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AC n8n-code-automation

Integrate n8n workflow automation into coding tasks. Use when building automated workflows, integrating n8n into development pipelines, executing existing workflows, modifying workflow configurations, or creating new automation solutions. ⚠️ **SECURITY UPDATE v1.1.0** - This version addresses critical security vulnerabilities from v1.0.0. See SECURITY section below.

ClawHub Agent Skills author: nelmaz v1.1.0 MIT-0 5 files body ≈ 3 943 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 63/100 · Has gaps — weak spots: result and completion, consistency, running it twice

ProcedureZapierSoftware developmentInfrastructuretype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
81
Run on models
none yet
Process rating
C
63/100
Has gaps
Running it twice w 4
30
Result and completion w 14
40
Consistency w 8
40
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: 3. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "changelog"

    Process rating: all ten parameters 63/100

    • 30Running it twice. 18 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 40Consistency. Frontmatter name (n8n-code-automation) differs from the folder (n8n-code-automation-nelmaz)
    • 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
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 74 steps
    • 100Execution cost. Instruction body is 3943 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • 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
    • -239 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 368: enough signal without eating the budget
    • +4Structure: 54 headings
    • +3Step-by-step instructions: 74 items
    • +4Has examples (33 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.

    External checks

    ClawHub: suspicious
    This skill supports legitimate n8n automation, but it bundles conflicting old and new instructions and can guide agents to execute, modify, or delete remote workflows with an API key.
    LLM: suspicious (high) · 28 May 2026