--- name: lightning-factory-explainer-711b27e93d53 description: Explain Bitcoin Lightning channel factories and the SuperScalar protocol — scalable Lightning onboarding using shared UTXOs, Decker-Wattenhofer trees, timeout-signature trees, MuSig2, and Taproot. No soft fork required. risk: safe source: community date_added: '2026-03-03' --- ## 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 ## Limitations - Use this skill only when the task clearly matches the scope described above. - Do not treat the output as a substitute for environment-specific validation, testing, or expert review. - Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing. --- Source attribution: Sukumar-Elley Original contribution (preserved): https://github.com/Sukumar-Elley/antigravity-awesome-skills/blob/5925731a273265928e78954df711358072c8de5b/plugins/antigravity-awesome-skills-claude/skills/lightning-factory-explainer/SKILL.md License: MIT + CC-BY-4.0 License information: https://creativecommons.org/licenses/by/4.0/ Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Antigravity User Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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Canonical license URL: https://creativecommons.org/licenses/by/4.0/ Review the full license text at the canonical URL before reuse. # Mega AI Hub resource metadata Creator / publisher: Sukumar-Elley Original contribution (preserved): https://github.com/Sukumar-Elley/antigravity-awesome-skills/blob/5925731a273265928e78954df711358072c8de5b/plugins/antigravity-awesome-skills-claude/skills/lightning-factory-explainer/SKILL.md Attribution basis: Explicit original-content license; package is not listed among upstream exceptions Fields of work: Data & Analytics, Development & Engineering, Education & Research Roles: Data Scientist, ML / AI Engineer, Researcher Specialties: Specialty not specified Tools: Github Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: cce6712a1eaa14c5b75651a0704eb1abbf5785e428e8276d68567fb31bec35af