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Lightning architecture review

Skill newmindsgroup/ai-agent-skills-library/dist/skills/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.From its SKILL.md

Install
npx -y skills add newmindsgroup/ai-agent-skills-library --skill lightning-architecture-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.6 KB, 446 tokens by cl100k_base, as published. Nobody here has run it

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

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.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most review quality skills give in 446 tokens

Counted across 1,048 of the 1,783 authors here whose files we hold, read 2026-08-07

  • Ask questions one at a timein 81 of 1048, across 64 files
  • Provide a recommended answer for each questionin 73 of 1048, across 50 files
  • Explore the codebase instead of asking answerable questionsin 66 of 1048, across 42 files
  • Resolve dependencies between decisions one-by-onein 42 of 1048, across 17 files
  • Interview the user relentlessly about the planin 38 of 1048, across 13 files
  • Order findings by severityin 31 of 1048
  • Resolve each branch of the decision treein 27 of 1048, across 5 files
  • Run a grilling sessionin 26 of 1048, across 5 files
  • Update CONTEXT.md immediately when a term is resolvedin 26 of 1048, across 11 files
  • Propose precise canonical terms for vague languagein 25 of 1048, across 7 files
  • Create documentation files lazilyin 24 of 1048, across 5 files
  • Assign severity to every findingin 24 of 1048

Said here and by no other author read

  • validate outcomes

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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Skills are one crate of 326,736. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.