: - Duration: 1 AI session (vs 2-6 day estimate) - All 6 tasks completed autonomously - Decision confidence: High - Ready for Phase 1: ✅ Yes Next: Await user validation of Go choice, then begin Phase 1 (Foundation)
MAJOR PIVOT: Paused Phase 0 implementation to understand what we're building.
User feedback revealed I was solving the wrong problem:
- Phase 0 chose "Deno vs Go for CLI app"
- But Sophie isn't a CLI app - it's an agent system that runs WITHIN Claude Code CLI
- The original agent doesn't run standalone - it's knowledge + config loaded into Claude Desktop
This commit begins proper foundation work: understanding WHY the original agent works
before deciding HOW to port it.
New analysis documents:
1. PRODUCT_DESIGN_AGENT_SUCCESS_ANALYSIS.md (2,400 lines)
- Why it works "amazingly well"
- 8 success factors identified:
* Expert agent personas (12 specialists with workflows, handoffs)
* Comprehensive methodologies (15,793 lines of curated knowledge)
* Task configuration system (64 tasks with clear deliverables)
* Agent collaboration network (handoffs between specialists)
* Natural conversation UX (no commands required)
* Bilingual support (EN/ES throughout)
* Comprehensive coverage (full design lifecycle)
* Quality standards (best practices, expert references)
- Analysis of methodology depth (usability testing, difficult conversations, journey mapping)
- Integration model questions (how does it actually work in Claude Desktop?)
2. AGENT_TASK_MAPPING.md (800 lines)
- Complete distribution: 12 agents × 64 tasks
- Research Analyst (14 tasks, 21.9%) - most active
- Strategy Analyst (13 tasks, 20.3%) - second most active
- Research + Strategy = 42% of all tasks (core work)
- Agent specialization patterns:
* High-volume generalists (Research, Strategy)
* Mid-volume specialists (Collaboration, AI)
* Low-volume depth experts (8 agents with 2-3 tasks each)
- Handoff network analysis
- Implications for Sophie
Key realizations:
- Original agent is NOT a standalone app
- It's expert knowledge system + orchestration loaded into AI environment
- Value is in 15,793 lines of methodologies, not technology stack
- 12 agent personas create role-based expertise with collaboration
- Success comes from actionable depth, not breadth
Next steps (proper sequence):
1. Continue deep analysis (conversation flows, integration model)
2. Understand Claude Desktop architecture
3. Define Sophie requirements based on preserving "soul"
4. THEN choose technology approach
This invalidates Phase 0 conclusions. Go vs Deno was premature.
Technology choice must follow requirements, not precede them.
Related: Phase 0 work archived but not deleted (learning preserved)
Deep analysis of how users experience the Product Design Agent through
actual conversation and guidance patterns.
Key findings:
1. Universal 12-Section Guide Structure
- Executive Summary (orientation)
- Overview & Objectives (context)
- Preparation (checklists before starting)
- Main Flow/Process (time-boxed steps)
- Templates/Canvases (actual formats to use)
- Best Practices by Context (specialized guidance)
- Roles & Responsibilities (when collaborative)
- Aftermath/Follow-ups (what's next)
- Best Practices & Pitfalls (do's and don'ts)
- Tools & Resources (practical enablers)
- FAQ (quick answers)
- References (expert sources)
2. Conversation Flow Phases
- Phase 1: Orientation (what you'll accomplish)
- Phase 2: Preparation (before you start)
- Phase 3: Execution (time-boxed process)
- Phase 4: Tooling (templates provided)
- Phase 5: Quality Control (best practices)
- Phase 6: Closure (next steps clear)
3. Characteristics
- Professional mentor voice (not academic, not casual)
- Actionable over theoretical (checklists, templates, time-boxes)
- Evidence-based (expert references throughout)
- Context-aware (acknowledges trade-offs)
- Quality-focused (success criteria, pitfall warnings)
4. Cross-Guide Patterns
- Explicit cross-references ("see prioritization.md")
- Bilingual support (EN/ES throughout)
- Progressive complexity (skim OR deep-dive)
- Connected knowledge system
5. User Experience
- Discovery without knowing (agent matches intent)
- Just-in-time guidance (loads when needed)
- Completeness without overwhelm (structured but scannable)
- Confidence building (clear path + validation)
Critical insights for Sophie:
- Not documentation lookup - professional mentorship experience
- Structure creates consistency across 64 methodologies
- Templates and checklists make work actionable
- Time-boxing and role clarity enable execution
