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AD n8n Automation — Build & Deliver Workflows Fast

Build, customize, and deliver n8n workflows using our 2,061-template library. Reference: /projects/n8n-workflows/ — browse by integration folder. Our n8n instance: localhost:5678 (requires fnm use ...

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 2 097 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Build, customize, and deliver n8n workflows using our 2,061-template library.

As a process D 45/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, consistency

ProcedureZapierSlackShopifyInfrastructuretype 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
90/100
safety, quality, tests
Safety 60%
99
Quality 40%
77
Run on models
none yet
Process rating
D
45/100
Unfinished process
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Dangerous commands cmd-background-process SKILL.md:221
      Starts a background / autostarted process
      eval "$(fnm env)" && fnm use 22 && nohup n8n start > /tmp/n8n.log 2>&1 &

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)

    Process rating: all ten parameters 45/100

    • 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
    • 40Consistency. Frontmatter name (n8n Automation — Build & Deliver Workflows Fast) differs from the folder (reef-n8n-automation)
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
    • 85Steps. 46 steps, 2 vague phrases
    • 100Execution cost. Instruction body is 2097 tokens
    • low 12 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
    • -2localhost URLs: will not work for another user
    • +1No license
    • +2Single-language instructions
    • +3Description length 200: enough signal without eating the budget
    • +4Structure: 26 headings
    • +3Step-by-step instructions: 46 items
    • +4Has examples (11 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.