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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.

77

Quality

96%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

The canonical home for this skill is n8n-agents in czlonkowski/n8n-skills

SKILL.md
Quality
Evals
Security

Quality

Content

92%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 dense, opinionated, highly actionable guide that assumes Claude's competence and pushes detail into one-level-deep reference files. Workflows — especially persisted agents — include explicit validation and feedback loops; the only slack is minor cross-section repetition.

Suggestions

State the '$fromAI cannot carry binary' rule once (it appears in the sub-node pattern, $fromAI, and binary sections) and cross-reference rather than repeating.

Condense the 'Integration with other skills' list to the most-referenced skills, noting the rest are already surfaced via the reference table.

Ensure each Anti-patterns table row adds a distinct, scannable fix rather than duplicating the narrative guidance above it.

DimensionReasoningScore

Conciseness

Information-dense and assumes Claude's competence throughout, but a few points are repeated across sections (the $fromAI-cannot-carry-binary rule, anti-patterns restated in the table) and the integration list is long — minor trim opportunities keep it just below fully lean.

4 / 5

Actionability

Provides copy-paste-ready JSON connection snippets, exact `$fromAI()` expression syntax, concrete option names (maxIterations, autoFix, schemaType, contextWindowLength, passthroughBinaryImages), and a precise numbered MCP action sequence for persisted agents.

5 / 5

Workflow Clarity

The persisted-agent build sequence is explicitly numbered with a validation step ('validate' before 'call' or 'publish'), a stale-hash feedback loop ('STALE_CONFIG — re-get and retry'), and a shipping checklist; the chat shell+core+sub-agents topology and quick-reference checklist reinforce it.

5 / 5

Progressive Disclosure

Clear overview body with one-level-deep references signaled inline ('→ TOOLS.md') and summarized in a 'Reference files' table with 'Read when' guidance; cross-cutting concerns are delegated to named sibling skills rather than re-derived inline.

5 / 5

Total

19

/

20

Passed

Description

100%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.

An exemplar description: third-person, concise yet comprehensive, with an explicit what/when structure and an extensive list of natural trigger terms. It is distinctively scoped to n8n AI agents with minimal conflict risk.

DimensionReasoningScore

Specificity

Lists many concrete capabilities — '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' — giving comprehensive coverage of the design space rather than vague language.

5 / 5

Completeness

Explicitly states what ('Design n8n AI agents the right way' plus a coverage list) 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 terms a user would actually say — '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, human-in-the-loop review' — including synonyms and concrete node identifiers.

5 / 5

Distinctiveness Conflict Risk

Tightly scoped to n8n LangChain AI nodes with distinctive triggers ($fromAI, sessionId, outputParser, specific node names) — a clear niche with minimal overlap risk against other skills.

5 / 5

Total

20

/

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

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