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Synthesized research findings from 4 parallel researcher agents: Key Findings: - Stack: discord.py 2.6.4 + PostgreSQL/SQLite with webhook-driven PluralKit integration - Architecture: 7-component system with clear separation of concerns, async-native - Features: Rule-based learning system starting simple, avoiding context inference and ML - Pitfalls: 8 critical risks identified with phase assignments and prevention strategies Recommended Approach: - 5-phase build order (detection → translation → teaching → config → polish) - Focus on dysgraphia accessibility for teaching interface - Start with message detection reliability (Phase 1, load-bearing) - Shared emoji dictionary (Phase 1-3); per-server overrides deferred to Phase 4+ Confidence Levels: - Tech Stack: VERY HIGH (all production-proven, no experimental choices) - Architecture: VERY HIGH (mirrors successful production bots) - Features: HIGH (tight scope, transparent approach) - Roadmap: HIGH (logical phase progression with value delivery) Gaps to Address in Requirements: - Vivi's teaching UX preferences (dysgraphia-specific patterns) - Exact emoji coverage and naming conventions - Moderation/teaching permissions model - Multi-system scope and per-system customization needs Ready for requirements definition and roadmap creation. Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>