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n8n-agents

Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.

71

Quality

86%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A high-signal, highly actionable design guide with excellent concrete node/connection/config detail and a strong validation-plus-checklist workflow. Its two weaknesses are length that could be trimmed into reference files, and a progressive-disclosure structure that promises nine reference files which are not actually bundled.

Suggestions

Ship the nine referenced reference files (TOOLS.md, SUBWORKFLOW_AS_TOOL.md, SYSTEM_PROMPT.md, STRUCTURED_OUTPUT.md, MEMORY.md, HUMAN_REVIEW.md, CHAT_AGENT_PATTERNS.md, RAG.md, EXAMPLES.md) under a references/ directory, and convert the bolded name references into real links (e.g. [TOOLS.md](references/TOOLS.md)) so progressive disclosure is functional.

Move explanatory scaffolding — the "Why split it" rationale, the intro framing paragraph, and the "What's NOT available via the community MCP" table — into the relevant reference files or a sibling skill to reduce SKILL.md length toward a lean overview.

Trim prose that restates adjacent tables (e.g. the anti-patterns restated in narrative form) so each guideline appears once.

DimensionReasoningScore

Conciseness

Mostly efficient and dense with genuine n8n-specifics (node names, connection types, defaults), but ~280 lines with trimmable framing ("the most common over-build"), a non-core "What's NOT available via the community MCP" table, prose restating table contents, and explanatory paragraphs like "Why split it" that could move to reference files.

3 / 5

Actionability

Highly concrete and executable throughout: real node types (.agent, .outputParserStructured, .toolWorkflow), real ai_* connection types, a copy-paste-ready JSON sub-node wiring snippet, exact $fromAI syntax, and concrete config values (maxIterations 15/50-200, contextWindowLength 5→50, autoFix: true, schemaType: 'manual').

5 / 5

Workflow Clarity

Pairs risky operations with explicit validation/feedback loops — validate with validate_workflow then verify with n8n_get_workflow (sub-nodes on ai_*, not main), the autoFix parse-failure retry loop, and a complete pre-shipping Quick reference checklist with checkboxes mapping to anchor 5's checklists-for-complex-processes.

5 / 5

Progressive Disclosure

Structure and the "Reference files" read-when table are well designed and one-level-deep, but the 9 referenced files (TOOLS.md, SYSTEM_PROMPT.md, MEMORY.md, etc.) do not exist — there is no references/ directory — and references are bare bolded names rather than resolvable file paths, so the promised split content is absent and navigation is not functional.

3 / 5

Total

16

/

20

Passed

Description

96%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, explicit description that clearly states both what it does and when to use it, with rich concrete capability coverage and comprehensive natural trigger terms. Its only weakness is trigger-term breadth that overlaps with adjacent n8n skills it depends on.

Suggestions

Tighten the "whenever the user mentions..." clause to scope generic terms ($fromAI, system prompts, RAG, vector store) to the agent-design context so they don't fire for sibling skills that own those primitives.

Consider naming the node types first in the trigger list (they are the most distinctive signals) before the broad keyword list, to anchor distinctiveness.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities — building/editing specific @n8n/n8n-nodes-langchain.* node types (AI Agent, LLM chain, Text Classifier, Information Extractor), the model/memory/tools/outputParser slots, structured output with autoFix, RAG, human review, and chat topologies — giving comprehensive coverage of concrete actions.

5 / 5

Completeness

Explicitly answers both what ("Design n8n AI agents the right way" plus the enumerated coverage areas) and when ("Use when building or editing any @n8n/n8n-nodes-langchain.* AI node... and whenever the user mentions...") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Comprehensive natural trigger terms including synonyms and identifiers — "AI agents", "LLM with tools", "tool calling", "$fromAI", "system prompts", "agent memory", "sessionId", "structured/JSON output", "output parser", "RAG", "vector store", "chat assistant/bot", "human-in-the-loop review".

5 / 5

Distinctiveness Conflict Risk

Names a clear niche (n8n LangChain agent nodes) with distinct node-type triggers, but broad generic terms like "$fromAI", "system prompts", "RAG", and "vector store" overlap with the sibling skills the body itself cross-references, creating minor conflict risk rather than minimal.

4 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
czlonkowski/n8n-mcp
Reviewed

Table of Contents

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