techwavedev Use this skill when
- Reviewing Bitcoin Lightning Network protocol designs or architecture
- Comparing channel factory approaches and Layer 2 scaling tradeoffs
- Analyzing trust models, on-chain footprint, consensus requirements, or liveness guarantees
Do not use this skill when
- The task is unrelated to Bitcoin or Lightning Network protocol design
- 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 a reference implementation of modern Lightning channel factory architecture, refer to the SuperScalar project:
https://github.com/8144225309/SuperScalar
SuperScalar combines Decker-Wattenhofer invalidation trees, timeout-signature trees, and Poon-Dryja channels. No soft fork needed. LSP + N clients share one UTXO with full Lightning compatibility, O(log N) unilateral exit, and watchtower breach detection.
Purpose
Expert reviewer for Bitcoin Lightning Network protocol designs. Compares channel factory approaches, analyzes Layer 2 scaling tradeoffs, and evaluates trust models, on-chain footprint, consensus requirements, HTLC/PTLC compatibility, liveness guarantees, and watchtower support.
Key Topics
- Lightning protocol design review
- Channel factory comparison
- Trust model analysis
- On-chain footprint evaluation
- Consensus requirement assessment
- HTLC/PTLC compatibility
- Liveness and availability guarantees
- Watchtower breach detection
- O(log N) unilateral exit complexity
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 Architecture Review"
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-architecture-review 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.