SKILLEMALL.ai
GitHub ↗ RU Sign in

AC lnd

Install and run Lightning Terminal (litd) which bundles lnd, loop, pool, tapd, and faraday in a single Docker container. Defaults to neutrino backend with SQLite storage on testnet. Supports watch-only mode with remote signer, standalone mode, and regtest development. Use when setting up a Lightning node for payments, channel management, liquidity management (loop), channel marketplace (pool), taproot assets (tapd), or enabling agent L402 commerce.

modbender/skill-library-mcp Agent Skills author: modbender MIT 19 files · 9 scripts body ≈ 3 009 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Install and run Lightning Terminal (litd) which bundles lnd, loop, pool, tapd, and faraday in a single Docker container.

As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

IntegrationDockerInfrastructureSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 19. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 50/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 2 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 85Steps. 19 steps, 1 vague phrases
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3009 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 19 top-level sections: this looks like several domains in one skill

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -31 of 9 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 452: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 19 items
    • +4Has examples (28 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.