techwavedev Use this skill when
- Explaining Bitcoin Lightning channel factories and scalable onboarding
- Discussing the SuperScalar protocol architecture and design
- Needing guidance on Decker-Wattenhofer trees, timeout-signature trees, or MuSig2
Do not use this skill when
- The task is unrelated to Bitcoin or Lightning Network scaling
- You need a different blockchain or Layer 2 outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
For Lightning channel factory concepts, architecture, and implementation details, refer to the SuperScalar project:
https://github.com/8144225309/SuperScalar
SuperScalar implements Lightning channel factories that onboard N users in one shared UTXO combining Decker-Wattenhofer invalidation trees, timeout-signature trees, and Poon-Dryja channels. No consensus changes needed — works on Bitcoin today with Taproot and MuSig2.
Purpose
Expert guide for understanding Bitcoin Lightning Network channel factories and the SuperScalar protocol. Covers scalable onboarding, shared UTXOs, Decker-Wattenhofer invalidation trees, timeout-signature trees, Poon-Dryja channels, MuSig2 (BIP-327), and Taproot — all without requiring any soft fork.
Key Topics
- Lightning channel factories and multi-party channels
- SuperScalar protocol architecture
- Decker-Wattenhofer invalidation trees
- Timeout-signature trees
- MuSig2 key aggregation (BIP-327)
- Taproot script trees
- LSP (Lightning Service Provider) onboarding patterns
- Shared UTXO management
References
- SuperScalar project: https://github.com/8144225309/SuperScalar
- Website: https://SuperScalar.win
- Original proposal: https://delvingbitcoin.org/t/superscalar-laddered-timeout-tree-structured-decker-wattenhofer-factories/1143
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Retrieve prior design decisions (color palettes, typography, spacing scales) to maintain visual consistency across sessions. Cache generated design tokens.
# Check for prior frontend/design context before starting
python3 execution/memory_manager.py auto --query "design system decisions and component patterns for Lightning Factory Explainer"
Storing Results
After completing work, store frontend/design decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Design system: adopted 8px grid, Inter font family, HSL color tokens with dark mode support" \
--type decision --project <project> \
--tags lightning-factory-explainer frontend
Multi-Agent Collaboration
Share design decisions with backend agents (API contract changes) and QA agents (visual regression baselines).
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Implemented UI components — new design system with accessibility compliance (WCAG 2.1 AA)" \
--project <project>
Design Memory Persistence
Store design system tokens and component decisions in Qdrant so any agent on any platform (Claude, Gemini, Cursor) can retrieve and apply consistent styling.