- Cross-references create connected knowledge network
Must preserve:
- 12-section guide structure
- Professional mentorship voice
- Progressive disclosure (summary → detail → deep-dive)
- Actionable templates and checklists
- Quality standards embedded throughout
Next: Analyze knowledge architecture organization
Analyzed organization of 15,793 lines across 75 files: - 64 task guides (methodologies with universal 12-section structure) - 11 materials (templates, registries, catalogs, data lists) - Two-tier system: guides = HOW to work, materials = TOOLS for work - Dense cross-reference network creates connected knowledge - Just-in-time loading pattern identified - Progressive disclosure through links - Evidence-based with expert references Key findings: - Universal structure creates consistency - Depth varies by complexity (50-500 lines) - Guides are REFERENCE not SCRIPT - Materials enable guides (templates with examples) - Bilingual support (EN/ES) where appropriate - CSV/JSON for machine-readable data Critical for Sophie: - Preserve two-tier system - Maintain 12-section structure - Implement just-in-time loading - Resolve cross-reference network - Keep actionable orientation
Analyzed how Product Design Agent works in Claude Desktop/Gemini Gems: - File-based knowledge system (not standalone CLI) - Orchestration via custom instructions (assets/instructions.md) - Just-in-time knowledge loading (task matching → guide loading) - Two-tier context (user project files + methodology framework) - Agent persona activation from agents.yaml - Platform provides infrastructure (file storage, reading, persistence) Key mechanisms preserved for Sophie: - Orchestration instructions (8-step workflow) - Task matching with confidence scoring - Agent persona system - Just-in-time loading pattern - User preferences support - Bilingual language detection Platform constraints that Sophie must overcome: - No persistent memory (sessions reset) - No multi-project support - Platform-dependent (Claude Pro/Google One AI Premium) - No external knowledge integration Integration strategy options for Sophie: - Option 1: MCP Server - Option 2: Custom Instructions + File Loading - Option 3: Session Bootstrap Technical challenges identified for Phase 1-2.
Comprehensive synthesis of 5 analysis documents into technical requirements: **The Three Requirements (User's Directive):** 1. Feels and functions as required to the user (UX) - Natural conversation (zero-scripted) - Expert mentorship feel (12 agent personas) - Invisible orchestration - Bilingual & culturally adaptive - Project-aware context 2. Functions and operates as required for the process - Task matching system (confidence scoring) - Two-tier knowledge + project memory (NEW) - Just-in-time loading - Cross-reference network - Agent collaboration patterns - Validation & quality (10-point checklist) 3. Produces what is required for product development - Structured documents (plans, reports, strategies) - Templates with examples - Reusable components - Professional quality standards - Actionable, not theoretical **What Sophie Must Preserve:** - File-based knowledge system - Orchestration pattern (8-step workflow) - Agent persona system - Universal guide structure (12 sections) - Just-in-time loading - Two-tier context priority - Bilingual support **What Sophie Must Add:** 1. Persistent memory (SQLite: conversations, decisions, artifacts) 2. Multi-project support (isolated contexts, switching) 3. External knowledge integration (4th memory tier, provenance) 4. Provider agnosticism (Claude Code CLI, Gemini CLI) 5. Enhanced search (semantic, cross-project) **Architecture Defined:** - CLI Interface (REPL) - Orchestration Engine (intent → task → agent → knowledge → response) - AI Provider Layer (abstract interface) - Memory Layer (SQLite) - Config/Knowledge Loaders - Validation Engine - External Knowledge Manager **Critical Design Decisions:** - Integration model: 3 options evaluated (MCP, Custom Instructions, Wrapper) - Just-in-time loading mechanism - Multi-project context management - User preferences handling - External knowledge integration **Implementation Phases (Refined):** - Phase 0: Foundation (COMPLETE - this analysis) - Phase 1: Core Infrastructure (config, knowledge, task matching, AI provider) - Phase 2: Memory & Persistence (SQLite, projects, conversations) - Phase 3: Agent Personas & Orchestration (full 8-step workflow) - Phase 4: External Knowledge & Advanced Features (4th tier, multi-provider) - Phase 5: Polish & Optimization (performance, bilingual, testing) **Technology Decision:** Deferred to Phase 1 (now we have clear requirements) **Status:** Requirements complete, ready for Phase 1 implementation planning
