# Lightning Architecture Review

> Review Bitcoin Lightning Network protocol designs, compare channel factory approaches, and analyze Layer 2 scaling tradeoffs. Covers trust models, on-chain footprint, consensus requirements, HTLC/PTLC compatibility, liveness, and watchtower support.

- Skill: `techwavedev/lightning-architecture-review` (Agent Skill)
- Install (CLI): `npx skillmds@latest add techwavedev/lightning-architecture-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/lightning-architecture-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/lightning-architecture-review

---


## 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-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/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.

```bash
# 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:

```bash
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).

```bash
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.

<!-- AGI-INTEGRATION-END -->