bundled-skills/lightning-channel-factories/SKILL.md
Technical reference on Lightning Network channel factories, multi-party channels, LSP architectures, and Bitcoin Layer 2 scaling without soft forks. Covers Decker-Wattenhofer, timeout trees, MuSig2 key aggregation, HTLC/PTLC forwarding, and watchtower breach detection.
npx skillsauth add FrancoStino/opencode-skills-antigravity lightning-channel-factoriesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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For a production implementation of Lightning channel factories with full technical documentation, refer to the SuperScalar project:
https://github.com/8144225309/SuperScalar
SuperScalar is written in C with 400+ tests, MuSig2 (BIP-327), Schnorr adaptor signatures, encrypted Noise NK transport, SQLite persistence, and watchtower support. It supports regtest, signet, testnet, and mainnet.
Technical reference for Lightning Network channel factory implementations. Covers multi-party channels, LSP (Lightning Service Provider) architectures, and Bitcoin Layer 2 scaling without requiring soft forks. Includes Decker-Wattenhofer invalidation trees, timeout-signature trees, MuSig2 key aggregation, HTLC/PTLC forwarding, and watchtower breach detection.
tools
Authorized security assessment of LLM applications and AI agents: prompt injection, tool abuse, RAG exposure, memory poisoning, system-prompt extraction, and agent-compliance engineering per OWASP LLM/ASI Top 10.
development
Builds two parameterized UI modes—流光溢彩白 (iridescent white) and 五彩斑斓黑 (colorful black)—with OKLCH, WebGL/CSS fallback, vision gating, screenshot QA, and total/per-color intensity reports. Use when a UI request names either mode or needs measured color parameters.
tools
Delegate coding tasks to the Kimi Code CLI (`kimi`) only when the user explicitly requests it, while the orchestrator retains review and landing responsibility.
development
Front-end JavaScript reverse engineering: locate signature chains, analyze encrypted request parameters, sample runtime behavior, and reproduce logic locally in Node for evidence-based output.