Agent skill · data analytics · n8n-io
config-evals
Builds and maintains configuration-based evaluations on a workflow with the eval-config tool. Use when the user asks to set up, add, view, change, or remove an evaluation, score, grade, or judge a workflow's output, or measure answer quality against a test dataset. This is the only eval form Instance AI handles — it does not touch on-canvas evaluation nodes.
Why this skill is useful
Adds specific commands and procedures for configuring evaluations using the eval-config tool that the AI wouldn't reliably generate on its own.
What it needs
Requires data-tables, eval-config installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 199,638 stars on the source repository.
What this skill does
Config-based Evaluations Use this skill to attach a configuration-based evaluation to a workflow with the eval-config tool. A config eval pairs a workflow with a name, a start node, an end node, one or more judged metrics, and a Data Table dataset. Nothing is added to the canvas — the config lives off-canvas via the evaluation-config API. Config evals are the only evaluation form you work with. Do not add, read, rewire, or reason about on-canvas evaluation nodes (EvaluationTrigger, Evaluation/checkIfEvaluating/setOutputs/setMetrics). If the user asks for those, build a config eval instead and briefly say that is how you set up evaluations. What a Config Eval Needs name — a human-readable evaluation name. startNodeName — the node where a run begins; it is fed one test-input row. Must be a node with an incoming connection — never a trigger (see step 2). endNodeName — the node whose output is judged. dataTableId — a Data Table holding the test dataset. Create and populate it with the data-tables tool first, then link it here by id. metrics — one or more judged metrics (see below). Default Procedure 1. Identify the target workflow and read it. Trace the main path from trigger to the node that produces the answer. 2. Pick the nodes: startNodeName is the first node after the trigger — the node that receives the input the dataset varies. Never use the trigger itself: an eval run swaps the trigger for a dataset-driven one, so the start node must have an incoming connection or the run fails to compile. For a chat/agent workflow this is usually the agent node (often the same as endNodeName). endNodeName is the node whose output you want scored (usually the AI agent or the final response node). 3. Resolve the dataset. Call data-tables(action="list") to find an existing dataset, or create and seed one with data-tables before creating the config. Never invent a dataTableId; use one returned by data-tables. 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills n8n-io/config-evals