Comprehensive extraction of 25 process memories from Phase 0: **Primary Paradigm Shifts:** - pm-006: Original agent is NOT a CLI (major pivot) - pm-010: Structure in content, not conversation (zero-scripted UX) - pm-007: Technology before understanding = wrong problem **Methodological Learnings:** - pm-001: Stop microfixing, think holistically - pm-002: Documentation-first validates direction - pm-008: House analogy - don't build until you understand why - pm-009: Deep analysis as foundation for implementation - pm-013: Requirements emerge FROM analysis, not before **Critical Architectural Discoveries:** - pm-011: Two-tier knowledge system (guides + materials) - pm-012: File-based orchestration via instructions - pm-018: Just-in-time knowledge loading pattern - pm-020: 4th memory tier for external knowledge - pm-021: Universal 12-section guide structure **AI-First Development Patterns:** - pm-003: ADRs and systematic tracking enable autonomy - pm-004: AI as primary developer, user as product owner - pm-005: Autonomous Phase 0 execution (successful but wrong problem) **Quality & Validation Mechanisms:** - pm-023: 10-point validation checklist - pm-024: Task matching confidence scoring (HIGH/MEDIUM/LOW) - pm-015: Five Cornerstones evaluation framework **Process Memory Types:** - 6 LessonLearned (methodological insights) - 4 StrategicDecision (major direction changes) - 4 MentalModels (thinking frameworks) - 10 Observations (discoveries from analysis) - 1 FailureAnalysis (wrong problem identification) - 1 ContextualMemory (user requirements framework) Each memory includes: - Unique ID, type, title, summary, rationale - Related concepts, timestamps, confidence level - Provenance (how memory was created) - Links (knowledge graph connections) - Tags (categorization) Archive designed for AI-First consumption (JSON schema) Enables future AI sessions to understand learning journey Meta-insights section highlights critical patterns
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Pull Request Overview
This is an extensive pull request documenting Phase 0 of the Sophie project - a comprehensive technology validation phase that evolved into a deep analysis and requirements definition exercise. The PR captures the transformation of the Product Design Agent into Sophie, a CLI-based system with persistent memory and multi-project support.
Key Changes:
- Process memory documentation capturing 25 critical learning moments from Phase 0
- Five comprehensive analysis documents (2,500+ lines) covering system architecture, knowledge organization, and integration patterns
- Go and Deno prototype implementations for technology validation
- Complete requirements specification (1,003 lines) synthesized from analysis
- Technology decision documentation (Go chosen: 82/100 vs Deno: 78/100)
Reviewed Changes
Copilot reviewed 27 out of 28 changed files in this pull request and generated no comments.
Show a summary per file
| File | Description |
|---|---|
| PROCESS_MEMORY.json | Structured archive of 25 critical learnings, paradigm shifts, and insights from Phase 0 |
| prototypes/go-poc/* | Go implementation prototype with modular package structure (config, memory, providers, orchestration) |
| prototypes/go-poc/go.mod | Go module definition specifying Go 1.24 and dependencies |
| prototypes/deno-poc/FINDINGS.md | Deno prototype evaluation (78/100 - viable but not recommended) |
| prototypes/go-poc/FINDINGS.md | Go prototype evaluation (82/100 - recommended) |
| docs/PROTOTYPE_COMPARISON.md | Comprehensive side-by-side comparison of Deno vs Go |
| docs/ADR-001-TECHNOLOGY-CHOICE.md | Formal architecture decision record selecting Go |
| docs/SOPHIE_REQUIREMENTS.md | Complete requirements specification (1,003 lines) |
| docs/*_ANALYSIS.md | Five analysis documents covering success factors, architecture, and integration |
| STATUS.md, ROADMAP.md, README.md | Updated project tracking and documentation |
| .github/ISSUE_TEMPLATE/* | Task and ADR templates for project tracking |
Note: This review focuses on identifying objective issues in the code and documentation. The extensive documentation represents a significant pivot from premature technology selection to comprehensive analysis-driven requirements definition.
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process memory